Biomechanical analysis of the lumbar spine is vital for understanding load distribution and stress patterns under physiological conditions. Traditional finite element analysis (FEA) relies on time-consuming manual segmentation and meshing, leading to long runtimes and inconsistent accuracy. Automating this process improves efficiency and reproducibility. This study introduces an automated FEA methodology for lumbar spine biomechanics, integrating deep learning-based segmentation with computational modeling to streamline workflows from imaging to simulation. Medical imaging data were segmented using deep learning frameworks for vertebrae and intervertebral discs. Segmented structures were transformed into optimized surface meshes via Laplacian smoothing and decimation. Using the Gibbon library and FEBio, FEA models incorporated cortical and cancellous bone, nucleus, annulus, cartilage, and ligaments. Ligament attachments used spherical coordinate-based segmentation; vertebral endplates were extracted via principal component analysis (PCA) for cartilage modeling. Simulations assessed stress, strain, and displacement under axial rotation, extension, flexion, and lateral bending. The automated pipeline cut model preparation time by 97.9%, from over 24 hours to 30 minutes and 49.48 seconds. Biomechanical responses aligned with experimental and traditional FEA data, showing high posterior element loads in extension and flexion, consistent ligament forces, and disc deformations. The approach enhanced reproducibility with minimal manual input. This automated methodology provides an efficient, accurate framework for lumbar spine biomechanics, eliminating manual segmentation challenges. It supports clinical diagnostics, implant design, and rehabilitation, advancing computational and patient-specific spinal studies. Rapid simulations enhance implant optimization, and early detection of degenerative spinal issues, improving personalized treatment and research.

Pneumonia is a respiratory condition characterized by inflammation of the alveolar sacs in the lungs, which disrupts normal oxygen exchange. This disease disproportionately impacts vulnerable populations, including young children (under five years of age) and elderly individuals (over 65 years), primarily due to their compromised immune systems. The mortality rate associated with pneumonia remains alarmingly high, particularly in low-resource settings where healthcare access is limited. Although effective prevention strategies exist, pneumonia continues to claim the lives of approximately one million children each year, earning its reputation as a "silent killer." Globally, an estimated 500 million cases are documented annually, underscoring its widespread public health burden. This study explores the design and evaluation of the CNN-based Computer-Aided Diagnostic (CAD) systems with an aim of carrying out competent as well as resourceful classification and categorization of chest radiographs into binary classes (Normal, Pneumonia). An augmented Kaggle dataset of 18,200 chest radiographs, split between normal and pneumonia cases, was utilized. This study conducts a series of experiments to evaluate lightweight CNN models--ShuffleNet, NASNet-Mobile, and EfficientNet-b0--using transfer learning that achieved accuracy of 90%, 88% and 89%, prompting the task for deep feature extraction from each of the networks and applying feature fusion to further pair it with SVM classifier and XGBoost classifier, achieving an accuracy of 97% and 98% resepectively. The proposed research emphasizes the crucial role of CAD systems in advancing radiological diagnostics, delivering effective solutions to aid radiologists in distinguishing between diagnoses by applying feature fusion, feature selection along with various machine learning algorithms and deep learning architectures.

The ToolCAP cohort study is a prospective, observational, multi-site platform study designed to collect harmonized, high-quality clinical, imaging, and biological data on children with IMCI-defined pneumonia in low- and middle-income countries (LMICs). The primary objective is to inform the development and validation of diagnostic and prognostic tools, including lung ultrasound (LUS), point-of-care biomarkers, and AI-based models, to improve pneumonia diagnosis, management, and antimicrobial stewardship. This statistical analysis plan (SAP) outlines the analytic strategy for describing the study population, assessing the performance of candidate diagnostic tools, and enabling data sharing in support of secondary research questions and AI model development. Children under 12 years presenting with suspected pneumonia are enrolled within 24 hours of presentation and undergo clinical assessment, digital auscultation, LUS, and optional biological sampling. Follow-up occurs on Day 8 and Day 29 to assess outcomes including recovery, treatment response, and complications. The SAP details variable definitions, data management strategies, and pre-specified analyses, including descriptive summaries, sensitivity and specificity of diagnostic tools against clinical reference standards, and exploratory subgroup analyses.

Lung cancer remains one of the leading causes of cancer mortality, and while low dose CT screening improves mortality, radiological detection is challenging due to the increasing shortage of radiologists. Artificial intelligence can significantly improve the procedure and also decrease the overall workload of the entire healthcare department. Building upon the existing works of application of genetic algorithm this study aims to create a novel algorithm for lung cancer diagnosis with utmost precision. We included a total of 156 CT scans of patients divided into two databases, followed by feature extraction using image statistics, histograms, and 2D transforms (FFT, DCT, WHT). Optimal feature vectors were formed and organized into Excel based knowledge-bases. Genetically trained classifiers like MLP, GFF-NN, MNN and SVM, are then optimized, with experimentations with different combinations of parameters, activation functions, and data partitioning percentages. Evaluation metrics included classification accuracy, Mean Squared Error (MSE), Area under Receiver Operating Characteristics (ROC) curve, and computational efficiency. Computer simulations demonstrated that the MNN (Topology II) classifier, specifically when trained with FFT coefficients and a momentum learning rule, consistently achieved 100% average classification accuracy on the cross-validation dataset for both Data-base I and Data-base II, outperforming MLP-based classifiers. This genetically optimized and trained MNN (Topology II) classifier is therefore recommended as the optimal solution for lung cancer diagnosis from CT scan images.

