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Diagnosis of Peritonsillar Abscess—A Prospective Study Comparing Clinical with CT Findings in 133 Consecutive Patients
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The Dynamic Evolution of Eosinophilic Esophagitis
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Echocardiography with Strain Assessment in Psychiatric Diseases: A Narrative Review
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How to Effectively Communicate Dismal Diagnoses in Dermatology and Venereology: From Skin Cancers to Sexually Transmitted Infections
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Transforming Microbiological Diagnostics in Nosocomial Lower Respiratory Tract Infections: Innovations Shaping the Future
Journal Description
Diagnostics
Diagnostics
is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q1 (Medicine, General and Internal) / CiteScore - Q2 (Internal Medicine)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.3 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Diagnostics include: LabMed and AI in Medicine.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
Clinical TNM Lung Cancer Staging: A Diagnostic Algorithm with a Pictorial Review
Diagnostics 2025, 15(7), 908; https://doi.org/10.3390/diagnostics15070908 (registering DOI) - 1 Apr 2025
Abstract
Lung cancer is a prevalent malignant disease with the highest mortality rate among oncological conditions. The assessment of its clinical TNM staging primarily relies on contrast-enhanced computed tomography (CT) of the thorax and proximal abdomen, sometimes with the addition of positron emission tomography/CT
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Lung cancer is a prevalent malignant disease with the highest mortality rate among oncological conditions. The assessment of its clinical TNM staging primarily relies on contrast-enhanced computed tomography (CT) of the thorax and proximal abdomen, sometimes with the addition of positron emission tomography/CT scans, mainly for better evaluation of mediastinal lymph node involvement and detection of distant metastases. The purpose of TNM staging is to establish a universal nomenclature for the anatomical extent of lung cancer, facilitating interdisciplinary communication for treatment decisions and research advancements. Recent studies utilizing a large international database and multidisciplinary insights indicate a need to update the TNM classification to enhance the anatomical categorization of lung cancer, ultimately optimizing treatment strategies. The eighth edition of the TNM classification, issued by the International Association for the Study of Lung Cancer (IASLC), transitioned to the ninth edition on 1 January 2025. Key changes include a more detailed classification of the N and M descriptor categories, whereas the T descriptor remains unchanged. Notably, the N2 category will be split into N2a and N2b based on the single-station or multi-station involvement of ipsilateral mediastinal and/or subcarinal lymph nodes, respectively. The M1c category will differentiate between single (M1c1) and multiple (M1c2) organ system involvement for extrathoracic metastases. This review article emphasizes the role of radiologists in implementing the updated TNM classification through CT imaging for correct clinical lung cancer staging and optimal patient management.
Full article
(This article belongs to the Special Issue Advances in Lung Cancer Diagnosis)
Open AccessArticle
Predominance of Calcium Pyrophosphate Crystals in Synovial Fluid Samples of Patients at a Large Tertiary Center
by
Tobias Manigold and Alexander Leichtle
Diagnostics 2025, 15(7), 907; https://doi.org/10.3390/diagnostics15070907 (registering DOI) - 1 Apr 2025
Abstract
Background: Crystal arthritides represent the most common inflammatory rheumatologic condition. While the prevalence of gouty arthritis by monosodium urate (MSU) is well established, the prevalences of calciumpyrophosphat (CPP) and basic calcium pyrophosphate (ARP) arthritis are less clear. We herein sought to assess the
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Background: Crystal arthritides represent the most common inflammatory rheumatologic condition. While the prevalence of gouty arthritis by monosodium urate (MSU) is well established, the prevalences of calciumpyrophosphat (CPP) and basic calcium pyrophosphate (ARP) arthritis are less clear. We herein sought to assess the prevalence and inflammatory characteristics of crystal arthritides at our institution, the biggest tertiary center in Switzerland. Methods: A total of 5036 synovial fluid (SF) samples were analyzed with regard to crystal positivity as well as joint, age, and sex distribution in affected patients. We furthermore compared inflammatory and non-inflammatory SF samples for yields of their Polymorphonuclear (PMN) fractions. Results: About half of all samples were derived from knee joints, a male/female ratio up to 10.1:1 among the MSU-positive, and a clear shift towards elder patients with CPP–arthritis was seen. These findings were in line with previous studies and suggest good comparability of our cohort. Of note, 21.9% of all samples were CPP positive, whereas 15.3% and 9.5% were positive for MSU and ARP/alizarin-red positive, respectively. Importantly, CPP crystals were predominant in inflammatory (58.9%) and non-inflammatory (65.7%) samples. By contrast, MSU crystals were significantly more often associated with synovitis (p < 0.001). Interestingly, higher PMN fractions were found in non-inflammatory MSU-positive samples (p < 0.01), whereas a similar trend was seen in CPP-positive samples. Conclusions: CPP arthritis represented the most frequent crystal arthritis form at our center. Higher PMN fractions in non-inflammatory samples with CPP and MSU crystals suggest subclinical inflammation and provide further arguments for earlier anti-inflammatory and uric acid-lowering therapies in patients with crystal deposits.
