Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition

Accurate analysis of medical videos remains a major challenge in deep learning (DL) due to the need for effective spatiotemporal feature mapping that captures both spatial detail and temporal dynamics. Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. To bridge this gap, a novel DL-based framework is proposed for spatiotemporal feature extraction from medical video sequences. As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. To address data imbalance, 60 matched pairs of videos are randomly selected across 20 experimental runs. Videos are resized to 224 × 224, and the 3D CNN captures spatiotemporal information. A 3D version of the parallel spatial and channel squeeze-and-excitation (P-scSE) is implemented, and a new block called the residual with parallel attention (RPA) block is proposed by combining P-scSE3D with a residual block. To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. The model achieves an average accuracy of 0.933, precision of 0.932, recall of 0.944, F1-score of 0.935, and AUC of 0.933. It is also observed that the integration of P-scSE3D increased the F1-score by 7%. This preliminary work opens avenues for exploring various GI endoscopic video-based prospective studies. Full article

Ultrasound attenuation imaging (ATI) is a non-invasive method for quantifying hepatic steatosis, offering advantages over the hepatorenal index (HRI). However, its reliability can be influenced by factors such as measurement depth, ROI size, and subcutaneous fat. This paper examines the impact of these confounders on ATI measurements and discusses diagnostic considerations. In this study, 33 healthy adults underwent liver ultrasound with ATI and HRI protocols. ATI measurements were taken at depths of 2–5 cm below the liver capsule using small and large ROIs. Two operators performed the measurements, and inter-operator variability was assessed. Subcutaneous fat thickness was measured to evaluate its influence on attenuation values. The ATI measurements showed a consistent decrease in attenuation coefficient values with increasing depth, approximately 0.05 dB/cm/MHz. Larger ROI sizes increased measurement variability due to greater anatomical heterogeneity. HRI values correlated weakly with ATI and were influenced by operator technique and subcutaneous fat, the latter accounting for roughly 2.5% of variability. ATI provides a quantitative assessment of hepatic steatosis compared to HRI, although its accuracy can vary depending on the depth and ROI selection. Standardised imaging protocols and AI tools may improve reproducibility and clinical utility, supporting advancements in ultrasound-based liver diagnostics for better patient care. Full article

Transverse fetal head position during labor is associated with increased rates of operative deliveries and cesarean sections. Traditional assessment methods rely on digital examination, which can be inaccurate in cases of prolonged labor. Intrapartum ultrasound offers improved diagnostic capabilities, but standardized interpretation frameworks are needed. This study aimed to evaluate the significance of appropriate assessment and management of transverse fetal head position during labor, with particular emphasis on the correlation between geometric parameters and delivery outcomes. Additionally, the investigation analyzed the potential role of Artificial Intelligence Dystocia Algorithm (AIDA) as an innovative decision support system in standardizing diagnostic approaches and optimizing clinical decision-making in cases of fetal malposition. This investigation was conducted as a focused secondary analysis of data originally collected for the development and validation of the Artificial Intelligence Dystocia Algorithm (AIDA). The study examined 66 cases of transverse fetal head position from a cohort of 135 nulliparous women with prolonged second-stage labor across three Italian hospitals. Cases were stratified by Midline Angle (MLA) measurements into classic transverse (≥75°), near-transverse (70–74°), and transitional (60–69°) positions. Four geometric parameters (Angle of Progression, Head–Symphysis Distance, Midline Angle, and Asynclitism Degree) were evaluated using the AIDA classification system. The predictive capabilities of three machine learning algorithms (Support Vector Machine, Random Forest, and Multilayer Perceptron) were assessed, and delivery outcomes were analyzed. The AIDA system successfully categorized labor dystocia into five distinct classes, with strong predictive value for delivery outcomes. A clear gradient of cesarean delivery risk was observed across the spectrum of transverse positions (100%, 93.1%, and 85.7% for near-transverse, classic transverse, and transitional positions, respectively). All cases classified as AIDA Class 4 required cesarean delivery regardless of the specific MLA value. Machine learning algorithms demonstrated high predictive accuracy, with Random Forest achieving 95.5% overall accuracy across the study cohort. The presence of concurrent asynclitism with transverse position was associated with particularly high rates of cesarean delivery. Among the seven cases that achieved vaginal delivery despite transverse positioning, none belonged to the classic transverse positions group, and five (71.4%) exhibited at least one parameter classified as favorable. The integration of artificial intelligence through AIDA as a decision support system, combined with intrapartum ultrasound, offered a promising approach for objective assessment and management of transverse fetal head position. The AIDA classification system’s integration of multiple geometric parameters, with particular emphasis on precise Midline Angle (MLA) measurement in degrees, provided superior predictive capability for delivery outcomes compared to qualitative position assessment alone. This multidimensional approach enabled more personalized and evidence-based management of malpositions during labor, potentially reducing unnecessary interventions while identifying cases where expectant management might be futile. Further prospective studies are needed to validate the predictive capability of this decision support system and its impact on clinical decision-making in real-time labor management. Full article

Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metrics and qualitative evaluation. Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. Data were acquired using standard clinical gamma cameras at the Nuclear Medicine Department of the University General Hospital of Alexandroupolis. The ECAE architecture integrates multiscale feature extraction, channel attention mechanisms, and efficient residual blocks to reconstruct high-quality images from low-dose inputs. The model was trained and validated using quantitative metrics—Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)—alongside qualitative assessments by nuclear medicine experts. Results: The model achieved significant improvements in both PSNR and SSIM across all tested dose levels, particularly between 30% and 70% of the full dose. Expert evaluation confirmed enhanced visibility of anatomical structures, noise reduction, and preservation of diagnostic detail in denoised images. In blinded evaluations, denoised images were preferred over the original full-dose scans in 66% of all cases, and in 61% of cases within the 30–70% dose range. Conclusion: The proposed ECAE model effectively reconstructs high-quality bone scintigraphy images from substantially reduced-dose acquisitions. This approach supports dose reduction in nuclear medicine imaging while maintaining—or even enhancing—diagnostic confidence, offering practical benefits in patient safety, workflow efficiency, and environmental impact. Full article

Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, a framework that combines anatomically constrained data augmentation with expert-guided annotation refinement. NCT-CXR applies carefully calibrated discrete-angle rotations (±5°, ±10°) and intensity-based augmentations to enrich training data while preserving spatial and anatomical integrity. To address label noise in the NIH Chest X-ray dataset, we further introduce a clinically validated annotation refinement pipeline using the OncoDocAI platform, resulting in multi-label pixel-level segmentation masks for nine thoracic conditions. YOLOv8 was selected as the segmentation backbone due to its architectural efficiency, speed, and high spatial accuracy. Experimental results show that NCT-CXR significantly improves segmentation precision, especially for pneumothorax (0.829 and 0.804 for ±5° and ±10°, respectively). Non-parametric statistical testing (Kruskal–Wallis, H = 14.874, p = 0.0019) and post hoc Nemenyi analysis (p = 0.0138 and p = 0.0056) confirm the superiority of discrete-angle augmentation over mixed strategies. These findings underscore the importance of clinically constrained augmentation and high-quality annotation in building robust segmentation models. NCT-CXR offers a practical, high-performance solution for integrating deep learning into radiological workflows. Full article

Soft tissue masses are predominantly benign, with a benign-to-malignant ratio exceeding 100:1, often located around joints. They may be contiguous or adjacent to joints or reflect systemic diseases or distant organ involvement. Clinically, they typically present as palpable swellings. Evaluation should consider duration, size, depth, and mobility. Also assess consistency, growth rate, symptoms, and history of trauma, infection, or malignancy. Laboratory tests are generally of limited diagnostic value. The primary clinical goal is to avoid unnecessary investigations or procedures for benign lesions while ensuring timely diagnosis and treatment of malignant ones. Imaging plays a central role: it confirms the presence of the lesion, assesses its location, size, and composition, differentiates between cystic and solid or benign and malignant features, and can sometimes provide a definitive diagnosis. Imaging is also crucial for biopsy planning, treatment strategy, identification of involved structures, and follow-up. Ultrasound (US) is the first-line imaging modality for palpable soft tissue masses due to its low cost, wide availability, and lack of ionizing radiation. If findings are inconclusive, magnetic resonance imaging (MRI) or computed tomography (CT) is recommended. This review aims to discuss the most common causes of periarticular soft tissue masses in the appendicular skeleton, focusing on clinical presentation and radiologic features. Full article

Atrial fibrillation (AF) is the most frequently observed type of arrhythmia among adults, and its absolute prevalence is steadily rising in close association with the aging of the population, with its prevalence varying from 2% in the general population to 10–12% among the elderly. The relatively new concepts of ‘atrial cardiomyopathy’ and “AF-related atrial cardiomyopathy”, along with the growing body of knowledge regarding remodeling, function, and tissue characterization, highlight the need for novel approaches to the diagnostic process as well as in the therapeutic guidance and monitoring of atrial arrhythmias. Advanced imaging techniques, particularly cardiac magnetic resonance (CMR) imaging, have emerged as pivotal in the detailed assessment of atrial structure and function. CMR facilitates the precise measurement of left atrial volume and morphology, which are critical predictors of AF recurrence post-intervention. Furthermore, it enables the evaluation of atrial fibrosis using late gadolinium enhancement (LGE), offering a non-invasive method to assess the severity and distribution of fibrotic tissue. The possibility of an accurate CMR pulmonary vein anatomy mapping enhances the precision of pulmonary vein isolation procedures, potentially improving outcomes in AF management. This review underlines the integration of novel diagnostic tools in enhancing the understanding and management of AF, advocating for a shift towards more personalized and effective therapeutic programs. Full article