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Search Results (532)

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Keywords = computer-aided diagnostic

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33 pages, 6967 KB  
Article
LCxNet: An Explainable CNN Framework for Lung Cancer Detection in CT Images Using Multi-Optimizer and Visual Interpretability
by Noor S. Jozi and Ghaida A. Al-Suhail
Appl. Syst. Innov. 2025, 8(5), 153; https://doi.org/10.3390/asi8050153 - 15 Oct 2025
Viewed by 264
Abstract
Lung cancer, the leading cause of cancer-related mortality worldwide, necessitates better methods for earlier and more accurate detection. To this end, this study introduces LCxNet, a novel, custom-designed convolutional neural network (CNN) framework for computer-aided diagnosis (CAD) of lung cancer. The IQ-OTH/NCCD lung [...] Read more.
Lung cancer, the leading cause of cancer-related mortality worldwide, necessitates better methods for earlier and more accurate detection. To this end, this study introduces LCxNet, a novel, custom-designed convolutional neural network (CNN) framework for computer-aided diagnosis (CAD) of lung cancer. The IQ-OTH/NCCD lung CT dataset, which includes three different classes—benign, malignant, and normal—is used to train and assess the model. The framework is implemented using five optimizers, SGD, RMSProp, Adam, AdamW, and NAdam, to compare the learning behavior and performance stability. To bridge the gap between model complexity and clinical utility, we integrated Explainable AI (XAI) methods, specifically Grad-CAM for decision visualization and t-SNE for feature space analysis. With accuracy, specificity, and AUC values of 99.39%, 99.45%, and 100%, respectively, the results demonstrate that the LCxNet model outperformed the state-of-the-art models in terms of diagnostic performance. In conclusion, this study emphasizes how crucial XAI is to creating trustworthy and efficient clinical tools for the early detection of lung cancer. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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35 pages, 2135 KB  
Review
Hybrid Molecular–Electronic Computing Systems and Their Perspectives in Real-Time Medical Diagnosis and Treatment
by David J. Herzog and Nitsa J. Herzog
Electronics 2025, 14(20), 3996; https://doi.org/10.3390/electronics14203996 - 12 Oct 2025
Viewed by 327
Abstract
Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a [...] Read more.
Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a molecular basis. Molecular computing, both organic and inorganic, has the advantages of high computational density, scalability, energy efficiency and parallel computing. Carbon-based and carbohydrate molecular machines are potentially biocompatible and well-suited for biomedical tasks. Molecular computing-enabled sensors, medication-delivery molecular machines, and diagnostic and therapeutic nanobots are at the cutting edge of medical research. Highly focused diagnostics, precision medicine, and personalized treatment can be achieved with molecular computing tools and machinery. At the same time, traditional electronics and AI advancements create a highly effective computerized environment for analyzing big data, assist in diagnostics with sophisticated pattern recognition and step in as a medical routine aid. The combination of the advantages of MOSFET-based electronics and molecular computing creates an opportunity for next-generation healthcare. Full article
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33 pages, 845 KB  
Review
An Overview of AI-Guided Thyroid Ultrasound Image Segmentation and Classification for Nodule Assessment
by Michalis Savelonas
Big Data Cogn. Comput. 2025, 9(10), 255; https://doi.org/10.3390/bdcc9100255 - 10 Oct 2025
Viewed by 586
Abstract
Accurate segmentation and analysis of thyroid nodules in ultrasound (US) images are essential for the diagnosis and management of thyroid conditions, including cancer. Despite advancements in medical imaging, achieving accurate and efficient segmentation remains a significant challenge due to the complexity and variability [...] Read more.
