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Diagnostics, Volume 12, Issue 2 (February 2022) – 331 articles

Cover Story (view full-size image): MALDI-ToF AI is a promising approach to provide more rapid and accurate antibiotic resistance and bring in more precise management of bacterial infections. However, the AI is not perfect yet, and the reason for uncertain or incorrect predictions has not been investigated. In the study, we examined the association between specimen types, MS spectral quality, phenotypic antibiotic susceptibility, bacterial strain composition, and the AI’s predictions. We identified that uncommon bacterial strain or highly complicated bacterial strain composition would be the causes leading to AI uncertainties. Knowing more about MALDI-ToF AI, especially its weakness, can increase our confidence in it and help us to use it better. View this paper.
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Article
Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm
Diagnostics 2022, 12(2), 557; https://doi.org/10.3390/diagnostics12020557 - 21 Feb 2022
Viewed by 616
Abstract
Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose [...] Read more.
Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women’s lives. The best feature optimization is performed to classify the results accurately. The CAD system’s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches. Full article
(This article belongs to the Special Issue AI as a Tool to Improve Hybrid Imaging in Cancer)
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Article
Awareness of Nuclear Medicine Physicians in Romania Regarding the Diagnostic of Cardiac Amyloidosis—A Survey-Based Study
Diagnostics 2022, 12(2), 556; https://doi.org/10.3390/diagnostics12020556 - 21 Feb 2022
Viewed by 558
Abstract
Amyloidosis is a heterogeneous group of diseases caused by the extracellular deposition of amyloid insoluble fibrils in multiple organs, resulting in various clinical manifestations. Cardiac amyloidosis (CA) occurs mainly in primary light-chain (AL) amyloidosis, hereditary transthyretin (ATTRv) amyloidosis and senile or wild-type transthyretin [...] Read more.
Amyloidosis is a heterogeneous group of diseases caused by the extracellular deposition of amyloid insoluble fibrils in multiple organs, resulting in various clinical manifestations. Cardiac amyloidosis (CA) occurs mainly in primary light-chain (AL) amyloidosis, hereditary transthyretin (ATTRv) amyloidosis and senile or wild-type transthyretin (ATTRwt) amyloidosis. Knowing that myocardial uptake at bone scintigraphy is an essential step in the ATTR-CA diagnostic algorithm, the level of awareness among nuclear medicine physicians (NMPs) using bone tracer scintigraphy is of great importance. The objective of the study was to evaluate NMPs’ awareness of scintigraphy with bisphosphonates for the detection of CA. We conducted an online survey among NMPs from Romania to assess their current awareness and state of knowledge of nuclear techniques used in CA. Among the total 65 Romanian NMPs, 35 (53%) responded to this questionnaire. Approximately three-quarters of participants (74%) found a diffuse accumulation of bisphosphonates in the heart on scintigraphy performed for bone pathology as an incidental discovery. Detection of myocardial uptake of 99mTc-labeled bisphosphonates on scintigraphy suggests CA-AL for 3% of participants and for 9% of respondents, the appearance is of uncertain cardiac amyloidosis, while 5% of participants observed cardiac uptake but did not report it as CA. Even if more than half of those who responded to this survey (54%) found abnormal cardiac uptake and interpreted it as CA-ATTR, only 14% contacted the referring physician to draw attention to the incidental discovery to refer the patient to a specialist in rare genetic cardiomyopathy. Regarding the knowledge about the categories of bisphosphonates recommended in the diagnosis of CA-ATTR, 54% answered inadequately that methylene diphosphonate (MDP) could be used. Romanian nuclear physicians are partially familiar with CA diagnosis by scintigraphy, but its diagnostic potential and standardization, recommended radiotracers and acquisition times and interpretation algorithms are known in varying proportions. Therefore, there is a need to enhance knowledge through continuing medical education programs in order to standardize the protocols for the acquisition, processing and interpretation of bisphosphonate scintigraphy for the detection of cardiac ATTR amyloidosis. Full article
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Article
Improving Visual-Patient-Avatar Design Prior to Its Clinical Release: A Mixed Qualitative and Quantitative Study
Diagnostics 2022, 12(2), 555; https://doi.org/10.3390/diagnostics12020555 - 21 Feb 2022
Viewed by 457
Abstract
Visual-Patient-avatar, an avatar-based visualisation of patient monitoring, is a newly developed technology aiming to promote situation awareness through user-centred design. Before the technology’s introduction into clinical practice, the initial design used to validate the concept had to undergo thorough examination and adjustments where [...] Read more.
