Diagnostics

14 pages, 4489 KiB

Open AccessArticle

Validity and Advantages of Three-Dimensional High-Frequency Ultrasound in Dermatological Evaluation

by Misaki Kinoshita-Ise, Taiichiro Ida, Tatsuro Iwasaki, Hideaki Iwazaki, Kazuyuki Yokota, Hoshito Taguchi and Manabu Ohyama

Diagnostics 2025, 15(2), 223; https://doi.org/10.3390/diagnostics15020223 (registering DOI) - 19 Jan 2025

Abstract

Background/Objectives: High-frequency ultrasound (HFUS) has been reported to be useful for the diagnosis of cutaneous diseases; however, its two-dimensional nature limits the value both in quantitative and qualitative evaluation. Three-dimensional (3D) visualization might help overcome the weakness of the currently existing HFUS. Methods: [...] Read more.

Background/Objectives: High-frequency ultrasound (HFUS) has been reported to be useful for the diagnosis of cutaneous diseases; however, its two-dimensional nature limits the value both in quantitative and qualitative evaluation. Three-dimensional (3D) visualization might help overcome the weakness of the currently existing HFUS. Methods: 3D-HFUS was newly developed and applied to various skin tumors and inflammatory hair diseases to assess its validity and advantages for dermatological use. Results: Three-dimensional images were successfully obtained from skin tumors, including basal cell carcinoma, subungual squamous cell carcinoma, Bowen’s disease, and malignant melanoma, as well as inflammatory hair loss diseases including alopecia areata in different disease phases and lichen planopilaris. Vertical and horizontal images were generated from the original 3D image data and assessed in comparison with histopathological and/or dermoscopic images. By additionally obtaining horizontal data, lateral tumor margins at any depth were visualized in tumors. In inflammatory hair loss diseases, signs potentially associated with disease activity and pathology were detected. In addition, horizontal evaluation helped grasp hair cycle status and hair follicle densities. Conclusions: These findings suggested that this novel technology holds promise as a robust noninvasive tool to diagnose and evaluate various cutaneous diseases. Full article

(This article belongs to the Special Issue Ultrasound in the Diagnosis and Management of Skin Diseases)

14 pages, 1582 KiB

Open AccessArticle

Analysis of Factors Related to Early Left Ventricular Dysfunction in Hypertensive Patients with Preserved Ejection Fraction Using Speckle Tracking Echocardiography: A Cross-Sectional Study in Vietnam

by Hoang M. Tran, Hung P. Truong, Cuong C. Tran, Tuan M. Vo, Dung N. Q. Nguyen and Liem T. Dao

Diagnostics 2025, 15(2), 222; https://doi.org/10.3390/diagnostics15020222 (registering DOI) - 19 Jan 2025

Abstract

Objective: The purpose of this research was to assess the factors linked to early left ventricular (LV) dysfunction in hypertensive patients who have preserved ejection fraction (EF ≥ 50%) using speckle tracking echocardiography. Methods: A cross-sectional study was carried out involving 579 [...] Read more.

Objective: The purpose of this research was to assess the factors linked to early left ventricular (LV) dysfunction in hypertensive patients who have preserved ejection fraction (EF ≥ 50%) using speckle tracking echocardiography. Methods: A cross-sectional study was carried out involving 579 outpatients recruited from City International Hospital in Ho Chi Minh City using a total sampling approach, where echocardiographic measurements and clinical data were gathered and analyzed. Results: The prevalence of LV diastolic dysfunction among hypertensive individuals was found to be 45.25%, with 9.15% showing abnormal global longitudinal strain (GLS). Factors such as being over the age of 60, having diabetes, concentric LV hypertrophy, concentric LV remodeling, and LV diastolic dysfunction were identified as correlating with abnormal GLS (p < 0.05). In contrast, other cardiovascular risk factors, including smoking and dyslipidemia, did not significantly influence the GLS index (p > 0.05). Conclusions: Key factors including diabetes, diastolic dysfunction, concentric hypertrophy, and concentric remodeling of the LV are significant predictors of abnormal GLS. These results are important for the management of hypertensive patients aimed at enhancing cardiac function. Full article

(This article belongs to the Special Issue Cardiovascular Diseases: Diagnosis and Management)

10 pages, 640 KiB

Open AccessArticle

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis

by Tobias Gehlen, Theresa Joost, Philipp Solbrig, Katharina Stahnke, Robert Zahn, Markus Jahn, Dominik Adl Amini and David Alexander Back

Diagnostics 2025, 15(2), 221; https://doi.org/10.3390/diagnostics15020221 (registering DOI) - 19 Jan 2025

Abstract

Background and Objective: The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom [...] Read more.