Accurate segmentation and analysis of thyroid nodules in ultrasound (US) images are essential for the diagnosis and management of thyroid conditions, including cancer. Despite advancements in medical imaging, achieving accurate and efficient segmentation remains a significant challenge due to the complexity and variability of US images. Recently, deep learning (DL) techniques, such as convolutional neural networks (CNNs) and vision transformers (ViTs), have emerged as powerful tools for computer-aided diagnosis (CAD). This review highlights recent advancements in thyroid US image segmentation, focusing on state-of-the-art DL techniques such as contrastive learning, consistency learning, and knowledge-driven DL. We explore various approaches to improve segmentation accuracy, including multi-task learning, self-supervised learning, and methods that minimize reliance on the availability of large, annotated datasets. Additionally, we examine the clinical significance of these methods in differentiating between benign and malignant nodules, as well as their potential for integration into clinically adopted, fully automated CAD systems. By addressing the latest developments and ongoing challenges, this review serves as a comprehensive reference for future research and clinical implementation of thyroid US diagnostics. Full article
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19 pages, 7932 KB  
Article
Unsupervised Domain Adaptation with Raman Spectroscopy for Rapid Autoimmune Disease Diagnosis
by Ziyang Zhang, Yang Liu, Cheng Chen, Xiaoyi Lv and Chen Chen
Sensors 2025, 25(19), 6186; https://doi.org/10.3390/s25196186 - 6 Oct 2025
Viewed by 382
Abstract
Autoimmune diseases constitute a broadly prevalent category of disorders. Conventional computer-aided diagnostic (CAD) techniques rely on large volumes of data paired with reliable annotations. However, the diverse symptomatology and diagnostic complexity of autoimmune diseases result in a scarcity of reliably labeled biological samples. [...] Read more.
Autoimmune diseases constitute a broadly prevalent category of disorders. Conventional computer-aided diagnostic (CAD) techniques rely on large volumes of data paired with reliable annotations. However, the diverse symptomatology and diagnostic complexity of autoimmune diseases result in a scarcity of reliably labeled biological samples. In this study, we propose a pseudo-label-based conditional domain adversarial network (CDAN-PL) framework by integrating Raman spectroscopy with domain adaptation technology, enabling label-free unsupervised transfer diagnosis of diseases. Compared to traditional unsupervised domain adaptation techniques, our CDAN-PL framework generates reliable pseudo-labels to ensure the robust implementation of conditional adversarial methods. Additionally, its spectral data-adaptive feature extraction techniques further solidify the model’s superiority in Raman spectroscopy-based disease diagnosis. CDAN-PL exhibits excellent performance in homologous transfer tasks, achieving an average accuracy of 92.3%—surpassing the baseline models’ 80.81% and 86.4%. Moreover, it attains an average accuracy of 90.05% in non-homologous transfer tasks, further validating its generalization capability. Full article
(This article belongs to the Collection Deep Learning in Biomedical Informatics and Healthcare)
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14 pages, 2927 KB  
Systematic Review
Real-Time Artificial Intelligence Versus Standard Colonoscopy in the Early Detection of Colorectal Cancer: A Systematic Review and Meta-Analysis
by Abdullah Sultany, Rahul Chikatimalla, Adishwar Rao, Mohamed A. Omar, Abdulkader Shaar, Hassam Ali, Fariha Hasan, Sheza Malik, Saqr Alsakarneh and Dushyant Singh Dahiya
Healthcare 2025, 13(19), 2517; https://doi.org/10.3390/healthcare13192517 - 3 Oct 2025
Viewed by 626
Abstract
Background: Colonoscopy remains the gold standard for colorectal cancer screening. Deep learning systems with real-time computer-aided polyp detection (CADe) demonstrate high accuracy in controlled research settings and preliminary randomized controlled trials (RCTs) report favorable outcomes in clinical settings. This study aims to evaluate [...] Read more.