Visual-Patient-avatar, an avatar-based visualisation of patient monitoring, is a newly developed technology aiming to promote situation awareness through user-centred design. Before the technology’s introduction into clinical practice, the initial design used to validate the concept had to undergo thorough examination and adjustments where necessary. This mixed qualitative and quantitative study, consisting of three different study parts, aimed to create a design with high user acceptance regarding perceived professionalism and potential for identification while maintaining its original functionality. The first qualitative part was based on structured interviews and explored anaesthesia personnel’s first impressions regarding the original design. Recurrent topics were identified using inductive coding, participants’ interpretations of the vital sign visualisations analysed and design modifications derived. The second study part consisted of a redesign process, in which the visualisations were adapted according to the results of the first part. In a third, quantitative study part, participants rated Likert scales about Visual-Patient-avatar’s appearance and interpreted displayed vital signs in a computer-based survey. The first, qualitative study part included 51 structured interviews. Twenty-eight of 51 (55%) participants mentioned the appearance of Visual-Patient-avatar. In 23 of 51 (45%) interviews, 26 statements about the general impression were identified with a balanced count of positive (14 of 26) and negative (12 of 26) comments. The analysis of vital sign visualisations showed deficits in several vital sign visualisations, especially central venous pressure. These findings were incorporated into part two, the redesign of Visual-Patient-avatar. In the subsequent quantitative analysis of study for part three, 20 of 30 (67%) new participants agreed that the avatar looks professional enough for medical use. Finally, the participants identified 73% (435 of 600 cases) of all vital sign visualisations intuitively correctly without prior instruction. This study succeeded in improving the original design with good user acceptance and a reasonable degree of intuitiveness of the new, revised design. Furthermore, the study identified aspects relevant for the release of Visual-Patient-avatar, such as the requirement for providing at least some training, despite the design’s intuitiveness. The results of this study will guide further research and improvement of the technology. The study provides a link between Visual-Patient-avatar as a scientific concept and as an actual product from a cognitive engineering point of view, and may serve as an example of methods to study the designs of technologies in similar contexts. Full article
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Article
Clinical Implication of Preoperative C-Reactive Protein/Albumin Ratio in Malignant Transformation of Intraductal Papillary Mucinous Neoplasm: A Propensity Score Analysis
Diagnostics 2022, 12(2), 554; https://doi.org/10.3390/diagnostics12020554 - 21 Feb 2022
Viewed by 436
Abstract
Background: Inflammation-based scoring has been reported to be useful for predicting the recurrence and prognosis of various carcinomas. This study retrospectively investigated the relationship between inflammation-based score and intraductal papillary mucinous neoplasms (IPMNs). Methods: Between January 2013 and October 2018, we enrolled 417 [...] Read more.
Background: Inflammation-based scoring has been reported to be useful for predicting the recurrence and prognosis of various carcinomas. This study retrospectively investigated the relationship between inflammation-based score and intraductal papillary mucinous neoplasms (IPMNs). Methods: Between January 2013 and October 2018, we enrolled 417 consecutive patients with pancreatic tumors who received surgical resections at our hospital. The main outcome was the association between the preoperative inflammation-based score and their accuracy in predicting malignant transformation of IPMN. Results: Seventy six patients were eligible. Pathological findings indicated that 35 patients had low-grade dysplasia, 18 had high-grade dysplasia, and 23 had invasive carcinomas. As the C-reactive protein albumin ratio (CAR) was higher, malignant transformation of IPMNs also increased (p = 0.007). In comparing CARhigh and CARlow using cutoff value, the results using a propensity score analysis showed that the CARhigh group predicted malignant transformation of IPMNs (odds ratio, 4.18; 95% confidence interval, 1.37–12.8; p = 0.01). In the CARhigh group, disease-free survival (DFS) was significantly shorter (p = 0.04). In the worrisome features, the AUC for the accuracy of malignant transformation with CARhigh was 0.84 when combining with the MPD findings. Conclusions: Preoperative CAR could be a predictive marker of malignant transformation of IPMNs. Full article
(This article belongs to the Special Issue Diagnosis of Pancreatic Cancer)
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Article
Small Renal Masses without Gross Fat: What Is the Role of Contrast-Enhanced MDCT?
Diagnostics 2022, 12(2), 553; https://doi.org/10.3390/diagnostics12020553 - 21 Feb 2022
Viewed by 340
Abstract
Increased detection of small renal masses (SRMs) has encouraged research for non-invasive diagnostic tools capable of adequately differentiating malignant vs. benign SRMs and the type of the tumour. Multi-detector computed tomography (MDCT) has been suggested as an alternative to intervention, therefore, it is [...] Read more.
Increased detection of small renal masses (SRMs) has encouraged research for non-invasive diagnostic tools capable of adequately differentiating malignant vs. benign SRMs and the type of the tumour. Multi-detector computed tomography (MDCT) has been suggested as an alternative to intervention, therefore, it is important to determine both the capabilities and limitations of MDCT for SRM evaluation. In our study, two abdominal radiologists retrospectively blindly assessed MDCT scan images of 98 patients with incidentally detected lipid-poor SRMs that did not present as definitely aggressive lesions on CT. Radiological conclusions were compared to histopathological findings of materials obtained during surgery that were assumed as the gold standard. The probability (odds ratio (OR)) in regression analyses, sensitivity (SE), and specificity (SP) of predetermined SRM characteristics were calculated. Correct differentiation between malignant vs. benign SRMs was detected in 70.4% of cases, with more accurate identification of malignant (73%) in comparison to benign (65.7%) lesions. The radiological conclusions of SRM type matched histopathological findings in 56.1%. Central scarring (OR 10.6, p = 0.001), diameter of lesion (OR 2.4, p = 0.003), and homogeneous accumulation of contrast medium (OR 3.4, p = 0.03) significantly influenced the accuracy of malignant diagnosis. SE and SP of these parameters varied from 20.6% to 91.3% and 22.9% to 74.3%, respectively. In conclusion, MDCT is able to correctly differentiate malignant versus uncharacteristic benign SRMs in more than 2/3 of cases. However, frequency of the correct histopathological SRM type MDCT identification remains low. Full article
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Review
The Juxtaoral Organ: From Anatomy to Clinical Relevance
Diagnostics 2022, 12(2), 552; https://doi.org/10.3390/diagnostics12020552 - 21 Feb 2022
Viewed by 390
Abstract
The juxtaoral organ was first described 1885 as a rudimentary structure that developed and disappeared in the embryonic period. Since then, it has been studied further and is now known to be a permanent anatomical structure of considerable importance in clinical, surgical and [...] Read more.