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(This article belongs to the Section Clinical Laboratory Medicine)
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Open AccessArticle
Multimodality Imaging Features of Papillary Renal Cell Carcinoma
by
Rosita Comune, Francesco Tiralongo, Eleonora Bicci, Pietro Paolo Saturnino, Francesco Michele Ronza, Chandra Bortolotto, Vincenza Granata, Salvatore Masala, Mariano Scaglione, Giacomo Sica, Fabio Tamburro and Stefania Tamburrini
Diagnostics 2025, 15(7), 906; https://doi.org/10.3390/diagnostics15070906 (registering DOI) - 1 Apr 2025
Abstract
Objectives: To describe the US, CEUS, CT, and MRI features of papillary renal cell carcinoma (PRCC) and to underline the imaging characteristics that are helpful in the differential diagnosis. Methods: Patients with histologically proven papillary renal cell carcinoma who underwent at least two
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Objectives: To describe the US, CEUS, CT, and MRI features of papillary renal cell carcinoma (PRCC) and to underline the imaging characteristics that are helpful in the differential diagnosis. Methods: Patients with histologically proven papillary renal cell carcinoma who underwent at least two imaging examinations (US, CEUS, CT, and MRI) were included in the study. Tumor size, homogeneity, morphology, perilesional stranding, contrast enhancement locoregional extension were assessed. A comparison and the characteristics of the imaging features for each imaging modality were analyzed. Results: A total of 27 patients with an histologically confirmed diagnosis of PRCC were included in the study. US was highly accurate in distinguishing solid masses from cystic masses, supporting the differential diagnosis of PRCC, as well as in patients with a poor representation of the solid component. CEUS significantly increased diagnostic accuracy in delineating the solid intralesional component. Furthermore, when using CEUS, in the arterial phase, PRCC exhibited hypo-enhancement, and in the late phase it showed an inhomogeneous and delayed wash-out compared with the surrounding renal parenchyma. At MRI, PRCC showed a marked restiction of DWI and was hypointense in the T2-weighted compared to the renal parenchyma. Conclusions: In our study, the characteristic hypodensity and hypoenhancement of PRCC make CT the weakest method of their recognition, while US/CEUS and MRI are necessary to reach a definitive diagnosis. Knowledge of the appearance of PRCC can support an early diagnosis and prompt management, and radiologists should be aware that PRCC, when detected using CT, may resemble spurious non-septate renal cyst.
Full article
(This article belongs to the Special Issue Imaging Diagnosis in Abdomen, 2nd Edition)
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Open AccessReview
Artificial Intelligence in Inflammatory Bowel Disease Endoscopy
by
Sabrina Gloria Giulia Testoni, Guglielmo Albertini Petroni, Maria Laura Annunziata, Giuseppe Dell’Anna, Michele Puricelli, Claudia Delogu and Vito Annese
Diagnostics 2025, 15(7), 905; https://doi.org/10.3390/diagnostics15070905 (registering DOI) - 1 Apr 2025
Abstract
Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades,
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Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy—and potentially also in histology and imaging in the context of IBD—is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs.
Full article
(This article belongs to the Special Issue Advances in Endoscopy)
Open AccessReview
The Role of ctDNA for Diagnosis and Histological Prediction in Early Stage Non-Small-Cell Lung Cancer: A Narrative Review
by
Carolina Sassorossi, Jessica Evangelista, Alessio Stefani, Marco Chiappetta, Antonella Martino, Annalisa Campanella, Elisa De Paolis, Dania Nachira, Marzia Del Re, Francesco Guerrera, Luca Boldrini, Andrea Urbani, Stefano Margaritora, Angelo Minucci, Emilio Bria and Filippo Lococo
Diagnostics 2025, 15(7), 904; https://doi.org/10.3390/diagnostics15070904 (registering DOI) - 1 Apr 2025
Abstract
Background: Circulating tumor DNA (ctDNA) may be released from neoplastic cells into biological fluids through apoptosis, necrosis, or active release. In patients with non-small-cell lung cancer (NSCLC), ctDNA analysis is being introduced in clinical practice only for advanced disease management. Nevertheless, an interesting
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Background: Circulating tumor DNA (ctDNA) may be released from neoplastic cells into biological fluids through apoptosis, necrosis, or active release. In patients with non-small-cell lung cancer (NSCLC), ctDNA analysis is being introduced in clinical practice only for advanced disease management. Nevertheless, an interesting and promising field of application is the analysis of ctDNA in the management of early stage non-small-cell lung cancer, both for evaluation before treatment, such as diagnosis and screening, and for prediction of histology or pathological features. Methods: A thorough review of the literature published between 2000 and 2024 was performed on PubMed, utilizing the advanced search feature to narrow down titles and abstracts containing the following keywords: ctDNA, early stage, and NSCLC. A total of 20 studies that met all inclusion criteria were chosen for this review. Results: In this review, we summarize the increasing evidence suggesting that ctDNA has potential clinical applications in the management of patients with early stage NSCLC. ctDNA levels in early stage cancers are very low, posing many technical challenges in improving the detection rate and sensitivity, especially in clinical practice, if it is to be implemented for early detection. Presently, the main limitation of ctDNA experimental and clinical studies, especially in early stage settings, is the lack of definitive standardization and consensus regarding methodology, the absence of systematically validated analyses, and the lack of adoption of sensitive approaches. Conclusions: Possible applications of this analyte open up new fields of diagnosis, treatment, and follow up, which are less invasive and more precise than other approaches currently in use, especially in early stage NSCLC patients.