Background: Colonoscopy remains the gold standard for colorectal cancer screening. Deep learning systems with real-time computer-aided polyp detection (CADe) demonstrate high accuracy in controlled research settings and preliminary randomized controlled trials (RCTs) report favorable outcomes in clinical settings. This study aims to evaluate the efficacy of AI-assisted colonoscopy compared to standard colonoscopy focusing on Polyp Detection Rate (PDR) and Adenoma Detection Rate (ADR), and to explore their implications for clinical practice. Methods: A systematic search was conducted using multiple indexing databases for RCTs comparing AI-assisted to standard colonoscopy. Random-effect models were utilized to calculate pooled odds ratios (ORs) with 95% confidence intervals. The risk of bias was assessed using the Cochrane Risk of Bias Tool, and heterogeneity was quantified using I statistics. Results: From 22,762 studies, 12 RCTs (n = 11,267) met the inclusion criteria. AI-assisted colonoscopy significantly improved PDR (OR 1.31, 95% CI 1.08–1.59, p = 0.005), despite heterogeneity among studies (I2 = 79%). While ADR showed improvement with AI-assisted colonoscopy (OR 1.24, 95% CI, 0.98–1.58, p = 0.08), the result was not statistically significant and had high heterogeneity (I2 = 81%). Conclusions: AI-assisted colonoscopy significantly enhances PDR, highlighting its potential role in colorectal cancer screening programs. However, while an improvement in the ADR was observed, the results were not statistically significant and showed considerable variability. These findings highlight the promise of AI in improving diagnostic accuracy but also point to the need for further research to better understand its impact on meaningful clinical outcomes. Full article
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21 pages, 2189 KB  
Article
Hybrid CNN-Swin Transformer Model to Advance the Diagnosis of Maxillary Sinus Abnormalities on CT Images Using Explainable AI
by Mohammad Alhumaid and Ayman G. Fayoumi
Computers 2025, 14(10), 419; https://doi.org/10.3390/computers14100419 - 2 Oct 2025
Viewed by 285
Abstract
Accurate diagnosis of sinusitis is essential due to its widespread prevalence and its considerable impact on patient quality of life. While multiple imaging techniques are available for detecting maxillary sinus, computed tomography (CT) remains the preferred modality because of its high sensitivity and [...] Read more.
Accurate diagnosis of sinusitis is essential due to its widespread prevalence and its considerable impact on patient quality of life. While multiple imaging techniques are available for detecting maxillary sinus, computed tomography (CT) remains the preferred modality because of its high sensitivity and spatial resolution. Although recent advances in deep learning have led to the development of automated methods for sinusitis classification, many existing models perform poorly in the presence of complex pathological features and offer limited interpretability, which hinders their integration into clinical workflows. In this study, we propose a hybrid deep learning framework that combines EfficientNetB0, a convolutional neural network, with the Swin Transformer, a vision transformer, to improve feature representation. An attention-based fusion module is used to integrate both local and global information, thereby enhancing diagnostic accuracy. To improve transparency and support clinical adoption, the model incorporates explainable artificial intelligence (XAI) techniques using Gradient-weighted Class Activation Mapping (Grad-CAM). This allows for visualization of the regions influencing the model’s predictions, helping radiologists assess the clinical relevance of the results. We evaluate the proposed method on a curated maxillary sinus CT dataset covering four diagnostic categories: Normal, Opacified, Polyposis, and Retention Cysts. The model achieves a classification accuracy of 95.83%, with precision, recall, and F1 score all at 95%. Grad-CAM visualizations indicate that the model consistently focuses on clinically significant regions of the sinus anatomy, supporting its potential utility as a reliable diagnostic aid in medical practice. Full article
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28 pages, 8295 KB  
Review
The Role of Imaging in Inflammatory Bowel Diseases: From Diagnosis to Individualized Therapy
by Salvatore Lavalle, Alessandro Vitello, Edoardo Masiello, Giuseppe Dell’Anna, Placido Romeo, Angelo Montana, Giambattista Privitera, Michele Cosenza, Domenico Santangelo, Tommaso Russo, Federico Bonomo, Emanuele Sinagra, Partha Pal, Antonio Facciorusso, Fabio Salvatore Macaluso, Ambrogio Orlando and Marcello Maida
Diagnostics 2025, 15(19), 2457; https://doi.org/10.3390/diagnostics15192457 - 26 Sep 2025
Viewed by 761
Abstract
Background: Inflammatory Bowel Disease (IBD), comprising Crohn’s disease and ulcerative colitis, requires accurate assessment over time. Imaging techniques play a crucial role in diagnosis, monitoring disease activity, and guiding therapeutic response. This review summarizes the current evidence on radiologic imaging techniques in IBD, [...] Read more.