Background and Objective: The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear. Therefore, this study aimed to investigate the performance of existing symptom checker apps in orthopedic and traumatology cases compared to physicians in the field. Methods: 30 fictitious case vignettes of common conditions in trauma surgery and orthopedics were retrospectively examined by four orthopedic and traumatology specialists and four different symptom checker apps for diagnostic accuracy and the recommended urgency of measures. Based on the estimation provided by the doctors and the individual symptom checker apps, the percentage of correct diagnoses and appropriate assessments of treatment urgency was calculated in mean and standard deviation [SD] in [%]. Data were analyzed statistically for accuracy and correlation between the apps and physicians using a nonparametric Spearman’s correlation test (p < 0.05). Results: The physicians provided the correct diagnosis in 84.4 ± 18.4% of cases (range: 53.3 to 96.7%), and the symptom checker apps in 35.8 ± 1.0% of cases (range: 26.7 to 54.2%). The agreement in the accuracy of the diagnoses varied from low to high (Physicians vs. Physicians: Spearman’s ρ: 0.143 to 0.538; Physicians vs. Apps: Spearman’s ρ: 0.007 to 0.358) depending on the different physicians and apps. In relation to the whole population, the physicians correctly assessed the urgency level in 70.0 ± 4.7% (range: 66.7 to 73.3%) and the apps in 20.6 ± 5.6% (range: 10.8 to 37.5%) of cases. The agreement on the accuracy of estimating urgency levels was moderate to high between and within physicians and individual apps. Conclusions: AI-based symptom checker apps for diagnosis in orthopedics and traumatology do not yet provide a more accurate analysis regarding diagnosis and urgency evaluation than physicians. However, there is a broad variation in the accuracy between different digital tools. Altogether, this field of AI application shows excellent potential and should be further examined in future studies. Full article

(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Surgery and Sport Medicine)

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11 pages, 564 KiB

Open AccessArticle

Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report

by Cristian Lazzari, Sara Montemerani, Cosimo Fabrizi, Cecilia Sacchi, Antoine Belperio, Marilena Fantacci, Giovanni Sbrana, Agostino Ognibene, Maurizio Zanobetti and Simone Nocentini

Diagnostics 2025, 15(2), 220; https://doi.org/10.3390/diagnostics15020220 (registering DOI) - 19 Jan 2025

Abstract

Background: Thanks to the evolution of laboratory medicine, point-of-care testing (POCT) for troponin levels in the blood (hs-cTn) has been greatly improved in order to quickly diagnose acute myocardial infarction (AMI) with an accuracy similar to standard laboratory tests. The rationale of [...] Read more.

Background: Thanks to the evolution of laboratory medicine, point-of-care testing (POCT) for troponin levels in the blood (hs-cTn) has been greatly improved in order to quickly diagnose acute myocardial infarction (AMI) with an accuracy similar to standard laboratory tests. The rationale of the HEART POCT study is to propose the application of the 0/1 h European Society of Cardiology (ESC) algorithm in the pre-hospital setting using a POCT device (Atellica VTLi). Methods: This is a prospective study comparing patients who underwent pre-hospital point-of-care troponin testing (Atellica VTLi) with a control group that underwent standard hospital-based troponin testing (Elecsys). The primary objectives were to determine if the 0/1 h algorithm of the Atellica VTLi is non-inferior to the standard laboratory method for diagnosing AMI and to analyze rule-out/rule-in times and emergency department (ED) stay times. The secondary objective was to evaluate the feasibility of pre-hospital troponin testing. Results: The Atellica VTLi demonstrated reasonable sensitivity for detecting AMI, with sensitivity increasing from 60% at the first measurement (time 0) to 80% at the second measurement (time 1 h). Both the Atellica VTLi and the Elecsys method showed high negative predictive value (NPV), indicating that a negative troponin result effectively ruled out AMI in most cases. Patients in the Atellica VTLi group experienced significantly shorter times to diagnosis and discharge from the emergency department compared to the control group (Elecsys). This highlights a potential benefit of point-of-care testing: streamlining the diagnostic and treatment processes. Conclusions: POCT allows for rapid troponin measurement, leading to a faster diagnosis of non-ST-segment elevation myocardial infarction (NSTEMI). This enables earlier initiation of appropriate treatment, potentially improving patient outcomes and the efficiency of emergency department operations. POCT could be particularly beneficial in pre-hospital settings, enabling faster triage and transportation of patients to appropriate care centers. Full article

(This article belongs to the Section Point-of-Care Diagnostics and Devices)

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17 pages, 1349 KiB

Open AccessReview

Osteoprotegerin as an Emerging Biomarker of Carotid Artery Stenosis? A Scoping Review with Meta-Analysis

by Jerzy Chudek, Marta Pośpiech, Anna Chudek, Michał Holecki and Monika Puzianowska-Kuźnicka

Diagnostics 2025, 15(2), 219; https://doi.org/10.3390/diagnostics15020219 (registering DOI) - 19 Jan 2025

Abstract

Objective: In developed countries, stroke is the fifth cause of death, with a high mortality rate, and with recovery to normal neurological function in one-third of survivors. Atherosclerotic occlusive disease of the extracranial part of the internal carotid artery and related embolic [...] Read more.