Full article
(This article belongs to the Section Pathology and Molecular Diagnostics)
Open AccessArticle
Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features
by
Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich and Milan Toma
Diagnostics 2025, 15(7), 903; https://doi.org/10.3390/diagnostics15070903 (registering DOI) - 1 Apr 2025
Abstract
Background/Objectives: Electrocardiogram data are widely used to diagnose cardiovascular diseases, a leading cause of death globally. Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. This study explores the
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Background/Objectives: Electrocardiogram data are widely used to diagnose cardiovascular diseases, a leading cause of death globally. Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. This study explores the use of machine learning models to classify electrocardiogram abnormalities using a dataset that combines clinical features (e.g., age, weight, smoking status) with key electrocardiogram measurements, without relying on time-series data. Methods: The dataset included demographic and electrocardiogram-related biometric data. Preprocessing steps addressed class imbalance, outliers, feature scaling, and the encoding of categorical variables. Five machine learning models—Gaussian Naive Bayes, support vector machines, random forest trees, extremely randomized trees, gradient boosted trees, and an ensemble of top-performing classifiers—were trained and optimized using stratified k-fold cross-validation. Model performance was evaluated on a reserved testing set using metrics such as accuracy, precision, recall, and F1-score. Results: The extremely randomized trees model achieved the best performance, with a testing accuracy of 66.79%, recall of 66.79%, and F1-score of 62.93%. Ventricular rate, QRS duration, and QTC (Bezet) were identified as the most important features. Challenges in classifying borderline cases were noted due to class imbalance and overlapping features. Conclusions: This study demonstrates the potential of machine learning models, particularly extremely randomized trees, in classifying electrocardiogram abnormalities using demographic and biometric data. While promising, the absence of time-series data limits diagnostic accuracy. Future work incorporating time-series signals and advanced deep learning techniques could further improve performance and clinical relevance.
Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Signal Analysis)
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Open AccessArticle
Clinicians’ Agreement on Extrapulmonary Radiographic Findings in Chest X-Rays Using a Diagnostic Labelling Scheme
by
Lea Marie Pehrson, Dana Li, Alyas Mayar, Marco Fraccaro, Rasmus Bonnevie, Peter Jagd Sørensen, Alexander Malcom Rykkje, Tobias Thostrup Andersen, Henrik Steglich-Arnholm, Dorte Marianne Rohde Stærk, Lotte Borgwardt, Sune Darkner, Jonathan Frederik Carlsen, Michael Bachmann Nielsen and Silvia Ingala
Diagnostics 2025, 15(7), 902; https://doi.org/10.3390/diagnostics15070902 (registering DOI) - 1 Apr 2025
Abstract
Objective: Reliable reading and annotation of chest X-ray (CXR) images are essential for both clinical decision-making and AI model development. While most of the literature emphasizes pulmonary findings, this study evaluates the consistency and reliability of annotations for extrapulmonary findings, using a labelling
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Objective: Reliable reading and annotation of chest X-ray (CXR) images are essential for both clinical decision-making and AI model development. While most of the literature emphasizes pulmonary findings, this study evaluates the consistency and reliability of annotations for extrapulmonary findings, using a labelling scheme. Methods: Six clinicians with varying experience levels (novice, intermediate, and experienced) annotated 100 CXR images using a diagnostic labelling scheme, in two rounds, separated by a three-week washout period. Annotation consistency was assessed using Randolph’s free-marginal kappa (RK), prevalence- and bias-adjusted kappa (PABAK), proportion positive agreement (PPA), and proportion negative agreement (PNA). Pairwise comparisons and the McNemar’s test were conducted to assess inter-reader and intra-reader agreement. Results: PABAK values indicated high overall grouped labelling agreement (novice: 0.86, intermediate: 0.90, experienced: 0.91). PNA values demonstrated strong agreement on negative findings, while PPA values showed moderate-to-low consistency in positive findings. Significant differences in specific agreement emerged between novice and experienced clinicians for eight labels, but there were no significant variations in RK across experience levels. The McNemar’s test confirmed annotation stability between rounds. Conclusions: This study demonstrates that clinician annotations of extrapulmonary findings in CXR are consistent and reliable across different experience levels using a pre-defined diagnostic labelling scheme. These insights aid in optimizing training strategies for both clinicians and AI models.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessArticle
Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor
by
Yuna Kim, Ji-Soo Keum, Jie-Hyun Kim, Jaeyoung Chun, Sang-Il Oh, Kyung-Nam Kim, Young-Hoon Yoon and Hyojin Park
Diagnostics 2025, 15(7), 901; https://doi.org/10.3390/diagnostics15070901 (registering DOI) - 1 Apr 2025
Abstract
Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy
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Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy videos to enhance AI model performance and reduce manual labeling labor. Methods: An integrated AI model was trained and validated on 86,258 training images and 17,616 validation images. Model 1 utilized only publicly available datasets, while Model 2 additionally incorporated images obtained from real colonoscopy videos of patients through a semi-automatic annotation process, significantly reducing the labeling burden on expert endoscopists. Results: The integrated AI model (Model 2) significantly outperformed the public-dataset-only model (Model 1). At epoch 35, Model 2 achieved a sensitivity of 90.6%, a specificity of 96.0%, an overall accuracy of 94.5%, and an F1 score of 89.9%. All polyps in the test videos were successfully detected, demonstrating considerable enhancement in detection performance compared to the public-dataset-only model. Conclusions: Integrating real-world colonoscopy video data using semi-automatic annotation markedly improved diagnostic accuracy while potentially reducing the need for extensive manual annotation typically performed by expert endoscopists. However, the findings need validation through multicenter external datasets to ensure generalizability.
Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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Open AccessArticle
A Multicentric Study on Adverse COVID-19 Outcomes Among Pregnant and Nonpregnant Women in Multidisciplinary Hospitals of Kazakhstan
by
Zhansaya Nurgaliyeva, Lyudmila Pivina, Sharapat Moiynbayeva, Galiya Alibayeva, Meruyert Suleimenova, Nailya Kozhekenova, Moldir Abdullina, Maulen Malgazhdarov, Mira Turbekova, Dejan Nikolic, Milan Lackovic, Antonio Sarria-Santamera and Milena Santric-Milicevic
Diagnostics 2025, 15(7), 900; https://doi.org/10.3390/diagnostics15070900 (registering DOI) - 1 Apr 2025
Abstract
Background and Objectives: The study aimed at identification and analysis of adverse COVID-19 outcomes (admission to intensive care units due to COVID-19, acute respiratory distress syndrome, mechanical ventilation, and death) among hospitalized pregnant and nonpregnant women, which are critical for informed decision-making in
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Background and Objectives: The study aimed at identification and analysis of adverse COVID-19 outcomes (admission to intensive care units due to COVID-19, acute respiratory distress syndrome, mechanical ventilation, and death) among hospitalized pregnant and nonpregnant women, which are critical for informed decision-making in obstetric diagnostics and healthcare. Materials and Methods: This was a retrospective observational study conducted on a series of inpatient pregnant women comparatively followed up with nonpregnant women hospitalized between 15 July 2020 to 20 January 2022 across multidisciplinary hospitals in three cities of Kazakhstan. Following group matching with propensity score for COVID-19 disease severity, residence status, and age, the study ultimately included 156 participants, of whom 50% were pregnant, from an initial sample of 314 female inpatients diagnosed with COVID-19. All findings were considered statistically significant at a p-value < 0.05. Results: Laboratory investigations revealed significantly elevated levels of erythrocyte sedimentation rate, creatinine, neutrophils, platelet count, alanine aminotransferase, aspartate aminotransferase, lymphocyte count, and C-reactive protein in pregnant inpatients compared to nonpregnant inpatients. Furthermore, pregnant women exhibited significantly higher levels of D-dimer (2402.97 ng/mL vs. 793.91 ng/mL) and procalcitonin (0.398 ng/mL vs. 0.134 ng/mL) compared to their nonpregnant counterparts. Overall, 16.88% of the pregnant women were admitted to the intensive care unit, whereas among the nonpregnant women, only 2.6% were hospitalized. The most lethal outcomes (8.3%) occurred among pregnant women, while for nonpregnant women, there were two cases (1.3%). Conclusions: Pregnant women diagnosed with COVID-19 may exhibit more severe clinical symptoms and encounter more adverse outcomes compared to their nonpregnant counterparts. Future research should incorporate larger matched samples to comprehensively explore the association between additional factors and clinical conditions.
Full article
(This article belongs to the Section Clinical Diagnosis and Prognosis)
Open AccessArticle
Automated Detection, Localization, and Severity Assessment of Proximal Dental Caries from Bitewing Radiographs Using Deep Learning
by
Mashail Alsolamy, Farrukh Nadeem, Amr Ahmed Azhari and Walaa Magdy Ahmed
Diagnostics 2025, 15(7), 899; https://doi.org/10.3390/diagnostics15070899 (registering DOI) - 1 Apr 2025
Abstract
Background/Objectives: Dental caries is a widespread chronic infection, affecting a large segment of the population. Proximal caries, in particular, present a distinct obstacle for early identification owing to their position, which hinders clinical inspection. Radiographic assessments, particularly bitewing images (BRs), are frequently
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Background/Objectives: Dental caries is a widespread chronic infection, affecting a large segment of the population. Proximal caries, in particular, present a distinct obstacle for early identification owing to their position, which hinders clinical inspection. Radiographic assessments, particularly bitewing images (BRs), are frequently utilized to detect these carious lesions. Nonetheless, misinterpretations may obstruct precise diagnosis. This paper presents a deep-learning-based system to improve the evaluation process by detecting proximal dental caries from BRs and classifying their severity in accordance with ICCMSTM guidelines. Methods: The system comprises three fundamental tasks: caries detection, tooth numbering, and describing caries location by identifying the tooth it belongs to and the surface, each built independently to enable reuse across many applications. We analyzed 1354 BRs annotated by a consultant of restorative dentistry to delineate the pertinent categories, concentrating on the detection and localization of caries tasks. A pre-trained YOLOv11-based instance segmentation model was employed, allocating 80% of the dataset for training, 10% for validation, and the remaining portion for evaluating the model on unseen data. Results: The system attained a precision of 0.844, recall of 0.864, F1-score of 0.851, and mAP of 0.888 for segmenting caries and classifying their severity, using an intersection over union (IoU) of 50% and a confidence threshold of 0.25. Concentrating on teeth that are entirely or three-quarters presented in BRs, the system attained 100% for identifying the affected teeth and surfaces. It achieved high sensitivity and accuracy in comparison to dentist evaluations. Conclusions: The results are encouraging, suggesting that the proposed system may effectively assist dentists in evaluating bitewing images, assessing lesion severity, and recommending suitable treatments.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessArticle
Intraoperative Ultrasound Guidance in Laparoscopic Adrenalectomy: A Retrospective Analysis of Perioperative Outcomes
by
Ionela Mihai, Adrian Boicean, Horatiu Dura, Cosmin Adrian Teodoru, Dan Georgian Bratu, Cristian Ichim, Samuel Bogdan Todor, Nicolae Bacalbasa, Alina Simona Bereanu and Adrian Hașegan
Diagnostics 2025, 15(7), 898; https://doi.org/10.3390/diagnostics15070898 (registering DOI) - 1 Apr 2025
Abstract
Background: This study aimed to evaluate the advantages of integrating intraoperative ultrasound (IOUS) into laparoscopic adrenal surgery by assessing its impact on perioperative outcomes and identifying potential complications. Methods: This retrospective study analyzed 128 patients with adrenal gland tumors who underwent
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Background: This study aimed to evaluate the advantages of integrating intraoperative ultrasound (IOUS) into laparoscopic adrenal surgery by assessing its impact on perioperative outcomes and identifying potential complications. Methods: This retrospective study analyzed 128 patients with adrenal gland tumors who underwent a laparoscopic adrenalectomy by comparing those who received intraoperative ultrasound guidance with those who did not. The procedures were performed using either the transperitoneal or the lateral retroperitoneal approach. Results: The IOUS-guided group had significantly lower blood loss (p < 0.001) and a shorter hospitalization duration (p = 0.005) compared with the non-IOUS group. No intraoperative complications were observed in the IOUS group, whereas three complications occurred in the non-IOUS group, including peritoneal breaches and minor liver damage. The retroperitoneal approach demonstrated superior perioperative outcomes, with a shorter operative time (p < 0.001), reduced blood loss (p < 0.001), earlier resumption of oral intake and lower postoperative analgesia requirements (p < 0.001). Conclusions: Intraoperative ultrasound enhanced the surgical precision in laparoscopic adrenalectomy, which reduced the blood loss, shortened the hospital stays and minimized the intraoperative complications.
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(This article belongs to the Special Issue Current Challenges and Perspectives of Ultrasound, 2nd Edition)
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Open AccessEditorial
Special Issue “Diagnosis and Management of Skin Diseases, Related Disorders and Their Comorbidities”
by
Alin Laurentiu Tatu and Lawrence Chukwudi Nwabudike
Diagnostics 2025, 15(7), 897; https://doi.org/10.3390/diagnostics15070897 (registering DOI) - 1 Apr 2025
Abstract
Dermatology is a continuously evolving specialty and touches on every part of the field of medicine [...]
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(This article belongs to the Special Issue Diagnosis and Management of Skin Diseases, Related Disorders and Their Comorbidities)
Open AccessSystematic Review
The Value of Cerebral Blood Volume Derived from Dynamic Susceptibility Contrast Perfusion MRI in Predicting IDH Mutation Status of Brain Gliomas—A Systematic Review and Meta-Analysis
by
José Pablo Martínez Barbero, Francisco Javier Pérez García, Paula María Jiménez Gutiérrez, Marta García Cerezo, David López Cornejo, Gonzalo Olivares Granados, José Manuel Benítez and Antonio Jesús Láinez Ramos-Bossini
Diagnostics 2025, 15(7), 896; https://doi.org/10.3390/diagnostics15070896 (registering DOI) - 1 Apr 2025
Abstract
Background: Dynamic susceptibility contrast perfusion MRI (DSC-MRI) is a promising non-invasive examination to predict histological and molecular characteristics of brain gliomas. However, the diagnostic accuracy of relative cerebral blood volume (rCBV) is heterogeneously reported in the literature. This systematic review and meta-analysis aims
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Background: Dynamic susceptibility contrast perfusion MRI (DSC-MRI) is a promising non-invasive examination to predict histological and molecular characteristics of brain gliomas. However, the diagnostic accuracy of relative cerebral blood volume (rCBV) is heterogeneously reported in the literature. This systematic review and meta-analysis aims to assess the diagnostic accuracy of mean rCBV derived from DSC-MRI in differentiating Isocitrate Dehydrogenase (IDH)-mutant from IDH-wildtype gliomas. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, and EMBASE up to January 2025, following PRISMA guidelines. Eligible studies reported mean CBV values in treatment-naïve gliomas with histologically confirmed IDH status. Pooled estimates of standardized mean differences (SMDs), diagnostic odds ratios (DOR), and area under the receiver-operating characteristic curve (AUC) were computed using a random-effects model. Heterogeneity was assessed via I2 statistic. Meta-regression analyses were also performed. Results: An analysis of 18 studies (n = 1733) showed that mean rCBV is significantly lower in IDH-mutant gliomas (SMD = −0.86; p < 0.0001). The pooled AUC was 0.80 (95% CI, 0.75–0.90), with moderate sensitivity and specificity. Meta-regression revealed no significant influence of DSC-MRI acquisition parameters, although a flip angle showed a trend toward significance (p = 0.055). Conclusions: Mean rCBV is a reliable imaging biomarker for IDH mutation status in gliomas, demonstrating good diagnostic performance. However, heterogeneity in acquisition parameters and post-processing methods limits generalizability of results. Future research should focus on standardizing DSC-MRI protocols.