Background: Inflammatory Bowel Disease (IBD), comprising Crohn’s disease and ulcerative colitis, requires accurate assessment over time. Imaging techniques play a crucial role in diagnosis, monitoring disease activity, and guiding therapeutic response. This review summarizes the current evidence on radiologic imaging techniques in IBD, focusing on intestinal ultrasound (IUS), computed tomography enterography (CTE), magnetic resonance enterography (MRE), and other emerging technologies. Methods: A literature review was conducted using PubMed, EMBASE, Scopus, and the Cochrane Library, encompassing publications up to 31 October 2024. Results: IUS offers a non-invasive tool for assessing bowel wall thickness, vascularity, and complications. CTE and MRE provide detailed visualization of luminal and extraluminal disease, with MRE preferred for routine monitoring due to the absence of ionizing radiation. Standardized indices and scoring systems aid in objective disease activity assessment. Emerging technologies like Positron Emission Tomography (PET)/MRI and radiomics show promise in combining metabolic and morphological information for complex cases. Conclusions: Imaging has a central role in IBD management, with IUS, CTE, and MRE demonstrating high diagnostic accuracy. Radiomics and Artificial Intelligence (AI) are paving the way for precision imaging. Integrating advanced imaging techniques, scoring systems, and AI-driven analytics represents a transformative step toward more effective and individualized care for patients with IBD. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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17 pages, 1581 KB  
Article
Curriculum Learning-Driven YOLO for Tumor Detection in Ultrasound Using Hierarchically Zoomed-In Images
by Yu Hyun Park, Hongseok Choi, Ki-Baek Lee and Hyungsuk Kim
Appl. Sci. 2025, 15(19), 10337; https://doi.org/10.3390/app151910337 - 23 Sep 2025
Viewed by 423
Abstract
Ultrasound imaging is widely employed for breast cancer detection; however, its diagnostic reliability is often constrained by operator dependence and subjective interpretation. Deep learning-based computer-aided diagnosis (CADx) systems offer potential to improve diagnostic consistency, yet their effectiveness is frequently limited by the scarcity [...] Read more.
Ultrasound imaging is widely employed for breast cancer detection; however, its diagnostic reliability is often constrained by operator dependence and subjective interpretation. Deep learning-based computer-aided diagnosis (CADx) systems offer potential to improve diagnostic consistency, yet their effectiveness is frequently limited by the scarcity of annotated medical images. This work introduces a training framework to enhance the performance and training stability of a YOLO-based object detection model for breast tumor localization, particularly in data-constrained scenarios. The proposed method integrates a detail-to-context curriculum learning scheme using hierarchically zoomed-in B-mode images, with progression difficulty determined by the tumor-to-background area ratio. A preprocessing step resizes all images to 640 × 640 pixels while preserving aspect ratio to improve intra-dataset consistency. Our evaluation indicates that aspect ratio-preserving resizing is associated with a 2.3% increase in recall and a reduction in the standard deviation of stability metrics by more than 20%. Moreover, the curriculum learning approach reached 97.2% of the final model performance using only 35% of the training data required by conventional methods, while achieving a more balanced precision–recall profile. These findings suggest that the proposed framework holds potential as an effective strategy for developing more robust and efficient tumor detection models, particularly for deployment in resource-limited clinical environments. Full article
(This article belongs to the Special Issue Current Updates on Ultrasound for Biomedical Applications)
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20 pages, 4700 KB  
Article
Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images
by Natalia Kozłowska, Marta Borowska, Tomasz Jasiński, Małgorzata Wierzbicka and Małgorzata Domino
Animals 2025, 15(18), 2758; https://doi.org/10.3390/ani15182758 - 22 Sep 2025
Viewed by 410
Abstract
In human medicine, computer-aided diagnosis (CAD) is increasingly employed for screening, identifying, and monitoring early endoscopic signs of various diseases. However, its potential—despite proven benefits in human healthcare—remains largely underexplored in equine veterinary medicine. This study aimed to quantify endoscopic signs of pharyngeal [...] Read more.
