Medical Imagings are considered one of the crucial diagnostic tools for different bones-related diseases, especially bones fractures. This paper investigates the robustness of pre-trained deep learning models for classifying bone fractures in X-ray images and seeks to address global healthcare disparity through the lens of technology. Three deep learning models have been tested under varying simulated equipment quality conditions. ResNet50, VGG16 and EfficientNetv2 are the three pre-trained architectures which are compared. These models were used to perform bone fracture classification as images were progressively degraded using noise. This paper specifically empirically studies how the noise can affect the bone fractures detection and how the pre-trained models performance can be changes due to the noise that affect the quality of the X-ray images. This paper aims to help replicate real world challenges experienced by medical imaging technicians across the world. Thus, this paper establishes a methodological framework for assessing AI model degradation using transfer learning and controlled noise augmentation. The findings provide practical insight into how robust and generalizable different pre-trained deep learning powered computer vision models can be when used in different contexts.

Cognitive impairment is a key contributor to disability and poor outcomes in schizophrenia, yet it is not adequately addressed by currently available treatments. Thus, it is important to search for preventable or treatable risk factors for cognitive impairment. Here, we hypothesized that obesity-related neurostructural alterations will be associated with worse cognitive outcomes in people with first episode of psychosis (FEP). This observational study presents cross-sectional data from the Early-Stage Schizophrenia Outcome project. We acquired T1-weighted 3D MRI scans in 440 participants with FEP at the time of the first hospitalization and in 257 controls. Metabolic assessments included body mass index (BMI), waist-to-hip ratio (WHR), serum concentrations of triglycerides, cholesterol, glucose, insulin, and hs-CRP. We chose machine learning-derived brain age gap estimate (BrainAGE) as our measure of neurostructural changes and assessed attention, working memory and verbal learning using Digit Span and the Auditory Verbal Learning Test. Among obesity/metabolic markers, only WHR significantly predicted both, higher BrainAGE (t(281)=2.53, p=0.012) and worse verbal learning (t(290) = -2.51, P = .026). The association between FEP and verbal learning was partially mediated by BrainAGE (average causal mediated effects, ACME = -0.04 [-0.10, -0.01], P = .022) and the higher BrainAGE in FEP was partially mediated by higher WHR (ACME = 0.08 [0.02, 0.15], P = .006). Central obesity-related brain alterations were linked with worse cognitive performance already early in the course of psychosis. These structure-function links suggest that preventing or treating central obesity could target brain and cognitive impairments in FEP.

The increasing incidence of thyroid nodules (TN) raises concerns about overdiagnosis and overtreatment. This study evaluates the clinical and economic impact of KOIOS, an FDA-approved artificial intelligence (AI) tool for the management of TN. A retrospective analysis was conducted on 176 patients who underwent thyroid surgery between May 2022 and November 2024. Ultrasound images were evaluated independently by an expert and novice operators using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), while KOIOS provided AI-adapted risk stratification. Sensitivity, specificity, and Receiver-Operating Curve (ROC) analysis were performed. The incremental cost-effectiveness ratio (ICER) was defined based on the number of optimal care interventions (FNAB and thyroid surgery). Both deterministic and probabilistic sensitivity analyses were conducted to evaluate model robustness. KOIOS AI demonstrated similar diagnostic performance to the expert operator (AUC: 0.794, 95% CI: 0.718-0.871 vs. 0.784, 95% CI: 0.706-0.861; p = 0.754) and significantly outperformed the novice operator (AUC: 0.619, 95% CI: 0.526-0.711; p < 0.001). ICER analysis estimated the cost per additional optimal care decision at -€8,085.56, indicating KOIOS as a dominant and cost-saving strategy when considering a third-party payer perspective over a one-year horizon. Deterministic sensitivity analysis identified surgical costs as the main drivers of variability, while probabilistic analysis consistently favored KOIOS as the optimal strategy. KOIOS AI is a cost-effective alternative, particularly in reducing overdiagnosis and overtreatment for benign TNs. Prospective, real-life studies are needed to validate these findings and explore long-term implications.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with complicated structural and functional impairments. For decades now, chest computed tomography (CT) has been used to quantify various abnormalities related to COPD. More recently, with the newer data-driven approaches, biomarker development and validation have evolved rapidly. Studies now target multiple anatomical structures including lung parenchyma, the airways, the vasculature, and the fissures to better characterize COPD. This review explores the evolution of chest CT biomarkers in COPD, beginning with traditional thresholding approaches that quantify emphysema and airway dimensions. We then highlight some of the texture analysis efforts that have been made over the years for subtyping lung tissue. We also discuss image registration-based biomarkers that have enabled spatially-aware mechanisms for understanding local abnormalities within the lungs. More recently, deep learning has enabled automated biomarker extraction, offering improved precision in phenotype characterization and outcome prediction. We highlight the most recent of these approaches as well. Despite these advancements, several challenges remain in terms of dataset heterogeneity, model generalizability, and clinical interpretability. This review lastly provides a structured overview of these limitations and highlights future potential of CT biomarkers in personalized COPD management.