Objective: In developed countries, stroke is the fifth cause of death, with a high mortality rate, and with recovery to normal neurological function in one-third of survivors. Atherosclerotic occlusive disease of the extracranial part of the internal carotid artery and related embolic complications are common preventable causes of ischemic stroke (IS), attributable to 7–18% of all first-time cases. Osteoprotegerin (OPG), a soluble member of the tumor necrosis factor receptor (TNFR) superfamily, is considered a modulator of vascular calcification linked to vascular smooth muscle cell proliferation and collagen production in atherosclerotic plaques. Therefore, OPG emerges as a potential biomarker (BM) of calcified carotid plaques and carotid artery stenosis (CAS). Methods: We performed a literature search of PubMed on OPG in CAS and atherosclerosis published until 2024. Results: Increased levels of serum OPG were reported in both patients with symptomatic and asymptomatic CAS, and higher values were observed in those with unstable atherosclerotic plaques. Notably, increased OPG levels were observed regardless of the location of atherosclerosis, including coronary and other peripheral arteries. In addition, chronic kidney disease, the most significant confounder disturbing the association between vascular damage and circulating OPG levels, decreases the usefulness of OPG as a BM in CAS. Conclusions: Osteoprotegerin may be considered an emerging BM of global rather than cerebrovascular atherosclerosis. Its diagnostic significance in identifying patients with asymptomatic CAS and their monitoring is limited. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

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15 pages, 7014 KiB

Open AccessArticle

First Clinical Experience of ⁶⁸Ga-FAPI PET/CT in Tertiary Cancer Center: Identifying Pearls and Pitfalls

by Akram Al-Ibraheem, Ahmed Saad Abdlkadir, Ula Al-Rasheed, Dhuha Al-Adhami, Feras Istatieh, Farah Anwar, Marwah Abdulrahman, Rula Amarin, Issa Mohamad and Asem Mansour

Diagnostics 2025, 15(2), 218; https://doi.org/10.3390/diagnostics15020218 (registering DOI) - 19 Jan 2025

Abstract

Background/Objectives: Over the past four years, ⁶⁸Ga-fibroblast activation protein inhibitor (FAPI) positron emission tomography/computed tomography (PET/CT) has been established at a tertiary cancer care facility in Jordan. This retrospective study aims to explore tracer uptake metrics across various epithelial neoplasms, identify diagnostic [...] Read more.

Background/Objectives: Over the past four years, ⁶⁸Ga-fibroblast activation protein inhibitor (FAPI) positron emission tomography/computed tomography (PET/CT) has been established at a tertiary cancer care facility in Jordan. This retrospective study aims to explore tracer uptake metrics across various epithelial neoplasms, identify diagnostic pitfalls associated with ⁶⁸Ga-FAPI PET/CT, and evaluate the influence of ⁶⁸Ga-FAPI PET/CT staging results on changes in therapeutic intent compared to gold standard molecular imaging modalities. Methods: A total of 48 patients with biopsy-confirmed solid tumors underwent 77 ⁶⁸Ga-FAPI PET/CT examinations for molecular imaging assessment, encompassing neoplasms originating from the gastrointestinal tract, head and neck, hepatobiliary system, pancreas, breast, and lung. Results: Notably, pancreaticobiliary tumors exhibited the highest tracer uptake, with mean maximum standardized uptake values (SUVmax) and tumor-to-background ratios (TBR) surpassing 10. A comparative sub-analysis of ⁶⁸Ga-FAPI PET metrics in 20 treatment-naïve patients revealed a significant correlation between ⁶⁸Ga-FAPI uptake metrics and tumor grade (Spearman’s rho 0.83; p = 0.00001). Importantly, the results from ⁶⁸Ga-FAPI PET/CT influenced treatment decisions in 35.5% of the cases, primarily resulting in an escalation of management plans. A total of 220 diagnostic challenges were identified across 88.3% of the scans, predominantly within the musculoskeletal system, attributed to degenerative changes (99 observations). Conclusions: This comprehensive analysis highlights the potential significance of ⁶⁸Ga-FAPI PET/CT in oncological imaging and treatment strategy, while also emphasizing the necessity for meticulous interpretation to mitigate diagnostic challenges. Full article

(This article belongs to the Special Issue PET/CT Imaging in Cancers)

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16 pages, 706 KiB

Open AccessArticle

Potentials of Presepsin as a Novel Sepsis Biomarker in Critically Ill Adults: Correlation Analysis with the Current Diagnostic Markers

by Mai S. Sater, Nourah Almansour, Zainab Hasan Abdulla Malalla, Salim Fredericks, Muhalab E. Ali and Hayder A. Giha

Diagnostics 2025, 15(2), 217; https://doi.org/10.3390/diagnostics15020217 (registering DOI) - 18 Jan 2025

Abstract

Background: Sepsis is a major cause of patient death in intensive care units (ICUs). Rapid diagnosis of sepsis assists in optimizing treatments and improves outcomes. Several biomarkers are employed to aid in the diagnosis, prognostication, severity grading, and sub-type discrimination of severe septic [...] Read more.