The juxtaoral organ was first described 1885 as a rudimentary structure that developed and disappeared in the embryonic period. Since then, it has been studied further and is now known to be a permanent anatomical structure of considerable importance in clinical, surgical and pathological fields. However, there are no precise and uniform descriptions about its anatomical localization and functional significance. Precise and in-depth anatomical knowledge is crucial to reducing the risk of incorrect identification of the juxtaoral organ, due to fact that this anatomical structure can be misinterpreted as a carcinoma, leading to unnecessary treatments. Therefore, the purpose of this review is to summarize the actual knowledge on the gross and microscopic anatomy of the juxtaoral organ and outline its clinical relevance in order to prevent unnecessary investigations/treatments of this anatomical pitfall. We believe that further studies are still needed to add new perspectives in relation to the juxtaoral organ. Full article
(This article belongs to the Special Issue Advances in Anatomy)
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Article
Predictive Factors for Successful Treatment of Deep Incisional Surgical Site Infections following Instrumented Spinal Surgeries: Retrospective Review of 1832 Cases
Diagnostics 2022, 12(2), 551; https://doi.org/10.3390/diagnostics12020551 - 21 Feb 2022
Viewed by 508
Abstract
Background: Surgical site infection (SSI) is a major complication in spinal instrumentation that is often difficult to treat. The purpose of this study was to identify and determine prognostic indicators for successful treatment of spine instrumentation SSI. Methods: Retrospectively, spine surgery cases were [...] Read more.
Background: Surgical site infection (SSI) is a major complication in spinal instrumentation that is often difficult to treat. The purpose of this study was to identify and determine prognostic indicators for successful treatment of spine instrumentation SSI. Methods: Retrospectively, spine surgery cases were examined on SSI diagnosis. Post-instrumentation SSI patients were categorized as “Successful” if SSI subsided after single debridement. Patients in whom SSI did not subsided and/or required removal of instrumentation were classified as “Challenging”. We investigated the relation of treatment outcomes to patients and treatment factors. Results: A total of 1832 spinal instrumentation cases were recognized with 44 (2.40%) SSI cases. White blood cell count, C-reactive protein (CRP) levels, causative bacteria (i.e., S. Aureus or MRSA), trauma injury, and early-stage antimicrobial agent sensitivity correlated with treatment prognosis. Multivariate analysis highlighted CRP levels and applying early-stage sensitive antibiotics as potential impactful predictive factors for successful treatment. Conclusions: Our results demonstrated that early selection of sensitive antimicrobial agents is critical and emphasizes the potential for early-stage classification methods such as Gram staining. Additionally, S. Aureus and MRSA SSI formed significantly more challenging infections to treat, thus requiring consideration when deciding on instrumentation retention. These factors offer promising aspects for further large-scale studies. Full article
(This article belongs to the Special Issue Paradigm Shift of Spinal Diagnosis and Treatment)
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Article
Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging
Diagnostics 2022, 12(2), 550; https://doi.org/10.3390/diagnostics12020550 - 21 Feb 2022
Viewed by 431
Abstract
Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and [...] Read more.
Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning. Full article
(This article belongs to the Special Issue Advances and Novelties in Hepatobiliary and Pancreatic Imaging)
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Review
A Review of the Role of Imaging Modalities in the Evaluation of Viral Myocarditis with a Special Focus on COVID-19-Related Myocarditis
Diagnostics 2022, 12(2), 549; https://doi.org/10.3390/diagnostics12020549 - 21 Feb 2022
Cited by 2 | Viewed by 633
Abstract
Viral myocarditis is inflammation of the myocardium secondary to viral infection. The clinical presentation of viral myocarditis is very heterogeneous and can range from nonspecific symptoms of malaise and fatigue in subclinical disease to a more florid presentation, such as acute cardiogenic shock [...] Read more.
Viral myocarditis is inflammation of the myocardium secondary to viral infection. The clinical presentation of viral myocarditis is very heterogeneous and can range from nonspecific symptoms of malaise and fatigue in subclinical disease to a more florid presentation, such as acute cardiogenic shock and sudden cardiac death in severe cases. The accurate and prompt diagnosis of viral myocarditis is very challenging. Endomyocardial biopsy is considered to be the gold standard test to confirm viral myocarditis; however, it is an invasive procedure, and the sensitivity is low when myocardial involvement is focal. Cardiac imaging hence plays an essential role in the noninvasive evaluation of viral myocarditis. The current coronavirus disease 2019 (COVID-19) pandemic has generated considerable interest in the use of imaging in the early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related myocarditis. This article reviews the role of various cardiac imaging modalities used in the diagnosis and assessment of viral myocarditis, including COVID-19-related myocarditis. Full article
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Article
Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning
Diagnostics 2022, 12(2), 548; https://doi.org/10.3390/diagnostics12020548 - 21 Feb 2022
Viewed by 496
Abstract
Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances [...] Read more.
Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the four-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, 0.971, and 0.956 in the non-neoplasm, CIN3, CIN1, and CIN2 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the three-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%), and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images. Full article
(This article belongs to the Special Issue Artificial Intelligence Approaches for Medical Diagnostics in Korea)
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Article
Patient-Assessed Outcomes following Temporal Bone Fractures
Diagnostics 2022, 12(2), 547; https://doi.org/10.3390/diagnostics12020547 - 21 Feb 2022
Viewed by 366
Abstract
The long-term impact of neurotological symptoms after a temporal bone fracture (TBF), including facial nerve palsy (FP), hearing loss, tinnitus, and dizziness on the quality of life of patients is often underevaluated. Thus, we retrospectively assessed 30 patients with TBF (26 men and [...] Read more.
