Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide.

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases.

Joint degeneration is a major global health issue requiring improved diagnostic and prognostic tools. This review examines whether integrating Bayesian graphical models with multiscale medical imaging can enhance detection, analysis, and prediction of joint degeneration compared to traditional single-scale methods. Recent advances in quantitative MRI, such as T2 mapping, enable early detection of subtle cartilage changes, supporting earlier intervention. Bayesian graphical models provide a flexible framework for representing complex relationships and updating predictions as new evidence emerges. Unlike prior reviews that address Bayesian methods or musculoskeletal imaging separately, this work synthesizes these domains into a unified framework that spans molecular, cellular, tissue, and organ-level analyses, providing methodological guidance and clinical translation pathways. Key topics within Bayesian inference include multiscale analysis, probabilistic graphical models, spatial-temporal modeling, network connectivity analysis, advanced imaging biomarkers, quantitative analysis, quantitative MRI techniques, radiomics and texture analysis, multimodal integration strategies, uncertainty quantification, variational inference approaches, Monte Carlo methods, and model selection and validation, as well as diffusion models for medical imaging and Bayesian joint diffusion models. Additional attention is given to diffusion models for advanced medical image generation, addressing challenges such as limited datasets and patient privacy. Clinical translation and validation requirements are emphasized, highlighting the need for rigorous evaluation to ensure that synthesized or processed images maintain diagnostic accuracy. Finally, this review discusses implementation challenges and outlines future research directions, emphasizing the potential for earlier diagnosis, improved risk assessment, and personalized treatment strategies to reduce the growing global burden of musculoskeletal disorders.

Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with coronary artery disease (CAD), also known as ischemic heart disease (IHD), responsible for approximately 13% of global deaths in 2021. Studies applying machine learning (ML) and deep learning (DL) to heart disease classification have demonstrated promising results in risk prediction and feature extraction. Background/Objectives: In this study, we develop an AI/ML framework to predict and classify ischemic heart disease risk using publicly available datasets, the Framingham Heart Study and the Cleveland subset of the UCI Heart Disease dataset, along with explanations for how predictions were made by a process called SHAP (SHapley Additive exPlanations). Methods: We implemented a leakage-controlled machine learning pipeline that included data cleaning, stratified 80/20 train-test splitting, training-fold-only feature scaling and class balancing, 5-fold hyperparameter tuning, SHAP interpretability, and Brier score-based calibration assessment. Logistic regression, random forest, K-nearest neighbors, XGBoost, and a feedforward neural network were evaluated on the Framingham dataset and the Cleveland subset of the UCI Heart Disease dataset. Performance was assessed using accuracy, precision, recall, F1-score, Matthews correlation coefficient, AUROC, and Brier score. Results: After leakage-controlled evaluation, Framingham performance was more modest than in the preliminary analysis. Logistic regression achieved the highest AUROC on the Framingham dataset (0.7234), while random forest achieved the lowest Brier score (0.1750), and the feedforward neural network achieved the highest accuracy (0.7719). On the Cleveland subset, logistic regression achieved the strongest threshold-based performance (accuracy 0.8667, precision 0.8571, recall 0.8571, F1-score 0.8571, MCC 0.7321), whereas K-nearest neighbors achieved the highest AUROC (0.9531) and lowest Brier score (0.0942). SHAP highlighted systolic blood pressure, smoking status, and hypertension as influential predictors (Framingham) and number of major vessels, chest pain type, thallium stress-test result (thal; normal, fixed defect, or reversible defect), and age (Cleveland) as top predictors. Conclusions: Optimal model performance is dataset-dependent, and SHAP enhances clinical interpretability. Broader access to high-quality, de-identified medical data could accelerate reproducible ML research in cardiology.

Hypertension and osteoporosis are prevalent chronic conditions that frequently coexist, particularly in aging populations. Increasing evidence indicates that they share interconnected pathophysiological pathways involving calcium metabolism, chronic inflammation, oxidative stress, and hormonal regulation. Diet is a critical adjustable factor that influences the onset and progression of both conditions. This review evaluates the impact of dietary modification, including adherence to the Dietary Approaches to Stop Hypertension (DASH) diet, the Mediterranean diet, sodium restriction, and targeted nutrient intake (e.g., calcium, vitamin D, potassium, and magnesium), on bone mineral density (BMD) in individuals with treated and untreated hypertension. While the DASH diet effectively reduces blood pressure, its potential to lower BMD in the absence of adequate calcium intake warrants caution. In contrast, the Mediterranean diet provides broader nutritional support for both vascular and skeletal health through its anti-inflammatory and antioxidant profile. Diets rich in dairy products, minerals, and bioactive compounds are associated with improved BMD and vascular function. This review combines current evidence, explains mechanistic links between diet, bone, and cardiovascular physiology, and offers evidence-based dietary recommendations to address the coexisting conditions of hypertension and osteoporosis.