Background: Sepsis is a major cause of patient death in intensive care units (ICUs). Rapid diagnosis of sepsis assists in optimizing treatments and improves outcomes. Several biomarkers are employed to aid in the diagnosis, prognostication, severity grading, and sub-type discrimination of severe septic infections (SSIs), including current diagnostic parameters, hemostatic measures, and specific organ dysfunction markers. Methods: This study involved 129 critically ill adults categorized into three groups: sepsis (Se = 48), pneumonia (Pn = 48), and Se/Pn (33). Concentrations of five plasma markers (IL-6, IL-8, TREM1, uPAR, and presepsin) were compared with 13 well-established measures of SSI in critically ill patients. These measures were heart rate (HR), white blood count (WBC), C-reactive protein (CRP), procalcitonin (PCT), lactate plasma concentrations, and measures of hemostasis status (platelets count (PLT), fibrinogen, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalization ratio (INR) and D-dimer). Plasma bilirubin and creatinine served as indicators of liver and kidney dysfunction, respectively. Results: Promising roles for these biomarkers were found. The best results were for presepsin, which scored 10/13, followed by IL-6 and IL-8 (each scored 7/13), and the worst were for TREM-1 and uPAR (scored 3/13). Presepsin, IL-6, and IL-8 discriminated between the SSI sub-types, whilst only presepsin correlated with bilirubin and creatinine. uPAR was positive for kidney dysfunction, and TREM-1 was the only indicator of artificial ventilation (AV). Conclusions: Presepsin is an important potential biomarker in SSIs. However, further work is needed to define this marker’s diagnostic and prognostic cutoff values. Full article

(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)

14 pages, 2350 KiB

Open AccessArticle

Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification

by Chun-Chao Huang, Hsin-Fan Chiang, Cheng-Chih Hsieh, Bo-Rui Zhu, Wen-Jie Wu and Jin-Siang Shaw

Diagnostics 2025, 15(2), 216; https://doi.org/10.3390/diagnostics15020216 (registering DOI) - 18 Jan 2025

Abstract

Background: This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). Methods: [...] Read more.

Background: This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). Methods: During the training, we compiled all images of each brain computed tomography scan into a single 3D image, which was then fed into the model to classify the presence of ICH. We divided the non-hemorrhage quantities into 20, 30, 40, 50, 100, and 150 and the ICH quantities into 20, 30, 40, and 50. Cross-validation was performed to compute the average area under the curve (AUC) over the last five iterations. The AUC and accuracy were used to evaluate the performance of the models. Results: Fifty patients, each with the three ICH types, and 150 non-hemorrhage cases were enrolled. Larger sample sizes achieved stable and acceptable performance in the artificial intelligence (AI) models, whereas training with a limited number of cases posed the risk of falsely high AUC values or accuracy. The overall trends and fluctuations in AUC values were similar between IPH and SDH but different for SAH. The accuracy of the results was relatively consistent among the three ICH types. Conclusions: The 3DCNN technique can be used to develop AI models capable of detecting ICH from limited case numbers. However, a minimal case number must be provided. The performance of AI models varies across different ICH types and is more stable with larger sample sizes. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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11 pages, 423 KiB

Open AccessReview

Illuminating the Shadows: Innovation in Advanced Imaging Techniques for Myeloma Precursor Conditions

by Kara I. Cicero, Rahul Banerjee, Mary Kwok, Danai Dima, Andrew J. Portuguese, Delphine Chen, Majid Chalian and Andrew J. Cowan

Diagnostics 2025, 15(2), 215; https://doi.org/10.3390/diagnostics15020215 (registering DOI) - 18 Jan 2025

Abstract

Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), the asymptomatic precursors to multiple myeloma, affect up to 5% of the population over the age of 40. Bone involvement, a myeloma-defining event, represents a major source of morbidity for patients. Key [...] Read more.

Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), the asymptomatic precursors to multiple myeloma, affect up to 5% of the population over the age of 40. Bone involvement, a myeloma-defining event, represents a major source of morbidity for patients. Key goals for the management of myeloma precursor conditions include (1) identifying patients at the highest risk for progression to MM with bone involvement and (2) differentiating precursor states from active myeloma requiring treatment. Computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT with [¹⁸F]fluorodeoxyglucose (FDG) have improved sensitivity for the detection of myeloma bone disease compared to traditional skeletal surveys, and such advanced imaging also provides this field with better tools for detecting early signs of progression. Herein, we review the data supporting the use of advanced imaging for both diagnostics and prognostication in myeloma precursor conditions. Full article

(This article belongs to the Special Issue Advances in Multiple Myeloma Imaging in 2025)

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12 pages, 1095 KiB

Open AccessArticle

Ganciclovir Resistance-Linked Mutations in the HCMV UL97 Gene: Sanger Sequencing Analysis in Samples from Transplant Recipients at a Tertiary Hospital in Southern Brazil

by Anna Caroline Avila da Rocha, Grazielle Motta Rodrigues, Alessandra Helena da Silva Hellwig, Dariane Castro Pereira, Fabiana Caroline Zempulski Volpato, Afonso Luís Barth and Fernanda de-Paris

Diagnostics 2025, 15(2), 214; https://doi.org/10.3390/diagnostics15020214 (registering DOI) - 18 Jan 2025

Abstract

Background/Objectives: Human cytomegalovirus (HCMV) DNAemia remains a significant concern for transplant recipients, largely due to mutations in the viral genome that may lead to antiviral-resistant strains. Mutations in the UL97 gene are frequently associated with resistance to ganciclovir (GCV), highlighting the importance of [...] Read more.