The long-term impact of neurotological symptoms after a temporal bone fracture (TBF), including facial nerve palsy (FP), hearing loss, tinnitus, and dizziness on the quality of life of patients is often underevaluated. Thus, we retrospectively assessed 30 patients with TBF (26 men and 4 women) in our university tertiary referral center. They participated from injury onset to the final follow-up, over an 18-month period. Quality of life was estimated using validated questionnaires, such as the Facial Disability Index (FDI: physical and social), Hearing Handicap Inventory (HHI), Tinnitus Handicap Inventory (THI), and Dizziness Handicap Inventory (DHI). The FDI score was significantly worse in patients with severe initial (for physical FDI) and final facial palsy (for both physical and social FDI), mainly with immediate onset. The HHI score was statistically worse in patients with mixed hearing loss compared to those with conductive or sensorineural hearing loss and in those with profound hearing loss vs. normal hearing. The mixed TBF and the severity of hearing loss (especially profound hearing loss) were correlated with HHI, THI and DHI score values. In the long-term period after a TBF, moderate or severe facial palsy, mainly with immediate onset, may cause psychological distress, more easily resulting in social disability than functional impairment. Mixed TBF and mixed or profound hearing loss may also negatively influence quality of life. Full article
(This article belongs to the Special Issue Advances in Diagnostics of Otology and Neurotology)
Article
Interexaminer Reliability and Validity of Quantity of Cervical Mobility during Online Dynamic Inspection
Diagnostics 2022, 12(2), 546; https://doi.org/10.3390/diagnostics12020546 - 21 Feb 2022
Viewed by 636
Abstract
Background: Physical therapists routinely measure range of motion (ROM) of cervical spine. The reliability of the cervical range of motion (CROM) device has been demonstrated in several studies, but current evidence on the validity and reliability of the visual inspection is contradictory. The [...] Read more.
Background: Physical therapists routinely measure range of motion (ROM) of cervical spine. The reliability of the cervical range of motion (CROM) device has been demonstrated in several studies, but current evidence on the validity and reliability of the visual inspection is contradictory. The aim is to assess the validity and interexaminer reliability of the online visual inspection of active cervical ROM in physiotherapy students. Methods: Flexion, extension, both lateral flexions and rotations of a single participant were measured using CROM. Online visual inspection of 18 physiotherapy students against CROM was registered. Results: The validity, against CROM, of the online visual inspection of the active ROM ranged from good to excellent (Intraclass Correlation Coefficient (ICC) 0.83–0.97). Interexaminer reliability of the online visual inspection had favorable outcomes in all cervical movements in the three physiotherapy courses (ICC 0.70–0.96), with the visual inspection of the rotations being the most reliable (ICC 0.93–0.97). Interexaminer reliability of the classification of mobility was poor to good (Kappa 0.03–0.90). Conclusions: The interexaminer reliability and validity of the quantification of active cervical movement during online visual inspection was shown to be good to excellent for flexion-extension and lateral flexions and excellent for rotations. Full article
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Article
Left Anterior Sectorectomy: An Alternative to Left Hepatectomy for Tumors Invading the Distal Part of the Left Portal Vein
Diagnostics 2022, 12(2), 545; https://doi.org/10.3390/diagnostics12020545 - 21 Feb 2022
Viewed by 465
Abstract
Background: Liver tumors invading the distal part of the umbilical portion of the left portal vein usually require left hepatectomy. The recent introduction of the concept of left anterior sector, an independent anatomo-functional unit including the anterior portion of the left liver and [...] Read more.
Background: Liver tumors invading the distal part of the umbilical portion of the left portal vein usually require left hepatectomy. The recent introduction of the concept of left anterior sector, an independent anatomo-functional unit including the anterior portion of the left liver and supplied by the distal part of the umbilical portion of the left portal vein, could represent the rational for an alternative surgical approach. The aim of this study was to introduce the novel surgical procedure of ultrasound-guided left anterior sectorectomy. Methods: Among 92 consecutive patients who underwent hepatectomy, 3 patients with tumor invading the distal part of the umbilical portion of the left portal (two with colorectal liver metastases and one with neuroendocrine tumor liver metastases) underwent left anterior sectorectomy alone or in association with liver multiple metastasectomies. Results: Mean operation time was 393 min; post-operative morbidity and mortality were not observed. After a mean FU of 23 months (range 19–28), no local recurrence occurred. Conclusions: In presence of tumors invading the distal part of the umbilical portion of the left portal, left anterior sectorectomy could be considered as an anatomic radical surgical option that is safe but more conservative than a left hepatectomy. Full article
(This article belongs to the Special Issue The Role of Imaging in Liver Surgery)
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Article
Factors Associated with Delirium in COVID-19 Patients and Their Outcome: A Single-Center Cohort Study
Diagnostics 2022, 12(2), 544; https://doi.org/10.3390/diagnostics12020544 - 20 Feb 2022
Viewed by 636
Abstract
Background: A significant proportion of patients with coronavirus disease 2019 (COVID-19) suffer from delirium during hospitalization. This single-center observational study investigates the occurrence of delirium, the associated risk factors and its impact on in-hospital mortality in an Italian cohort of COVID 19 inpatients. [...] Read more.