In human medicine, computer-aided diagnosis (CAD) is increasingly employed for screening, identifying, and monitoring early endoscopic signs of various diseases. However, its potential—despite proven benefits in human healthcare—remains largely underexplored in equine veterinary medicine. This study aimed to quantify endoscopic signs of pharyngeal lymphoid hyperplasia (PLH) as digital data and to assess their effectiveness in CAD of PLH in comparison and in combination with clinical data reflecting respiratory tract disease. Endoscopic images of the pharynx were collected from 70 horses clinically assessed as either healthy or affected by PLH. Digital data were extracted using an object detection-based processing technique and first-order statistics (FOS). The data were transformed using linear discriminant analysis (LDA) and classified with the random forest (RF) algorithm. Classification metrics were then calculated. When considering digital and clinical data, high classification performance was achieved (0.76 accuracy, 0.83 precision, 0.78 recall, and 0.76 F1 score), with the highest importance assigned to selected FOS features: Number of Objects and Neighbors, and Tracheal Auscultation. The proposed protocol of digitizing standard respiratory tract diagnostic methods provides effective discrimination of PLH grades, supporting the clinical value of CAD in veterinary medicine and paving the way for further research in digital medical diagnostics. Full article
(This article belongs to the Special Issue Animal–Computer Interaction: New Horizons in Animal Welfare)
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13 pages, 3028 KB  
Article
Structural Brain Abnormalities, Diagnostic Approaches, and Treatment Strategies in Vertigo: A Case-Control Study
by Klaudia Széphelyi, Szilvia Kóra, Gergely Orsi and József Tollár
Neurol. Int. 2025, 17(9), 146; https://doi.org/10.3390/neurolint17090146 - 10 Sep 2025
Viewed by 679
Abstract
Background/Objectives: Dizziness is a frequent medical complaint with neurological, otolaryngological, and psychological origins. Imaging studies such as CT (Computer Tomography), cervical X-rays, and ultrasound aid diagnosis, while MRI (Magnetic Resonance Imaging) is crucial for detecting brain abnormalities. Our purpose is to identify structural [...] Read more.
Background/Objectives: Dizziness is a frequent medical complaint with neurological, otolaryngological, and psychological origins. Imaging studies such as CT (Computer Tomography), cervical X-rays, and ultrasound aid diagnosis, while MRI (Magnetic Resonance Imaging) is crucial for detecting brain abnormalities. Our purpose is to identify structural brain changes associated with vertigo, assess pre-MRI diagnostic approaches, and evaluate treatment strategies. Methods: A case-control study of 232 vertigo patients and 232 controls analyzed MRI findings, pre-MRI examinations, symptoms, and treatments. Statistical comparisons were performed using chi-square and t-tests (p < 0.05). Results: White matter lesions, lacunar infarcts, Circle of Willis variations, and sinusitis were significantly more frequent in vertigo patients (p < 0.05). Pre-MRI diagnostics frequently identified atherosclerosis (ultrasound) and spondylosis (X-ray). Common symptoms included headache, imbalance, and visual disturbances. The most frequent post-MRI diagnosis was Benign Paroxysmal Positional Vertigo (BPPV). Treatments included lifestyle modifications, physical therapy (e.g., Epley maneuver), and pharmacological therapies such as betahistine. Conclusions: MRI revealed structural brain changes linked to vertigo. Pre-MRI assessments are essential for ruling out vascular and musculoskeletal causes. A multidisciplinary treatment approach is recommended. Trial Registration: This study was registered in ClinicalTrials.gov with the trial registration number NCT06848712 on 22 February 2025. Full article
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14 pages, 954 KB  
Article
A YOLO Ensemble Framework for Detection of Barrett’s Esophagus Lesions in Endoscopic Images
by Wan-Chih Lin, Chi-Chih Wang, Ming-Chang Tsai, Chao-Yen Huang, Chun-Che Lin and Ming-Hseng Tseng
Diagnostics 2025, 15(18), 2290; https://doi.org/10.3390/diagnostics15182290 - 10 Sep 2025
Viewed by 486
Abstract
Background and Objectives: Barrett’s Esophagus (BE) is a precursor to esophageal adenocarcinoma, and early detection is essential to reduce cancer risk. This study aims to develop a YOLO-based ensemble framework to improve the automated detection of BE-associated mucosal lesions on endoscopic images. [...] Read more.