Background/Objectives: Human cytomegalovirus (HCMV) DNAemia remains a significant concern for transplant recipients, largely due to mutations in the viral genome that may lead to antiviral-resistant strains. Mutations in the UL97 gene are frequently associated with resistance to ganciclovir (GCV), highlighting the importance of early mutation detection to effectively manage viremia. This study aimed to optimize a Sanger sequencing protocol for analyzing GCV resistance-linked mutations in the HCMV UL97 gene from plasma samples of transplant patients treated at Hospital de Clínicas de Porto Alegre, Rio Grande do Sul, Brazil. Methods: A nested-PCR approach combined with a touchdown PCR method was employed to enhance the sensitivity and specificity of the sequencing analysis. Results: The study sample included various transplants, encompassing solid organ and bone marrow recipients. Among 16 sequenced samples, 8 exhibited nucleotide substitutions resulting in amino acid changes. Notably, the A594V and C603W mutations, associated with GCV resistance, were identified in four samples. Additionally, three mutations with unknown phenotypic impact (P509L, A628T, and H662Y) and two viral polymorphisms (N510S and D605E) were detected. Furthermore, double peaks in the Sanger electropherograms, indicative of mixed viral populations of HCMV were observed in seven samples. Conclusions: The optimized Sanger sequencing protocol provides a cost-effective solution for detecting GCV resistance mutations in HCMV UL97 among transplant recipients. This approach could improve the understanding of HCMV strain dynamics and serve as a valuable tool for long-term patient monitoring, particularly within resource-constrained settings such as the public health systems of middle-income countries. Full article

(This article belongs to the Special Issue New Diagnostic and Testing Strategies for Infectious Diseases)

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11 pages, 204 KiB

Open AccessArticle

Impact of Extended Membrane Rupture on Neonatal Inflammatory Responses and Composite Neonatal Outcomes in Early-Preterm Neonates—A Prospective Study

by Maura-Adelina Hincu, Liliana Gheorghe, Luminita Paduraru, Daniela-Cristina Dimitriu, Anamaria Harabor, Ingrid-Andrada Vasilache, Iustina Solomon-Condriuc, Alexandru Carauleanu, Ioana Sadiye Scripcariu and Dragos Nemescu

Diagnostics 2025, 15(2), 213; https://doi.org/10.3390/diagnostics15020213 (registering DOI) - 18 Jan 2025

Abstract

Background/Objectives: Prolonged prelabour rupture of membranes (PROMs), and the resulting inflammatory response, can contribute to the occurrence of adverse neonatal outcomes, especially for early-preterm neonates. This prospective study aimed to measure neonates’ inflammatory markers in the first 72 h of life based [...] Read more.

Background/Objectives: Prolonged prelabour rupture of membranes (PROMs), and the resulting inflammatory response, can contribute to the occurrence of adverse neonatal outcomes, especially for early-preterm neonates. This prospective study aimed to measure neonates’ inflammatory markers in the first 72 h of life based on ROM duration. The second aim was to examine the relationship between PROMs, serum inflammatory markers, and composite adverse neonatal outcomes after controlling for gestational age (GA). Methods: Data from 1026 patients were analyzed considering the following groups: group 1 (ROM < 18 h, n = 447 patients) and group 2 (ROM > 18 h, n = 579 patients). These groups were further segregated depending on the GA at the moment of membranes’ rupture into subgroup 1 (<33 weeks of gestation and 6 days, n = 168 patients) and subgroup 2 (at least 34 completed weeks of gestation, n = 858 patients). Multiple logistic regressions and interaction analyses adjusted for GA considering five composite adverse neonatal outcomes and predictors were employed. Results: PROMs and high c-reactive protein (CRP) values significantly increased the risk of composite outcome 1 occurrence by 14% (95%CI: 1.03–1.57, p < 0.001). PROMs and high CRP values increased the risk of composite outcome 5 by 14% (95%CI: 1.07–1.78, p < 0.001), PROM and leukocytosis by 11% (95%CI: 1.02–1.59, p = 0.001), and PROMs and high PCT values by 21% (95%CI: 1.04–2.10, p < 0.001). Conclusions: The combination of PROMs and high CRP values significantly increased the risk of all evaluated adverse composite outcomes in early-preterm neonates and should point to careful monitoring of these patients. Full article

(This article belongs to the Special Issue Advancements in Maternal–Fetal Medicine)

11 pages, 1004 KiB

Open AccessArticle

Comparative Analysis of Automated and Handheld Breast Ultrasound Findings for Small (≤1 cm) Breast Cancers Based on BI-RADS Category

by Han Song Mun, Eun Young Ko, Boo-Kyung Han, Eun Sook Ko, Ji Soo Choi, Haejung Kim, Myoung Kyoung Kim and Jieun Kim

Diagnostics 2025, 15(2), 212; https://doi.org/10.3390/diagnostics15020212 - 17 Jan 2025

Abstract

Objectives: This study aimed to compare ultrasound (US) findings between automated and handheld breast ultrasound (ABUS and HHUS, respectively) in small breast cancers, based on the breast imaging reporting and data system (BI-RADS) category. Methods: We included 51 women (mean age: [...] Read more.