Background: A significant proportion of patients with coronavirus disease 2019 (COVID-19) suffer from delirium during hospitalization. This single-center observational study investigates the occurrence of delirium, the associated risk factors and its impact on in-hospital mortality in an Italian cohort of COVID 19 inpatients. Methods: Data were collected in the COVID units of a general medical hospital in the South of Italy. Socio-demographic, clinical and pharmacological features were collected. Diagnosis of delirium was based on a two-step approach according to 4AT criteria and DSM5 criteria. Outcomes were: dates of hospital discharge, Intensive Care Unit (ICU) admission, or death, whichever came first. Univariable and multivariable proportional hazards Cox regression models were estimated, and risks were reported as hazard ratios (HR) along with their 95% confidence intervals (95% CI). Results: A total of 47/214 patients (22%) were diagnosed with delirium (21 hypoactive, 15 hyperactive, and 11 mixed). In the multivariable model, four independent variables were independently associated with the presence of delirium: dementia, followed by age at admission, C-reactive protein (CRP), and Glasgow Coma Scale. In turn, delirium was the strongest independent predictor of death/admission to ICU (composite outcome), followed by Charlson Index (not including dementia), CRP, and neutrophil-to-lymphocyte ratio. The probability of reaching the composite outcome was higher for patients with the hypoactive subtype than for those with the hyperactive subtype. Conclusions: Delirium was the strongest predictor of poor outcome in COVID-19 patients, especially in the hypoactive subtype. Several clinical features and inflammatory markers were associated with the increased risk of its occurrence. The early recognition of these factors may help clinicians to select patients who would benefit from both non-pharmacological and pharmacological interventions in order to prevent delirium, and in turn, reduce the risk of admission to ICU or death. Full article
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Review
Strain Imaging for the Early Detection of Cardiac Remodeling and Dysfunction in Primary Aldosteronism
Diagnostics 2022, 12(2), 543; https://doi.org/10.3390/diagnostics12020543 - 20 Feb 2022
Viewed by 440
Abstract
Speckle tracking echocardiography is a novel technique to quantify cardiac function and deformation. It has been applied in a series of cardiovascular diseases for the evaluation of early cardiac impairment. We recently used this technique to investigate cardiac structure and function in patients [...] Read more.
Speckle tracking echocardiography is a novel technique to quantify cardiac function and deformation. It has been applied in a series of cardiovascular diseases for the evaluation of early cardiac impairment. We recently used this technique to investigate cardiac structure and function in patients with primary aldosteronism. Cardiac damage usually occurs earlier in patients with primary aldosteronism than those with primary hypertension, probably because aldosterone hypersecretion is more commonly observed in the former than the latter patients. In this article, we will review the imaging studies, especially with speckle tracking echocardiography, for the detection of early cardiac dysfunction in primary aldosteronism as a disease model. Full article
(This article belongs to the Special Issue Clinical Relevance and Usefulness of Strain Imaging)
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Case Report
Diagnostic Value of Preoperative Electrodiagnostic Analysis in a Patient with Facial Palsy and a Large Vestibular Schwannoma: Case Report
Diagnostics 2022, 12(2), 542; https://doi.org/10.3390/diagnostics12020542 - 20 Feb 2022
Viewed by 488
Abstract
Although radiologic methods confirm the diagnosis of patients with large vestibular schwannomas, these methods usually indicate only the size of the tumor and its possible nerve compression. Electrodiagnostic methods can reveal the functional state of the nerves, particularly the trigeminal and facial nerves, [...] Read more.
Although radiologic methods confirm the diagnosis of patients with large vestibular schwannomas, these methods usually indicate only the size of the tumor and its possible nerve compression. Electrodiagnostic methods can reveal the functional state of the nerves, particularly the trigeminal and facial nerves, as well as providing a basis for objectively evaluating nerve injury. Due to the lack of an established objective evaluation method, electrodiagnostic methods were utilized to assess injury to the cranial nerve in a patient with a large vestibular schwannoma. A 79-year-old woman presented with a one-month history of right facial palsy, vertigo, dizziness, right postauricular pain, and right-sided hearing disturbance. Physical examination suggested injuries to the facial and vestibulocochlear nerves. Magnetic resonance imaging identified a vestibular schwannoma and showed that the tumor mass was affecting the brainstem, including the fourth ventricle, resulting in mild obstructive hydrocephalus. Preoperative electrodiagnostic evaluation identified asymptomatic trigeminal neuropathy accompanying a vestibular schwannoma. The patient underwent surgery, consisting of a suboccipital craniotomy with additional gamma knife radiosurgery. Postoperatively, she demonstrated significant recovery from right facial palsy and partial improvement of her neurologic symptoms. Large vestibular schwannomas with facial paralysis may be accompanied by additional entrapment neuropathy. Routine preoperative electrophysiological evaluation is recommended to establish a definitive diagnosis and evaluate the function of the trigeminal nerve, facial nerve, and brainstem in patients with large and compressive vestibular schwannomas. Full article
(This article belongs to the Special Issue Evidence-Based Diagnosis and Management of Facial Nerve Disorders)
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Systematic Review
Evaluation of Diagnostic Performance of Automatic Breast Volume Scanner Compared to Handheld Ultrasound on Different Breast Lesions: A Systematic Review
Diagnostics 2022, 12(2), 541; https://doi.org/10.3390/diagnostics12020541 - 19 Feb 2022
Viewed by 472
Abstract
Objective: To compare the diagnostic performance of the automatic breast volume scanner (ABVS) against the handheld ultrasound (HHUS) in the differential diagnosis of benign and malignant breast lesions. Methods: A systematic search and review of studies involving ABVS and HHUS for breast cancer [...] Read more.