Background and Objectives: Barrett’s Esophagus (BE) is a precursor to esophageal adenocarcinoma, and early detection is essential to reduce cancer risk. This study aims to develop a YOLO-based ensemble framework to improve the automated detection of BE-associated mucosal lesions on endoscopic images. Methods: A dataset of 3620 annotated endoscopic images was collected from 132 patients. Five YOLO variants, YOLOv5, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, were selected based on their architectural diversity and detection capabilities. Each model was trained individually, and their outputs were integrated using a Non-Maximum Suppression (NMS)-based ensemble strategy. Multiple ensemble configurations were evaluated to assess the impact of fusion depth on detection performance. Results: The ensemble models consistently outperformed individual YOLO variants in recall, the primary evaluation metric. The entire five-model ensemble achieved the highest recall (0.974), significantly reducing missed lesion detections. Statistical analysis using McNemar’s test and bootstrap confidence intervals confirmed the superiority in most comparisons. Conclusions: The proposed YOLO ensemble framework demonstrates enhanced sensitivity and robustness in detecting BE lesions. Its integration into clinical workflows can improve early diagnosis and reduce diagnostic workload, offering a promising tool for computer-aided screening in gastroenterology. Full article
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30 pages, 3045 KB  
Article
A Retrospective Study of CBCT-Based Detection of Endodontic Failures and Periapical Lesions in a Romanian Cohort
by Oana Andreea Diaconu, Lelia Mihaela Gheorghiță, Anca Gabriela Gheorghe, Mihaela Jana Țuculină, Maria Cristina Munteanu, Cătălina Alexandra Iacov, Virginia Maria Rădulescu, Mihaela Ionescu, Adina Andreea Mirea and Carina Alexandra Bănică
J. Clin. Med. 2025, 14(18), 6364; https://doi.org/10.3390/jcm14186364 - 9 Sep 2025
Viewed by 791
Abstract
Background and Objectives: Cone Beam Computed Tomography (CBCT) offers high-resolution, three-dimensional imaging for detecting apical periodontitis (AP) and evaluating the technical quality of endodontic treatments. This study aimed to investigate the diagnostic value of CBCT in identifying endodontic failures and periapical lesions [...] Read more.