Objectives: This study aimed to compare ultrasound (US) findings between automated and handheld breast ultrasound (ABUS and HHUS, respectively) in small breast cancers, based on the breast imaging reporting and data system (BI-RADS) category. Methods: We included 51 women (mean age: 52 years; range: 39–66 years) with breast cancer (invasive or DCIS), all of whom underwent both ABUS and HHUS. Patients with tumors measuring ≤1 cm on either modality were enrolled. Two breast radiologists retrospectively evaluated multiple imaging features, including shape, orientation, margin, echo pattern, and posterior characteristics and assigned BI-RADS categories. Lesion sizes were compared between US and pathological findings. Statistical analyses were performed using Bowker’s test of symmetry, a paired t-test, and a cumulative link mixed model. Results: ABUS assigned lower BI-RADS categories than HHUS while still maintaining malignancy suspicion in categories 4A or higher (54.8% consistent with HHUS; 37.3% downcategorized in ABUS, p = 0.005). While ABUS demonstrated less aggressive margins in some cases (61.3% consistent with HHUS; 25.8% showing fewer suspicious margins in ABUS), this difference was not statistically significant (p = 0.221). Similarly, ABUS exhibited slightly greater height–width ratios compared to HHUS (median, interquartile range: 0.98, 0.7–1.12 vs. 0.86, 0.74–1.10, p = 0.166). No significant differences were observed in other US findings or tumor sizes between the two modalities (all p > 0.05). Conclusions: Small breast cancers exhibited suspicious US features on both ABUS and HHUS, yet they were assigned lower BI-RADS assessment categories on ABUS compared to HHUS. Therefore, when conducting breast cancer screening with ABUS, it is important to remain attentive to even subtle suspicious findings, and active consideration for biopsy may be warranted. Full article

(This article belongs to the Special Issue Recent Advances in Breast Imaging)

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23 pages, 5680 KiB

Open AccessArticle

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation

by Manash Sarma and Subarna Chatterjee

Diagnostics 2025, 15(2), 211; https://doi.org/10.3390/diagnostics15020211 - 17 Jan 2025

Abstract

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer’s disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer’s [...] Read more.

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer’s disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. The investigation obtained the highest multiclassification performance to date in the multistage diagnosis of Alzheimer’s disease utilizing the blood gene expression profiles of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. This study presents the sole investigation in which multiclassification-based AD stage diagnosis was conducted utilizing blood gene expression data. We obtained the best multiclassification result in both modalities of the ADNI data in terms of F1-score and were able to identify new genetic biomarkers. Methods: The combination of the XGBoost and SFBS (Sequential Floating Backward Selection) methods was used to select the features. We were able to select the 95 most effective gene probe sets out of 49,386. For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages—cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD)/dementia. DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. Because of the high data imbalance in genomic data, borderline oversampling/data augmentation was applied in the model training and original samples for validation. Results: Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. The highest F1 scores achieved were 0.971, 0.939, and 0.886. Employing gene expression data, we obtained ROC AUC scores of 0.763, 0.761, and 0.706 for the CN, MCI, and dementia stages, respectively, and F1 scores of 0.71, 0.77, and 0.53 for CN, MCI, and dementia, respectively. Conclusions: This represents the best outcome to date for AD stage diagnosis from ADNI blood gene expression profile data utilizing multiclassification techniques. The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. MAPK14, PLG, FZD2, FXYD6, and TEP1 are among the novel genes identified as being associated with AD risk. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)

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29 pages, 566 KiB

Open AccessSystematic Review

Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review

by Kholoud Elnaggar, Mostafa M. El-Gayar and Mohammed Elmogy

Diagnostics 2025, 15(2), 210; https://doi.org/10.3390/diagnostics15020210 - 17 Jan 2025

Abstract

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become [...] Read more.

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

16 pages, 1079 KiB

Open AccessArticle

Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis

by Qingqing Zhu, Qi Wang, Xi Hu, Xin Dang, Xiaojing Yu, Liye Chen and Hongjie Hu

Diagnostics 2025, 15(2), 209; https://doi.org/10.3390/diagnostics15020209 - 17 Jan 2025

Abstract

Objectives:We wished to compare the diagnostic performance of texture analysis (TA) against that of a visual qualitative assessment in identifying early sacroiliitis (nr-axSpA). Methods: A total of 92 participants were retrospectively included at our university hospital institution, comprising 30 controls and 62 [...] Read more.