Objective: To compare the diagnostic performance of the automatic breast volume scanner (ABVS) against the handheld ultrasound (HHUS) in the differential diagnosis of benign and malignant breast lesions. Methods: A systematic search and review of studies involving ABVS and HHUS for breast cancer screening were performed. The search involved the data taken from Scopus, PubMed, and science direct databases and was conducted between the year 2011 to 2020. The prospective method was used in determining the inclusion and exclusion criteria while the evidence level was determined using the BI-RADS categories for diagnostic studies. In addition, the parameters of specificity, mean age, sensitivity, tumor number, and diagnostic accuracy of the ABVS and HHUS were summarized. Results: No systematic review or randomized controlled trial were identified in the systematic search while one cross-sectional study, eight retrospective studies, and 10 prospective studies were found. Sufficient follow-up of the subjects with benign and malignant findings were made only in 10 studies, in which only two had used ABVS and HHUS after performing mammographic screening and MRI. Analysis was made of 21 studies, which included 5448 lesions (4074 benign and 1374 malignant) taken from 6009 patients. The range of sensitivity was (0.72–1.0) for ABVS and (0.62–1.0) for HHUS; the specificity range was (0.52–0.98)% for ABVS and (0.49–0.99)% for HHUS. The accuracy range among the 11 studies was (80–99)% and (59–98)% for the HHUS and ABVS, respectively. The identified tumors had a mean size of 2.1 cm, and the detected cancers had a mean percentage of 94% (81–100)% in comparison to the non-cancer in all studies. Conclusions: The evidence available in the literature points to the fact that the diagnostic performance of both ABVS and HHUS are similar with reference to the differentiation of malignant and benign breast lesions. Full article
(This article belongs to the Topic Medical Image Analysis)
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Article
Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans
Diagnostics 2022, 12(2), 540; https://doi.org/10.3390/diagnostics12020540 - 19 Feb 2022
Cited by 1 | Viewed by 709
Abstract
It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel [...] Read more.
It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Early diagnosis can minimise the risk of proliferated diabetic retinopathy, which is the advanced level of this disease, and having higher risk of severe impairment. Therefore, it becomes important to classify DR stages. To this effect, this paper presents a weighted fusion deep learning network (WFDLN) to automatically extract features and classify DR stages from fundus scans. The proposed framework aims to treat the issue of low quality and identify retinopathy symptoms in fundus images. Two channels of fundus images, namely, the contrast-limited adaptive histogram equalization (CLAHE) fundus images and the contrast-enhanced canny edge detection (CECED) fundus images are processed by WFDLN. Fundus-related features of CLAHE images are extracted by fine-tuned Inception V3, whereas the features of CECED fundus images are extracted using fine-tuned VGG-16. Both channels’ outputs are merged in a weighted approach, and softmax classification is used to determine the final recognition result. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method tested on the Messidor dataset reports an accuracy level of 98.5%, sensitivity of 98.9%, and specificity of 98.0%, whereas on the Kaggle dataset, the proposed model reports an accuracy level of 98.0%, sensitivity of 98.7%, and specificity of 97.8%. Compared with other models, our proposed network achieves comparable performance. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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Case Report
A Rare Case of Hepatic Vanishing Bile Duct Syndrome Occurring after Combination Therapy with Nivolumab and Cabozantinib in a Patient with Renal Carcinoma
Diagnostics 2022, 12(2), 539; https://doi.org/10.3390/diagnostics12020539 - 19 Feb 2022
Viewed by 532
Abstract
Tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) significantly improve the outcomes of patients with advanced clear cell renal cell carcinoma (ccRCC); however, high-grade toxicities can occur, particularly during combination therapy. Herein, we report a patient with advanced metastatic ccRCC, who developed [...] Read more.
Tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) significantly improve the outcomes of patients with advanced clear cell renal cell carcinoma (ccRCC); however, high-grade toxicities can occur, particularly during combination therapy. Herein, we report a patient with advanced metastatic ccRCC, who developed grade 4 cholestasis during combined therapy with nivolumab and cabozantinib. After the exclusion of common disorders associated with cholestasis and a failure of corticosteroids (CS), a liver biopsy was performed that demonstrated severe ductopenia. Consequently, a diagnosis of vanishing bile duct syndrome related to TKI and ICI administration was made, resulting in CS discontinuation and ursodeoxycholic acid administration. After a 7-month follow-up, liver tests had returned to normal values. Immunological studies revealed that our patient had developed robust T-cells and macrophages infiltrates in his lung metastasis, as well as in skin and liver tissues at the onset of toxicities. At the same time, peripheral blood immunophenotyping revealed significant changes in T-cell subsets, suggesting their potential role in the pathophysiology of the disease. Full article
(This article belongs to the Special Issue Immune-Related Adverse Events Diagnosis and Immunotherapy in Cancer)
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Article
The Role of Early Revascularization and Biomarkers in the Management of Diabetic Foot Ulcers: A Single Center Experience
Diagnostics 2022, 12(2), 538; https://doi.org/10.3390/diagnostics12020538 - 19 Feb 2022
Viewed by 656
Abstract
Diabetic neuropathy and Peripheral Arterial Disease (PAD) are the main etiological factors in foot ulceration. Herein, we report our experience of diabetic foot ulceration (DFU) management, with an analysis of the relationship between the rate of lower extremity amputation, in persons with infected [...] Read more.
Diabetic neuropathy and Peripheral Arterial Disease (PAD) are the main etiological factors in foot ulceration. Herein, we report our experience of diabetic foot ulceration (DFU) management, with an analysis of the relationship between the rate of lower extremity amputation, in persons with infected DFU, after revascularization procedures performed to prevent major amputation. This study highlights the role of different biomarkers, showing their usefulness and potentiality in diabetic foot ulcer management, especially for the early diagnosis and therapy effectiveness monitoring. A retrospective analysis, from September 2016 to January 2021, of diabetic patients presenting diabetic foot with DFU, was performed. All patients were treated with at least one vascular procedure (endovascular, open, hybrid procedures) targeting PAD lesions. Outcomes measured were perioperative mortality and morbidity. Freedom from occlusion, primary and secondary patency, and amputation rate were registered. A total of 267 patients, with a mean age of 72.5 years, were included in the study. The major amputation rate was 6.2%, minor amputation rate was 17%. In our experience, extreme revascularization to obtain direct flow reduced the rate of amputations, with an increase in ulcer healing. Full article
(This article belongs to the Special Issue Biomarkers of Vascular Diseases 2.0)
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Review
Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review
Diagnostics 2022, 12(2), 537; https://doi.org/10.3390/diagnostics12020537 - 19 Feb 2022
Viewed by 701
Abstract
The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review [...] Read more.