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(This article belongs to the Special Issue New Advances in Neurosurgery: Clinical Diagnosis, Treatment and Prognosis)
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Open AccessArticle
Prevalence of Upper Gastrointestinal Symptoms and Gastric Dysrhythmias in Diabetic and Non-Diabetic Indian Populations: A Real-World Retrospective Analysis from Electrogastrography Data
by
Sanjay Bandyopadhyay and Ajit Kolatkar
Diagnostics 2025, 15(7), 895; https://doi.org/10.3390/diagnostics15070895 (registering DOI) - 1 Apr 2025
Abstract
Background: Upper gastrointestinal (GI) motility disorders, such as gastroparesis and functional dyspepsia (FD), contribute significantly to morbidity, especially in populations at risk for type 2 diabetes. However, the prevalence and clinical manifestations of these disorders in India, and associated gastric dysrhythmias, are not
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Background: Upper gastrointestinal (GI) motility disorders, such as gastroparesis and functional dyspepsia (FD), contribute significantly to morbidity, especially in populations at risk for type 2 diabetes. However, the prevalence and clinical manifestations of these disorders in India, and associated gastric dysrhythmias, are not well studied within this population. Methods: This retrospective, cross-sectional study analyzed 3689 patients who underwent electrogastrography with water load satiety test (EGGWLST) testing across multiple motility clinics in India. The prevalence of gastroparesis and FD-like symptoms, symptom severity, and their association with diabetes and other comorbidities were evaluated. Symptom severity was assessed using the Gastroparesis Cardinal Symptom Index (GCSI). EGGWLST findings were documented, including the gastric myoelectric activity threshold (GMAT) scores. Results: The study population had a mean age of 43.18 years. GCSI scores indicated that patients had symptoms that were mild (55%), moderate (33%), and severe (8%). Compared with the non-diabetic population, diabetic subjects had significantly higher rates of early satiety (56% vs. 45%, p < 0.0001), bloating (73% vs. 67%, p = 0.005), and reflux (28% vs. 24%, p = 0.029). WLST data analysis revealed that significantly more diabetic subjects ingested <350 mL (16% vs. 12%, p = 0.000016). EGG analysis revealed gastric dysthymias in one-third (65%) of patients. Significantly more diabetic subjects (22% vs. 18% p = 0.015) had a GMAT score >0.59. Conclusions: Upper GI motility disorders are prevalent in India, particularly among diabetic patients. EGG is a valuable tool for characterizing these disorders, and may help in personalizing therapeutic approaches. Further research is required to optimize treatment strategies.
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(This article belongs to the Special Issue Gastrointestinal Motility Disorders: Diagnosis and Management)
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Lung Adenocarcinoma Exhibiting Thanatosomes (Hyaline Bodies), Cytoplasmic Clearing, and Nuclear Pleomorphism, with a KRAS Mutation
by
Mitsuhiro Tachibana, Yutaro Ito, Ryo Fujikawa, Kei Tsukamoto, Masahiro Uehara, Jun Kobayashi and Takuo Hayashi
Diagnostics 2025, 15(7), 894; https://doi.org/10.3390/diagnostics15070894 (registering DOI) - 1 Apr 2025
Abstract
Since epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors were introduced in 2004, various driver gene mutations have been identified in non-small cell lung cancer, particularly adenocarcinoma, where mutations are typically mutually exclusive. EGFR and Kirsten rat sarcoma viral oncogene (KRAS) mutations are
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Since epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors were introduced in 2004, various driver gene mutations have been identified in non-small cell lung cancer, particularly adenocarcinoma, where mutations are typically mutually exclusive. EGFR and Kirsten rat sarcoma viral oncogene (KRAS) mutations are most prevalent in Japan, with routine testing now standard. However, hematoxylin and eosin staining often fails to detect mutations, except in cases such as ALK fusion lung cancer. We report a 76-year-old non-smoking Japanese woman diagnosed with adenocarcinoma confirmed as KRAS G12D/S-positive. Histological features, including thanatosomes (hyaline globules), nuclear pleomorphism, and cytoplasmic clearing, may aid in identifying mutations. Numerous thanatosomes were identified, some containing nuclear dust. Thanatosomes revealed periodic acid–Schiff reactivity with diastase resistance, fuchsinophilia with Masson’s trichrome stain, and dark blue-black color with Mallory’s PTAH stain. This is the first report linking thanatosomes in KRAS-mutant pulmonary adenocarcinoma to apoptosis via cleaved caspase-3 staining.