Background and Objectives: Cone Beam Computed Tomography (CBCT) offers high-resolution, three-dimensional imaging for detecting apical periodontitis (AP) and evaluating the technical quality of endodontic treatments. This study aimed to investigate the diagnostic value of CBCT in identifying endodontic failures and periapical lesions and to explore the clinical patterns associated with these findings in a Romanian patient cohort. Materials and Methods: A retrospective study was conducted on 258 patients (with 876 root canal-treated teeth), all of whom underwent CBCT imaging between October 2024 and April 2025 at a private radiology center in Craiova, Romania. Of the 876 treated teeth, 409 were diagnosed with apical periodontitis. Patients were present for endodontic treatment at the Endodontics Clinic of the Faculty of Dentistry, University of Medicine and Pharmacy of Craiova. With the patients’ consent, 3D radiological examinations were recommended for better case planning and accurate diagnosis. The periapical status and technical parameters of root canal fillings were assessed using the CBCT-PAI index and evaluated by three calibrated observers. Associations with demographic, clinical, and behavioral factors were statistically analyzed. Results: Apical periodontitis was detected in 46.69% of the teeth examined during the study period, with CBCT-PAI score 3 being the most prevalent. Poor root canal obturation quality (underfilling, overfilling, and voids) was significantly associated with periapical pathology. Chronic lesions were more common than acute ones, especially in older patients. The number of teeth with endodontic treatments and no AP, as well as the number of teeth with AP, was significantly lower for patients with acute AP, indicating the more severe impact of chronic AP on the patients’ oral health status. CBCT allowed the precise localization of missed canals and assessment of lesion severity. Conclusions: Within the limits of a retrospective, referral-based cohort, CBCT aided the detection of periapical pathology in root canal-treated teeth (46.69%). These findings do not represent population-based rates but support the selective use of CBCT, in line with current ESE guidance, for complex cases or when conventional imaging is inconclusive. Full article
(This article belongs to the Special Issue Oral Health in Children: Clinical Management)
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11 pages, 4595 KB  
Article
Computed Tomography of Neoplastic Infiltrating Renal Masses in Patients Without a Previous History of Cancer
by Carlos Nicolau, Andreu Ivars, Carmen Sebastia, Clara Bassaganyas, María Fresno, Leonardo Rodríguez, Josep Puig, Marc Comas-Cufí and Blanca Paño
Cancers 2025, 17(17), 2936; https://doi.org/10.3390/cancers17172936 - 8 Sep 2025
Viewed by 589
Abstract
Background/Objectives: Infiltrative renal masses, characterized by ill-defined margins and parenchymal invasion without forming a discrete mass, present a diagnostic challenge, particularly in patients without a prior history of malignancy. Differentiating among the most common malignant etiologies—renal cell carcinoma (RCC), urothelial carcinoma (UC), and [...] Read more.
Background/Objectives: Infiltrative renal masses, characterized by ill-defined margins and parenchymal invasion without forming a discrete mass, present a diagnostic challenge, particularly in patients without a prior history of malignancy. Differentiating among the most common malignant etiologies—renal cell carcinoma (RCC), urothelial carcinoma (UC), and lymphoma—is essential for guiding appropriate treatment. This study aimed to evaluate whether specific computed tomography (CT) features can assist in the differential diagnosis of these lesions. Methods: A retrospective review was conducted on 68 patients with infiltrative renal masses presented at a tertiary hospital’s oncologic urology committee between 2018 and 2022. Patients with prior malignancy or signs of infection were excluded. All cases underwent contrast-enhanced CT within three months of diagnosis and had histopathological confirmation. Imaging features such as necrosis, collecting system involvement, lymphadenopathy, and others were assessed and statistically analyzed. Results: RCC was the most frequent diagnosis (68%), followed by UC (18%) and lymphoma (7.4%). Significant differences were observed in imaging features: necrosis was more common in RCC (87%) than in UC (25%) and lymphoma (20%), p < 0.001; collecting system involvement was universal in UC (100%) and less common in RCC (65%) and lymphoma (40%), p = 0.009; and lymphadenopathy was more frequent in lymphoma (80%) than in UC (67%) and RCC (35%), p = 0.038. Tumor size also varied significantly, with lymphomas presenting the largest median size (11 cm), followed by RCCs (8.2 cm) and UCs (5 cm), p < 0.001. Conclusions: CT imaging features, particularly necrosis, collecting system involvement, and lymphadenopathy, can aid in distinguishing among RCC, UC, and lymphoma in patients with infiltrative renal masses and no prior cancer history. These findings may support more accurate diagnoses and inform tailored therapeutic strategies. Full article
(This article belongs to the Section Methods and Technologies Development)
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31 pages, 8445 KB  
Article
HIRD-Net: An Explainable CNN-Based Framework with Attention Mechanism for Diabetic Retinopathy Diagnosis Using CLAHE-D-DoG Enhanced Fundus Images
by Muhammad Hassaan Ashraf, Muhammad Nabeel Mehmood, Musharif Ahmed, Dildar Hussain, Jawad Khan, Younhyun Jung, Mohammed Zakariah and Deema Mohammed AlSekait
Life 2025, 15(9), 1411; https://doi.org/10.3390/life15091411 - 8 Sep 2025
Viewed by 886
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, underscoring the need for accurate and early diagnosis to prevent disease progression. Although fundus imaging serves as a cornerstone of Computer-Aided Diagnosis (CAD) systems, several challenges persist, including lesion scale variability, blurry [...] Read more.
Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, underscoring the need for accurate and early diagnosis to prevent disease progression. Although fundus imaging serves as a cornerstone of Computer-Aided Diagnosis (CAD) systems, several challenges persist, including lesion scale variability, blurry morphological patterns, inter-class imbalance, limited labeled datasets, and computational inefficiencies. To address these issues, this study proposes an end-to-end diagnostic framework that integrates an enhanced preprocessing pipeline with a novel deep learning architecture, Hierarchical-Inception-Residual-Dense Network (HIRD-Net). The preprocessing stage combines Contrast Limited Adaptive Histogram Equalization (CLAHE) with Dilated Difference of Gaussian (D-DoG) filtering to improve image contrast and highlight fine-grained retinal structures. HIRD-Net features a hierarchical feature fusion stem alongside multiscale, multilevel inception-residual-dense blocks for robust representation learning. The Squeeze-and-Excitation Channel Attention (SECA) is introduced before each Global Average Pooling (GAP) layer to refine the Feature Maps (FMs). It further incorporates four GAP layers for multi-scale semantic aggregation, employs the Hard-Swish activation to enhance gradient flow, and utilizes the Focal Loss function to mitigate class imbalance issues. Experimental results on the IDRiD-APTOS2019, DDR, and EyePACS datasets demonstrate that the proposed framework achieves 93.46%, 82.45% and 79.94% overall classification accuracy using only 4.8 million parameters, highlighting its strong generalization capability and computational efficiency. Furthermore, to ensure transparent predictions, an Explainable AI (XAI) approach known as Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize HIRD-Net’s decision-making process. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Disease Prediction and Prevention)
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16 pages, 715 KB  
Systematic Review
Artificial Intelligence in Computed Tomography Radiology: A Systematic Review on Risk Reduction Potential
by Sandra Coelho, Aléxia Fernandes, Marco Freitas and Ricardo J. Fernandes
Appl. Sci. 2025, 15(17), 9659; https://doi.org/10.3390/app15179659 - 2 Sep 2025
Viewed by 1114
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the [...] Read more.
Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the current literature on AI applications in computed tomography (CT) radiology and their contributions to risk reduction. Following the PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus and Web of Science for studies published between 2021 and 2025 (the databases were last accessed on 15 April 2025). Thirty-four studies were included based on their relevance to AI in radiology and reported outcomes. Extracted data included study type, geographic region, AI application and type, role in clinical workflow, use cases, sensitivity and specificity. The majority of studies addressed triage (61.8%) and computer-aided detection (32.4%). AI was most frequently applied in chest imaging (47.1%) and brain haemorrhage detection (29.4%). The mean reported sensitivity was 89.0% and specificity was 93.3%. AI tools demonstrated advantages in image interpretation, automated patient positioning, prioritisation and measurement standardisation. Reported benefits included reduced cognitive workload, improved triage efficiency, decreased manual annotation and shorter exposure times. AI systems in CT radiology show strong potential to enhance diagnostic consistency and reduce occupational risks. The evidence supports the integration of AI-based tools to assist diagnosis, lower human workload and improve overall safety in radiology departments. Full article
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