The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice. Full article
(This article belongs to the Special Issue Management of Knee Problems Based on the Proper Diagnostic Procedures)
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Editorial
Innovations in Diagnostic Imaging in Oral and Maxillofacial Diseases
Diagnostics 2022, 12(2), 536; https://doi.org/10.3390/diagnostics12020536 - 19 Feb 2022
Viewed by 386
Abstract
In recent years, improvements in imaging techniques have profoundly facilitated the diagnosis of pathologies of the maxillofacial district [...] Full article
(This article belongs to the Special Issue Current Concepts and Prospects of Diagnostics in Oral Diseases)
Case Report
Testosterone Therapy and Diaphragm Performance in a Male Patient with COVID-19: A Case Report
Diagnostics 2022, 12(2), 535; https://doi.org/10.3390/diagnostics12020535 - 19 Feb 2022
Viewed by 571
Abstract
Low levels of testosterone may lead to reduced diaphragm excursion and inspiratory time during COVID-19 infection. We report the case of a 38-year-old man with a positive result on a reverse transcriptase-polymerase chain reaction test for SARS-CoV-2, admitted to the intensive care unit [...] Read more.
Low levels of testosterone may lead to reduced diaphragm excursion and inspiratory time during COVID-19 infection. We report the case of a 38-year-old man with a positive result on a reverse transcriptase-polymerase chain reaction test for SARS-CoV-2, admitted to the intensive care unit with acute respiratory failure. After several days on mechanical ventilation and use of rescue therapies, during the weaning phase, the patient presented dyspnea associated with low diaphragm performance (diaphragm thickness fraction, amplitude, and the excursion-time index during inspiration were 37%, 1.7 cm, and 2.6 cm/s, respectively) by ultrasonography and reduced testosterone levels (total testosterone, bioavailable testosterone and sex hormone binding globulin (SHBG) levels were 9.3 ng/dL, 5.8 ng/dL, and 10.5 nmol/L, respectively). Testosterone was administered three times 2 weeks apart (testosterone undecanoate 1000 mg/4 mL intramuscularly). Diaphragm performance improved significantly (diaphragm thickness fraction, amplitude, and the excursion-time index during inspiration were 70%, 2.4 cm, and 3.0 cm/s, respectively) 45 and 75 days after the first dose of testosterone. No adverse events were observed, although monitoring was required after testosterone administration. Testosterone replacement therapy led to good diaphragm performance in a male patient with COVID-19. This should be interpreted with caution due to the exploratory nature of the study. Full article
(This article belongs to the Special Issue Critical Care Imaging)
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Article
Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning
Diagnostics 2022, 12(2), 534; https://doi.org/10.3390/diagnostics12020534 - 19 Feb 2022
Viewed by 507
Abstract
An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed [...] Read more.
An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan. Full article
(This article belongs to the Topic Medical Image Analysis)
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Article
Understanding Healthcare Professionals’ Perspectives on Point-of-Care Testing
Diagnostics 2022, 12(2), 533; https://doi.org/10.3390/diagnostics12020533 - 19 Feb 2022
Viewed by 495
Abstract
Point-of-care testing (POCT) is an emerging technology that provides crucial assistance in delivering healthcare. The COVID-19 pandemic led to the accelerated importance of POCT technology due to its in-home accessibility. While POCT use and implementation has increased, little research has been published about [...] Read more.
Point-of-care testing (POCT) is an emerging technology that provides crucial assistance in delivering healthcare. The COVID-19 pandemic led to the accelerated importance of POCT technology due to its in-home accessibility. While POCT use and implementation has increased, little research has been published about how healthcare professionals perceive these technologies. The objective of our study was to examine the current perspectives of healthcare professionals towards POCT. We surveyed healthcare professionals to quantify perceptions of POCT usage, adoption, benefits, and concerns between October 2020 and November 2020. Questions regarding POCT perception were assessed on a 5-point Likert Scale. We received a total of 287 survey responses. Of the respondents, 53.7% were male, 66.6% were white, and 30.7% have been in practice for over 20 years. We found that the most supported benefit was POCTs ability to improve patient management (92%) and that the most supported concern was that POCTs lead to over-testing (30%). This study provides a better understanding of healthcare workers’ perspectives on POCT. To improve patient outcomes through the usage of POCT, greater research is needed to assess the needs and concerns of industry and healthcare stakeholders. Full article
(This article belongs to the Section Point-of-Care Diagnostics and Devices)
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Article
Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm
Diagnostics 2022, 12(2), 532; https://doi.org/10.3390/diagnostics12020532 - 18 Feb 2022
Viewed by 544
Abstract
Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or [...] Read more.
Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Eye Disease)
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Article
Radiation Dose and Fluoroscopy Time of Endovascular Coil Embolization in Patients with Carotid Cavernous Fistulas
Diagnostics 2022, 12(2), 531; https://doi.org/10.3390/diagnostics12020531 - 18 Feb 2022
Viewed by 382
Abstract
Carotid cavernous fistulas (CCFs) are abnormal connections between the cavernous sinus and the internal and/or external carotid artery. Endovascular therapy is the gold standard treatment. In the current retrospective single-center study we report detailed dosimetrics of all patients with CCFs treated by endovascular [...] Read more.