Objectives:We wished to compare the diagnostic performance of texture analysis (TA) against that of a visual qualitative assessment in identifying early sacroiliitis (nr-axSpA). Methods: A total of 92 participants were retrospectively included at our university hospital institution, comprising 30 controls and 62 patients with axSpA, including 32 with nr-axSpA and 30 with r-axSpA, who underwent MR examination of the sacroiliac joints. MRI at 3T of the lumbar spine and the sacroiliac joint was performed using oblique T1-weighted (W), fluid-sensitive, fat-saturated (Fs) T2WI images. The modified New York criteria for AS were used. Patients were classified into the nr-axSpA group if their digital radiography (DR) and/or CT results within 7 days from the MR examination showed a DR and/or CT grade <2 for the bilateral sacroiliac joints or a DR and/or CT grade <3 for the unilateral sacroiliac joint. Patients were classified into the r-axSpA group if their DR and/or CT grade was 2 to 3 for the bilateral sacroiliac joints or their DR and/or CT grade was 3 for the unilateral sacroiliac joint. Patients were considered to have a confirmed diagnosis if their DR or CT grade was 4 for the sacroiliac joints and were thereby excluded. A control group of healthy individuals matched in terms of age and sex to the patients was included in this study. First, two readers independently qualitatively scored the oblique coronal T1WI and FsT2WI non-enhanced sacroiliac joint images. The diagnostic efficacies of the two readers were judged and compared using an assigned Likert score, conducting a Kappa consistency test of the diagnostic results between two readers. Texture analysis models (the T1WI-TA model and the FsT2WI-TA model) were constructed through feature extraction and feature screening. The qualitative and quantitative results were evaluated for their diagnostic performance and compared against a clinical reference standard. Results: The qualitative scores of the two readers could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA and r-axSpA groups (both p < 0.05). Both TA models could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA group and the r-axSpA group (both p < 0.05). There was no significant difference in the differential diagnoses of the two TA models between the healthy controls and the nr-axSpA group (AUC: 0.934 vs. 0.976; p = 0.1838) and between the nr-axSpA and r-axSpA groups (AUC: 0.917 vs. 0.848; p = 0.2592). In terms of distinguishing between the healthy control and nr-axSpA groups, both the TA models were superior to the qualitative scores of the two readers (all p < 0.05). In terms of distinguishing between the nr-axSpA and r-axSpA groups, the T1WI-TA model was superior to the qualitative scores of the two readers (p = 0.023 and p = 0.007), whereas there was no significant difference between the fsT2WI-TA model and the qualitative scores of the two readers (p = 0.134 and p = 0.065). Conclusions: Based on MR imaging, the T1WI-TA and fsT2WI-TA models were highly effective for the early diagnosis of sacroiliac joint arthritis. The T1WI-TA model significantly improved the early diagnostic efficacy for sacroiliac arthritis compared to that of the qualitative scores of the readers, while the efficacy of the fsT2WI-TA model was comparable to that of the readers. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

15 pages, 229 KiB

Open AccessReview

Fetal Safety in MRI During Pregnancy: A Comprehensive Review

by Gal Puris, Angela Chetrit and Eldad Katorza

Diagnostics 2025, 15(2), 208; https://doi.org/10.3390/diagnostics15020208 - 17 Jan 2025

Abstract

As medical imaging continues to expand, concerns about the potential risks of ionizing radiation to the developing fetus have led to a preference for non-radiation-based alternatives such as ultrasonography and fetal MRI. This review examines the current evidence on the safety of MRI [...] Read more.

As medical imaging continues to expand, concerns about the potential risks of ionizing radiation to the developing fetus have led to a preference for non-radiation-based alternatives such as ultrasonography and fetal MRI. This review examines the current evidence on the safety of MRI during pregnancy, with a focus on 3 T MRI and contrast agents, aiming to provide a comprehensive synthesis that informs clinical decision-making, ensures fetal safety and supports the safe use of all available modalities that could impact management. We conducted a comprehensive review of studies from 2000 to 2024 on MRI safety during pregnancy, focusing on 3 T MRI and gadolinium use. The review included peer-reviewed articles and large database studies, summarizing key findings and identifying areas for further research. Fetal MRI, used alongside ultrasound, enhances diagnostic accuracy for fetal anomalies, particularly in the brain, thorax, gastrointestinal and genitourinary systems, with no conclusive evidence of adverse effects on fetal development. While theoretical risks such as tissue heating and acoustic damage exist, studies show no significant harm at 1.5 T or 3 T, though caution is still advised in the first trimester. Regarding gadolinium-based contrast agents, the evidence is conflicting: while some studies suggest risks such as stillbirth and rheumatological conditions, animal studies show minimal fetal retention and no significant toxicity, and later clinical research has not substantiated these risks. The existing literature on fetal MRI is encouraging, suggesting minimal risks; however, further investigation through larger, prospective and long-term follow-up studies is essential to comprehensively determine its safety and late effects. Full article

(This article belongs to the Special Issue Advances in Fetal Diagnosis and Therapy)

15 pages, 3242 KiB

Open AccessArticle

Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation

by Amani Ben Khalifa, Manel Mili, Mezri Maatouk, Asma Ben Abdallah, Mabrouk Abdellali, Sofiene Gaied, Azza Ben Ali, Yassir Lahouel, Mohamed Hedi Bedoui and Ahmed Zrig

Diagnostics 2025, 15(2), 207; https://doi.org/10.3390/diagnostics15020207 - 17 Jan 2025

Abstract

Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model’s performance [...] Read more.

Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model’s performance in comparison to various pre-trained base models and MRI readers. Methods: This study included 361 LGE images for MI, 222 for myocarditis, and 254 for the healthy class. The left ventricle was extracted automatically using a U-net segmentation model on LGE images. Fine-tuned VGG16 was performed for feature extraction. A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. The evaluation metrics were calculated using a separate test set. To compare the VGG16 model’s performance in feature extraction, various pre-trained base models were evaluated: VGG19, DenseNet121, DenseNet201, MobileNet, InceptionV3, and InceptionResNetV2. The Support Vector Machine (SVM) classifier was evaluated and compared to MLP for the classification task. The performance of the VGG16-MLP model was compared with a subjective visual analysis conducted by two blinded independent readers. Results: The VGG16-MLP model allowed high-performance differentiation between MI, myocarditis, and healthy LGE cardiac MRI images. It outperformed the other tested models with 96% accuracy, 97% precision, 96% sensitivity, and 96% F1-score. Our model surpassed the accuracy of Reader 1 by 27% and Reader 2 by 17%. Conclusions: Our study demonstrated that the VGG16-MLP model permits accurate classification of MI, myocarditis, and healthy LGE cardiac MRI images and could be considered a reliable computer-aided diagnosis approach specifically for radiologists with limited experience in cardiovascular imaging. Full article

(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)

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12 pages, 1188 KiB

Open AccessArticle

Diagnostic Potential of CTRP5 and Chemerin for Coronary Artery Disease: A Study by Coronary Computed Tomography Angiography

by Taha Okan, Cihan Altın, Caner Topaloglu, Mehmet Doruk and Mehmet Birhan Yılmaz

Diagnostics 2025, 15(2), 206; https://doi.org/10.3390/diagnostics15020206 - 17 Jan 2025

Abstract

Background/Objectives: As an endocrine organ, adipose tissue produces adipokines that influence coronary artery disease (CAD). The objective of this study was to assess the potential value of CTRP5 and chemerin in differentiating coronary computed tomography angiography (CCTA)-confirmed coronary artery disease (CAD) versus [...] Read more.

Background/Objectives: As an endocrine organ, adipose tissue produces adipokines that influence coronary artery disease (CAD). The objective of this study was to assess the potential value of CTRP5 and chemerin in differentiating coronary computed tomography angiography (CCTA)-confirmed coronary artery disease (CAD) versus non-CAD. Secondarily, within the CCTA-confirmed CAD group, the aim was to investigate the relationship between the severity and extent of CAD, as determined by coronary artery calcium score (CACS), and the levels of CTRP5 and chemerin. Methods: Consecutive individuals with chest pain underwent CCTA to evaluate coronary artery anatomy and were divided into two groups. The CCTA-confirmed CAD group included patients with any atherosclerotic plaque (soft, mixed, or calcified) regardless of calcification, while the non-CAD group consisted of individuals without plaques on CCTA, with zero CACS, and without ischemia on stress ECG. Secondarily, in the CCTA-confirmed CAD group, the severity and extent of CAD were evaluated using CACS. Blood samples were collected and stored at −80 °C for analysis of CTRP5 and chemerin levels via ELISA. Results: Serum CTRP5 and chemerin levels were significantly higher in the CAD group compared to the non-CAD group (221.83 ± 103.81 vs. 149.35 ± 50.99 ng/mL, p = 0.003 and 105.02 ± 35.62 vs. 86.07 ± 19.47 ng/mL, p = 0.005, respectively). Receiver operating characteristic (ROC) analysis showed that a CTRP5 cutoff of 172.30 ng/mL had 70% sensitivity and 73% specificity for identifying CAD, while a chemerin cutoff of 90.46 ng/mL had 61% sensitivity and 62% specificity. A strong positive correlation was observed between CTRP5 and chemerin, but neither adipokine showed a correlation with the Agatston score, a measure of CAD severity and extent, nor with coronary artery stenosis as determined by CCTA. Conclusions: CTRP5 and chemerin were significantly elevated in the CCTA-confirmed CAD group compared to the non-CAD group, with CTRP5 showing greater sensitivity and specificity. However, neither adipokine was linked to CAD severity and extent, differing from findings based on invasive coronary angiography (ICA). CTRP5 may serve as a promising “all-or-none biomarker” for CAD presence. Full article

(This article belongs to the Special Issue Advances in the Diagnosis and Management of Cardiovascular Diseases)

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3 pages, 2043 KiB

Open AccessInteresting Images

Marked Gingival Overgrowth Protruding from the Oral Cavity Due to Sodium Valproate

by Mami Uegami, Hiroaki Ito and Tadashi Shiohama

Diagnostics 2025, 15(2), 205; https://doi.org/10.3390/diagnostics15020205 - 17 Jan 2025

Abstract

Drug-induced gingival overgrowth is associated with various systemic diseases, including epilepsy. Among antiepileptic medications, phenytoin is commonly reported to cause this condition. In contrast, sodium valproate (VPA), another widely used antiepileptic drug, rarely induces gingival overgrowth. This difference in side effects highlights the [...] Read more.

Drug-induced gingival overgrowth is associated with various systemic diseases, including epilepsy. Among antiepileptic medications, phenytoin is commonly reported to cause this condition. In contrast, sodium valproate (VPA), another widely used antiepileptic drug, rarely induces gingival overgrowth. This difference in side effects highlights the variability in drug-induced oral complications among different antiepileptic medications. This case study presents a patient who developed significant gingival overgrowth after using VPA for over 10 years. The study aims to identify VPA as the causative agent and observe changes during long-term administration and after dose reduction. Our findings demonstrate that even long-standing gingival overgrowth can improve rapidly following discontinuation of the causative medication, providing valuable insights for managing similar cases in the future. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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