Disability progression despite disease-modifying therapy remains a major challenge in multiple sclerosis (MS). Artificial intelligence (AI) models exploiting magnetic resonance imaging (MRI) promise personalized prognostication, yet their real-world accuracy is uncertain. To systematically review and meta-analyze MRI-based AI studies predicting future disability progression in MS. Five databases were searched from inception to 17 May 2025 following PRISMA. Eligible studies used MRI in an AI model to forecast changes in the Expanded Disability Status Scale (EDSS) or equivalent metrics. Two reviewers conducted study selection, data extraction, and QUADAS-2 assessment. Random-effects meta-analysis was applied when ≥3 studies reported compatible regression statistics. Twenty-one studies with 12,252 MS patients met inclusion criteria. Five used regression on continuous EDSS, fourteen classification, one time-to-event, and one both. Conventional machine learning predominated (57%), and deep learning (38%). Median classification area under the curve (AUC) was 0.78 (range 0.57-0.86); median regression root-mean-square-error (RMSE) 1.08 EDSS points. Pooled RMSE across regression studies was 1.31 (95% CI 1.02-1.60; I² = 95%). Deep learning conferred only marginal, non-significant gains over classical algorithms. External validation appeared in six studies; calibration, decision-curve analysis and code releases were seldom reported. QUADAS-2 indicated generally low patient-selection bias but frequent index-test concerns. MRI-driven AI models predict MS disability progression with moderate accuracy, but error margins that exceed one EDSS point limit individual-level utility. Harmonized endpoints, larger multicenter cohorts, rigorous external validation, and prospective clinician-in-the-loop trials are essential before routine clinical adoption.

Neuroimaging plays a crucial role in diagnosing multiple system atrophy and monitoring progressive neurodegeneration in this fatal disease. Advanced MRI techniques and post-processing methods have demonstrated significant volume loss and microstructural changes in brain regions well known to be affected by MSA pathology. These observations can be exploited to support the differential diagnosis of MSA distinguishing it from Parkinson's disease and progressive supranuclear palsy with high sensitivity and specificity. Longitudinal studies reveal aggressive neurodegeneration in MSA, with notable atrophy rates in the cerebellum, pons, and putamen. Radiotracer imaging using PET and SPECT has shown characteristic disease-related patterns, aiding in differential diagnosis and tracking disease progression. Future research should focus on early diagnosis, particularly in prodromal stages, and the development of reliable biomarkers for clinical trials. Combining different neuroimaging modalities and machine learning algorithms can enhance diagnostic precision and provide a comprehensive understanding of MSA pathology.

This study aimed to develop and evaluate eight machine learning models based on multimodal ultrasound to precisely predict of diabetic tibial neuropathy (DTN) in patients. Additionally, the SHapley Additive exPlanations(SHAP)framework was introduced to quantify the importance of each feature variable, providing a precise and noninvasive assessment tool for DTN patients, optimizing clinical management strategies, and enhancing patient prognosis. A prospective analysis was conducted using multimodal ultrasound and clinical data from 255 suspected DTN patients who visited the Second Affiliated Hospital of Fujian Medical University between January 2024 and November 2024. Key features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Predictive models were constructed using Extreme Gradient Boosting (XGB), Logistic Regression, Support Vector Machines, k-Nearest Neighbors, Random Forest, Decision Tree, Naïve Bayes, and Neural Network. The SHAP method was employed to refine model interpretability. Furthermore, in order to verify the generalization degree of the model, this study also collected 135 patients from three other tertiary hospitals for external test. LASSO regression identified Echo intensity(EI), Cross-sectional area (CSA), Mean elasticity value(Emean), Superb microvascular imaging(SMI), and History of smoking were key features for DTN prediction. The XGB model achieved an Area Under the Curve (AUC) of 0.94, 0.83 and 0.79 in the training, internal test and external test sets, respectively. SHAP analysis highlighted the ranking significance of EI, CSA, Emean, SMI, and History of smoking. Personalized prediction explanations provided by theSHAP values demonstrated the contribution of each feature to the final prediction, and enhancing model interpretability. Furthermore, decision plots depicted how different features influenced mispredictions, thereby facilitating further model optimization or feature adjustment. This study proposed a DTN prediction model based on machine-learning algorithms applied to multimodal ultrasound data. The results indicated the superior performance of the XGB model and its interpretability was enhanced using SHAP analysis. This cost-effective and user-friendly approach provides potential support for personalized treatment and precision medicine for DTN.