Accurately and preoperatively predicting tumor response to transarterial chemoembolization (TACE) treatment is crucial for individualized treatment decision-making hepatocellular carcinoma (HCC). In this study, we propose a novel feature fusion network based on the Convolutional Neural Networks Meet Vision Transformers (CMT) architecture, termed CMT-FFNet, to predict TACE efficacy using preoperative multiphase Magnetic Resonance Imaging (MRI) scans. The CMT-FFNet combines local feature extraction with global dependency modeling through attention mechanisms, enabling the extraction of complementary information from multiphase MRI data. Additionally, we introduce an orthogonality loss to optimize the fusion of imaging and clinical features, further enhancing the complementarity of cross-modal features. Moreover, visualization techniques were employed to highlight key regions contributing to model decisions. Extensive experiments were conducted to evaluate the effectiveness of the proposed modules and network architecture. Experimental results demonstrate that our model effectively captures latent correlations among features extracted from multiphase MRI data and multimodal inputs, significantly improving the prediction performance of TACE treatment response in HCC patients.

BackgroundPositron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD). MethodsFrom 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD. Deep learning was used to derive coronary artery calcium score (CAC) from CT attenuation correction maps. XGBoost machine learning model was developed using data from one site to detect CAD, defined as left main stenosis [&ge;]50% or [&ge;]70% in other arteries. The model utilized 10 image-derived parameters from clinical practice: CAC, stress/rest left ventricle ejection fraction, stress myocardial blood flow (MBF), myocardial flow reserve (MFR), ischemic and stress total perfusion deficit (TPD), transient ischemic dilation ratio, rate pressure product, and sex. Generalizability was evaluated in the remaining three sites--chosen to maximize testing power and capture inter-site variability--and model performance was compared with quantitative analyses using the area under the receiver operating characteristic curve (AUC). Patient-specific predictions were explained using shapley additive explanations. ResultsThere was a 61% and 53% CAD prevalence in the training (n=386) and external testing (n=1,278) set, respectively. In the external evaluation, the AI model achieved a higher AUC (0.83 [95% confidence interval (CI): 0.81-0.85]) compared to clinical score by experienced physicians (0.80 [0.77-0.82], p=0.02), ischemic TPD (0.79 [0.77-0.82], p<0.001), MFR (0.75 [0.72-0.78], p<0.001), and CAC (0.69 [0.66-0.72], p<0.001). The models performances were consistent in sex, body mass index, and age groups. The top features driving the prediction were stress/ischemic TPD, CAC, and MFR. ConclusionAI integrating perfusion, flow, and CAC scoring improves PET MPI diagnostic accuracy, offering automated and interpretable predictions for CAD diagnosis.

Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs' predictions and confidence scores in few-shot in-context learning for medical image classification. We introduce CALIN, an inference-time calibration method designed to mitigate the associated biases. Specifically, CALIN estimates the amount of calibration needed, represented by calibration matrices, using a bi-level procedure: progressing from the population level to the subgroup level prior to inference. It then applies this estimation to calibrate the predicted confidence scores during inference. Experimental results on three medical imaging datasets: PAPILA for fundus image classification, HAM10000 for skin cancer classification, and MIMIC-CXR for chest X-ray classification demonstrate CALIN's effectiveness at ensuring fair confidence calibration in its prediction, while improving its overall prediction accuracies and exhibiting minimum fairness-utility trade-off.

Timely and accurate diagnosis of appendicitis is critical in clinical settings to prevent serious complications. While CT imaging remains the standard diagnostic tool, the growing number of cases can overwhelm radiologists, potentially causing delays. In this paper, we propose a deep learning model that leverages 3D CT scans for appendicitis classification, incorporating Slice Attention mechanisms guided by external 2D datasets to enhance small lesion detection. Additionally, we introduce a hierarchical classification framework using pre-trained 2D models to differentiate between simple and complicated appendicitis. Our approach improves AUC by 3% for appendicitis and 5.9% for complicated appendicitis, offering a more efficient and reliable diagnostic solution compared to previous work.

We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (1) k-space measurement consistency and (2) adherence to the Bloch response model. Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines, providing more accurate parameter maps while better preserving measurement fidelity and physical model consistency-critical for solving reliably inverse problems in medical imaging.

Federated learning (FL) has emerged as a promising paradigm for collaboratively training deep learning models across institutions without exchanging sensitive medical data. However, its effectiveness is often hindered by limited data availability and non-independent, identically distributed data across participating clients, which can degrade model performance and generalization. To address these challenges, we propose a generative AI based data augmentation framework that integrates synthetic image sharing into the federated training process for breast cancer diagnosis via ultrasound images. Specifically, we train two simple class-specific Deep Convolutional Generative Adversarial Networks: one for benign and one for malignant lesions. We then simulate a realistic FL setting using three publicly available breast ultrasound image datasets: BUSI, BUS-BRA, and UDIAT. FedAvg and FedProx are adopted as baseline FL algorithms. Experimental results show that incorporating a suitable number of synthetic images improved the average AUC from 0.9206 to 0.9237 for FedAvg and from 0.9429 to 0.9538 for FedProx. We also note that excessive use of synthetic data reduced performance, underscoring the importance of maintaining a balanced ratio of real and synthetic samples. Our findings highlight the potential of generative AI based data augmentation to enhance FL results in the breast ultrasound image classification task.