Carotid cavernous fistulas (CCFs) are abnormal connections between the cavernous sinus and the internal and/or external carotid artery. Endovascular therapy is the gold standard treatment. In the current retrospective single-center study we report detailed dosimetrics of all patients with CCFs treated by endovascular coil embolization between January 2012 and August 2021. Procedural and dosimetric data were compared between direct and indirect fistulas according to Barrow et al., and different DSA protocol groups. The local diagnostic reference level (DRL) was defined as the 3rd quartile of the dose distribution. In total, thirty patients met the study criteria. The local DRL was 376.2 Gy cm2. The procedural dose area product (DAP) (p = 0.03) and the number of implanted coils (p = 0.02) were significantly lower in direct fistulas. The median values for fluoroscopy time (FT) (p = 0.08) and number of DSA acquisitions (p = 0.84) were not significantly different between groups. There was a significantly positive correlation between DAP and FT (p = 0.003). The application of a dedicated low-dose protocol yielded a 32.6% DAP reduction. In conclusion, this study provides novel DRLs for endovascular CCF treatment using detachable coils. The data presented in this work might be used to establish new specific DRLs. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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Article
Lumbar Spine Computed Tomography to Magnetic Resonance Imaging Synthesis Using Generative Adversarial Network: Visual Turing Test
Diagnostics 2022, 12(2), 530; https://doi.org/10.3390/diagnostics12020530 - 18 Feb 2022
Viewed by 439
Abstract
(1) Introduction: Computed tomography (CT) and magnetic resonance imaging (MRI) play an important role in the diagnosis and evaluation of spinal diseases, especially degenerative spinal diseases. MRI is mainly used to diagnose most spinal diseases because it shows a higher resolution than CT [...] Read more.
(1) Introduction: Computed tomography (CT) and magnetic resonance imaging (MRI) play an important role in the diagnosis and evaluation of spinal diseases, especially degenerative spinal diseases. MRI is mainly used to diagnose most spinal diseases because it shows a higher resolution than CT to distinguish lesions of the spinal canals and intervertebral discs. When it is inevitable for CT to be selected instead of MR in evaluating spinal disease, evaluation of spinal disease may be limited. In these cases, it is very helpful to diagnose spinal disease with MR images synthesized with CT images. (2) Objective: To create synthetic lumbar magnetic resonance (MR) images from computed tomography (CT) scans using generative adversarial network (GAN) models and assess how closely the synthetic images resembled the true images using visual Turing tests (VTTs). (3) Material and Methods: Overall, 285 patients aged ≥ 40 years who underwent lumbar CT and MRI were enrolled. Based on axial CT and T2-weighted axial MR images from 285 patients, an image synthesis model using a GAN was trained using three algorithms (unsupervised, semi-supervised, and supervised methods). Furthermore, VTT to determine how similar the synthetic lumbar MR images generated from lumbar CT axial images were to the true lumbar MR axial images were conducted with 59 patients who were not included in the model training. For the VTT, we designed an evaluation form comprising 600 randomly distributed axial images (150 true and 450 synthetic images from unsupervised, semi-supervised, and supervised methods). Four readers judged the authenticity of each image and chose their first- and second-choice candidates for the true image. In addition, for the three models, structural similarities (SSIM) were evaluated and the peak signal to noise ratio (PSNR) was compared among the three methods. (4) Results: The mean accuracy for the selection of true images for all four readers for their first choice was 52.0% (312/600). The accuracies of determining the true image for each reader’s first and first + second choices, respectively, were as follows: reader 1, 51.3% and 78.0%; reader 2, 38.7% and 62.0%, reader 3, 69.3% and 84.0%, and reader 4, 48.7% and 70.7%. In the case of synthetic images chosen as first and second choices, supervised algorithm-derived images were the most often selected (supervised, 118/600 first and 164/600 second; semi-supervised, 90/600 and 144/600; and unsupervised, 80/600 and 114/600). For image quality, the supervised algorithm received the best score (PSNR: 15.987 ± 1.039, SSIM: 0.518 ± 0.042). (5) Conclusion: This was the pilot study to apply GAN to synthesize lumbar spine MR images from CT images and compare training algorithms of the GAN. Based on VTT, the axial MR images synthesized from lumbar CT using GAN were fairly realistic and the supervised training algorithm was found to provide the closest image to true images. Full article
(This article belongs to the Special Issue Skeletal Radiology)
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Article
Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities
Diagnostics 2022, 12(2), 529; https://doi.org/10.3390/diagnostics12020529 - 18 Feb 2022
Cited by 2 | Viewed by 561
Abstract
Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a [...] Read more.
Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists’ validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care. Full article
(This article belongs to the Special Issue Digital Pathology: Records of Successful Implementations)
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Review
Clinical Applications of Somatostatin Receptor (Agonist) PET Tracers beyond Neuroendocrine Tumors
Diagnostics 2022, 12(2), 528; https://doi.org/10.3390/diagnostics12020528 - 18 Feb 2022
Viewed by 477
Abstract
Somatostatin receptor (SSTR) agonist tracers used in nuclear medicine scans are classically used for neuroendocrine tumor diagnosis and staging. SSTR are however, expressed more widely in a variety of cells as seen in the distribution of physiological tracer uptake during whole body scans. [...] Read more.
Somatostatin receptor (SSTR) agonist tracers used in nuclear medicine scans are classically used for neuroendocrine tumor diagnosis and staging. SSTR are however, expressed more widely in a variety of cells as seen in the distribution of physiological tracer uptake during whole body scans. This provides opportunities for using these tracers for applications other than NETs and meningiomas. In this qualitative systematic review, novel diagnostics in SSTR-PET imaging are reviewed. A total of 70 studies comprised of 543 patients were qualitatively reviewed. Sarcoidosis, atherosclerosis and phosphaturic mesenchymal tumors represent the most studied applications currently with promising results. Other applications remain in progress where there are many case reports but a relative dearth of cohort studies. [18F]FDG PET provides the main comparative method in many cases but represents a well-established general PET technique that may be difficult to replace, without prospective clinical studies. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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