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(This article belongs to the Section Pathology and Molecular Diagnostics)
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Open AccessReview
AI and Interventional Radiology: A Narrative Review of Reviews on Opportunities, Challenges, and Future Directions
by
Andrea Lastrucci, Nicola Iosca, Yannick Wandael, Angelo Barra, Graziano Lepri, Nevio Forini, Renzo Ricci, Vittorio Miele and Daniele Giansanti
Diagnostics 2025, 15(7), 893; https://doi.org/10.3390/diagnostics15070893 (registering DOI) - 1 Apr 2025
Abstract
The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of
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The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.
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(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging: 2nd Edition)
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Open AccessArticle
A Clinical and Genetic Evaluation of Cases with Folate Receptor α Gene Mutation: A Case Series from Türkiye
by
Abdurrahman Akgun and Ibrahim Tas
Diagnostics 2025, 15(7), 892; https://doi.org/10.3390/diagnostics15070892 (registering DOI) - 1 Apr 2025
Abstract
Background/Objectives: Cerebral folate transporter deficiency is characterized by pauses and regression in general development stages, with ataxia, choreoathetoid movements, and myoclonic epilepsy generally resistant to treatment. The aim of this study was to comprehensively evaluate cases followed up in two centres in
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Background/Objectives: Cerebral folate transporter deficiency is characterized by pauses and regression in general development stages, with ataxia, choreoathetoid movements, and myoclonic epilepsy generally resistant to treatment. The aim of this study was to comprehensively evaluate cases followed up in two centres in Türkiye for a diagnosis of folate receptor-α deficiency. Methods: The study included nine cases from six different families. Results: The patients comprised 22.2% males and there was parental consanguinity in 88.9% of cases. The mean age at which complaints were first noticed was 3.7 years, and the age of definitive diagnosis was 10.4 years. The most frequently seen first complaints were febrile convulsions and attention deficit-hyperactivity-learning difficulties. The diagnosis most commonly made before the definitive diagnosis was epilepsy, and the first seizure occurred at a mean of 5.2 years. On cranial imaging, white matter involvement, cerebellar atrophy and cerebral atrophy were determined most often. Definitive diagnosis was established solely through clinical findings and genetic analysis. Three different variants in the FOLR1 gene were determined. Treatment with folinic acid at a dose of 5.2 mg/kg/day of PO was started at the age of 9.8 years on average, and intravenous folinate was started at different doses. Conclusions: This study stands out as one of the largest case series in the literature and identifies a previously unreported novel variant. Our study suggests that FOLR1-related CFD should be considered in cases with febrile convulsions, developmental delay, ataxia, autism spectrum disorder, acquired microcephaly, and MRI findings of white matter involvement and cerebellar atrophy. Due to an asymptomatic early period, CFD diagnosis may be delayed, and treatment after symptom onset may be less effective. Incorporating FOLR1 gene analysis into newborn screening programmes could facilitate early diagnosis and treatment. It is thought that the application of vagus nerve stimulation, in addition to folinic acid and anticonvulsant drug treatment, could be effective in seizure control.
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(This article belongs to the Section Pathology and Molecular Diagnostics)
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The Role of Ultrasound in Diagnosing Endometrial Pathologies: Adherence to IETA Group Consensus and Preoperative Assessment of Myometrial Invasion in Endometrial Cancer
by
Mihaela Camelia Tîrnovanu, Elena Cojocaru, Vlad Gabriel Tîrnovanu, Bogdan Toma, Ștefan Dragoș Tîrnovanu, Ludmila Lozneanu, Razvan Socolov, Sorana Anton, Roxana Covali and Loredana Toma
Diagnostics 2025, 15(7), 891; https://doi.org/10.3390/diagnostics15070891 (registering DOI) - 1 Apr 2025
Abstract
Background: Ultrasonography is essential for diagnosing endometrial pathologies, such as hyperplasia, polyps, and endometrial cancer. The International Endometrial Tumor Analysis (IETA) group provides guidelines for using ultrasound to assess endometrial thickness, texture, and irregularities, aiding in the diagnosis of these conditions. The aim
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Background: Ultrasonography is essential for diagnosing endometrial pathologies, such as hyperplasia, polyps, and endometrial cancer. The International Endometrial Tumor Analysis (IETA) group provides guidelines for using ultrasound to assess endometrial thickness, texture, and irregularities, aiding in the diagnosis of these conditions. The aim of this study was to evaluate the utility of various endometrial morphological features, as assessed by gray-scale ultrasound, and endometrial vascular features, as assessed by power Doppler ultrasound, in differentiating benign and malignant endometrial pathologies. A secondary objective was to compare the effectiveness of these ultrasound techniques in assessing myometrial invasion. Methods: A total of 162 women, both pre- and postmenopausal, with or without abnormal vaginal bleeding were enrolled in a prospective study. All participants underwent transvaginal gray-scale and color Doppler ultrasound examinations, conducted by examiners with over 15 years of experience in gynecological ultrasonography. Endometrial morphology and vascularity characteristics were evaluated based on the IETA group criteria, which include parameters such as endometrial uniformity, echogenicity, the three-layer pattern, regularity of the endometrial–myometrial border, Doppler color score, and vascular pattern (single dominant vessel with or without branching, multiple vessels with focal or multifocal origin, scattered vessels, color splashes, and circular flow). Sonographic findings were compared with histopathological results for comprehensive assessment. Results: The mean age of the study population was 56.46 ± 10.84 years, with a range from 36 to 88 years. Approximately 53.08% of the subjects were postmenopausal. The mean endometrial thickness, as measured by transvaginal ultrasonography, was 18.02 ± 10.94 mm with a range of 5 to 64 mm (p = 0.028), and it was found to be a significant predictor of endometrial malignancy. The AUC for the ROC analysis was 0.682 (95% CI: 0.452–0.912), with a cut-off threshold of 26 mm, yielding a sensitivity of 62.5% and a specificity of 89%. Vascularization was absent in 68.4% of patients with polyps. Among the cases with submucosal myomas, 80% exhibited a circular flow pattern. Malignant lesions were identified in 22.84% of the cases. Subjective ultrasound assessment of myometrial invasion, categorized as <50% or ≥50%, corresponded in all cases with the histopathological evaluation, demonstrating the effectiveness of ultrasound in evaluating myometrial invasion in endometrial cancer. Conclusions: In this study, cystic atrophic endometrium was identified as the most prevalent cause of postmenopausal bleeding. The most significant ultrasound parameters for predicting malignancy included heterogeneous endometrial echogenicity, increased endometrial thickness, and the presence of multiple vessels with multifocal origins or scattered vascular patterns. Additionally, color Doppler blood flow mapping was demonstrated to be an effective diagnostic tool for the differential diagnosis of benign intrauterine focal lesions.