To develop an artificial intelligence (AI)-driven model for automatic Lenke classification of adolescent idiopathic scoliosis (AIS) and assess its performance. This retrospective study utilized 860 spinal radiographs from 215 AIS patients with four views, including 161 training sets and 54 testing sets. Additionally, 1220 spinal radiographs from 610 patients with only anterior-posterior (AP) and lateral (LAT) views were collected for training. The model was designed to perform keypoint detection, pedicle segmentation, and AIS classification based on a custom classification strategy. Its performance was evaluated against the gold standard using metrics such as mean absolute difference (MAD), intraclass correlation coefficient (ICC), Bland-Altman plots, Cohen's Kappa, and the confusion matrix. In comparison to the gold standard, the MAD for all predicted angles was 2.29°, with an excellent ICC. Bland-Altman analysis revealed minimal differences between the methods. For Lenke classification, the model exhibited exceptional consistency in curve type, lumbar modifier, and thoracic sagittal profile, with average Kappa values of 0.866, 0.845, and 0.827, respectively, and corresponding accuracy rates of 87.07%, 92.59%, and 92.59%. Subgroup analysis further confirmed the model's high consistency, with Kappa values ranging from 0.635 to 0.930, 0.672 to 0.926, and 0.815 to 0.847, and accuracy rates between 90.7 and 98.1%, 92.6-98.3%, and 92.6-98.1%, respectively. This novel AI system facilitates the rapid and accurate automatic Lenke classification, offering potential assistance to spinal surgeons.

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation therapy, targeted biological therapy, immunotherapy) need better tumor characterization and prognostic information along with the accurate anatomy. Magnetic resonance imaging (MRI) has become the gold standard in head and neck cancer evaluation not only for staging but also for assessing tumor response, posttreatment status and complications, as well as for finding residual or recurrent tumor. Multiparametric anatomical and functional MRI (MP-MRI) is a true cancer imaging biomarker providing, in addition to high resolution tumor anatomy, more molecular and functional, qualitative and quantitative data using diffusion- weighted MRI (DW-MRI) and perfusion-dynamic contrast enhanced MRI (P-DCE-MRI), can improve the assessment of biological target volume and determine treatment response. DW-MRI provides information at the cellular level about the cell density and the integrity of the plasma membrane, based on water movement. P-DCE-MRI provides useful hemodynamic information about tissue vascularity and vascular permeability. Recent studies have shown promising results using radiomics features, MP-MRI has opened new perspectives in oncologic imaging with better realization of the latest technological advances with the help of artificial intelligence.

The rapid and accurate detection of COVID-19 (coronavirus disease 2019) from normal and pneumonia chest x-ray images is essential for timely diagnosis and treatment. The overlapping features in radiology images make it challenging for radiologists to distinguish COVID-19 cases. This research study investigates the effectiveness of combining local binary pattern (LBP) and histogram of oriented gradients (HOG) features with machine learning algorithms to differentiate COVID-19 from normal and pneumonia cases using chest x-rays. The proposed hybrid fusion model "RadientFusion-XR" utilizes LBP and HOG features with shallow learning algorithms. The proposed hybrid HOG-LBP fusion model, RadientFusion-XR, detects COVID-19 cases from normal and pneumonia classes. This fusion model provides a comprehensive representation, enabling more precise differentiation among the three classes. This methodology presents a promising and efficient tool for early COVID-19 and pneumonia diagnosis in clinical settings, with potential integration into automated diagnostic systems. The findings highlight the potential of this hybrid feature extraction and a shallow learning approach to improve diagnostic accuracy in chest x-ray analysis significantly. The hybrid model using LBP and HOG features with an ensemble model achieved an exceptional accuracy of 99% for binary class (COVID-19, normal) and 97% for multi-class (COVID-19, normal, pneumonia), respectively. These results demonstrate the efficacy of our hybrid approach in enhancing feature representation and achieving superior classification accuracy. The proposed RadientFusion-XR model with hybrid feature extraction and shallow learning approach significantly increases the accuracy of COVID-19 and pneumonia diagnoses from chest x-rays. The interpretable nature of RadientFusion-XR, alongside its effectiveness and explainability, makes it a valuable tool for clinical applications, fostering trust and enabling informed decision-making by healthcare professionals.