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(This article belongs to the Special Issue Imaging for the Diagnosis of Obstetric and Gynecological Diseases)
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CCTA-Guided Selective Invasive Coronary Catheterization: A Strategy to Reduce Contrast Volume and Improve Efficiency
by
Jorge Dahdal, Ruurt Jukema, Aernout G. Somsen, Eline Kooijman, Ellaha Wahedi, Jorrit S. Lemkes, Pieter G. Raijmakers, Ton Heestermans, Niels van Royen, Paul Knaapen and Ibrahim Danad
Diagnostics 2025, 15(7), 890; https://doi.org/10.3390/diagnostics15070890 (registering DOI) - 1 Apr 2025
Abstract
Background: Symptomatic patients with unilateral obstructive coronary artery disease (CAD) identified by coronary computed tomography angiography (CCTA), involving either the right or left coronary artery, typically undergo per-protocol bilateral coronary visualization during invasive coronary angiography (ICA). However, a selective visualization approach may be
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Background: Symptomatic patients with unilateral obstructive coronary artery disease (CAD) identified by coronary computed tomography angiography (CCTA), involving either the right or left coronary artery, typically undergo per-protocol bilateral coronary visualization during invasive coronary angiography (ICA). However, a selective visualization approach may be sufficient. Objectives: The objectives of this study were to assess the accuracy of CCTA in excluding hemodynamically significant coronary stenosis in patients with unilateral CAD and to evaluate whether a CCTA-guided selective ICA strategy can reduce procedure time and contrast agent use. Methods: In this cross-sectional cohort study, 454 patients with clinically suspected stable CAD who underwent CCTA prior to ICA were included. The study population consisted of 190 patients with unilateral obstructive CAD, defined as ≥50% diameter stenosis on CCTA, and an absence of obstructive CAD on the contralateral side. ICA with invasive functional assessment was used as the reference standard. Results: CCTA demonstrated a high accuracy, 97.4% (95% CI: 94–99%), in excluding hemodynamically significant disease in the contralateral arteries without obstructive CAD. Compared to the conventional ICA approach, a CCTA-guided selective visualization strategy resulted in significant reductions in procedure time and contrast agent usage: procedure time and contrast agent usage were reduced by 27% (95% CI: 12.1–47.5%) and 46.8% (95% CI: 27.5–67.0%), respectively. Conclusions: In patients with unilateral obstructive CAD identified by CCTA, a CCTA-guided selective ICA visualization strategy is highly accurate in ruling out hemodynamically significant CAD on the contralateral side. Additionally, this unilateral ICA approach has the potential to reduce both contrast agent usage and procedure time compared to the conventional bilateral visualization strategy.
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(This article belongs to the Special Issue New Trends in Cardiovascular Imaging)
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Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review
by
Ahmad Moayad Naser, Rhea Vyas, Ahmed Ashraf Morgan, Abdul Mukhtadir Kalaiger, Amrin Kharawala, Sanjana Nagraj, Raksheeth Agarwal, Maisha Maliha, Shaunak Mangeshkar, Nikita Singh, Vikyath Satish, Sheetal Mathai, Leonidas Palaiodimos and Robert T. Faillace
Diagnostics 2025, 15(7), 889; https://doi.org/10.3390/diagnostics15070889 (registering DOI) - 1 Apr 2025
Abstract
Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored
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Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. A major advancement in this field was the FDA approval of the Aidoc© AI model, which has demonstrated high specificity and negative predictive value in PE diagnosis from imaging scans. Additionally, AI is being explored for optimizing anticoagulation strategies and predicting PE recurrence risk. While further large-scale studies are needed to fully establish AI’s role in clinical practice, its integration holds significant potential to enhance diagnostic accuracy and overall patient management.
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(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)
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