Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification.

Large Language Models (LLMs) have evolved beyond being mere downstream Natural Language Processing (NLP) components: an increasing number of recent LLM-based systems can perform end-to-end planning, assembly, and evaluation of machine learning (ML) workflows. Advances in alignment techniques, tool integration, and program-generation methods enable LLMs to create end-to-end automated pipelines that execute from data collection through model deployment, verification, and reporting. The review examines three essential areas of current research, which include (i) the evolution of Transformer-based LLMs with tool-use interfaces, (ii) the exploration of fundamental studies about LLM capabilities, risks, and evaluation methodologies, and (iii) the design of new systems for LLM-based Automated Machine Learning (AutoML) and pipeline management systems that use multi-agent architecture, compiler frameworks, and iterative improvement cycles. The evaluation compares different approaches through their planning methods, verification techniques, optimization systems, monitoring capabilities, and operational expenses. The case studies report significant speed improvements, but researchers must address four essential problems that affect the reliable deployment of systems. The review follows IEEE Access guidelines for articles by including a clear methods section that explains the sources, time frame, and selection criteria, and by presenting visual representations that link systems to their corresponding pipeline stages. The review presents a unified, pipeline-stage–aligned conceptual framework that integrates terminology, design rules, and failure-detection practices for analyzing and validating LLM-based end-to-end ML systems. As a narrative review, this work emphasizes system-level design patterns and representative benchmarks rather than exhaustive quantitative meta-analysis.

Peripheral Artery Disease (PAD) significantly impairs quality of life and presents varying degrees of severity that correctly identifying would help choose the proper treatment approach and enable personalized treatment approaches. However, the challenge is that there is no single agreed-on measure to quantify the severity of a patient with PAD. This led to a trial-and-error approach to deciding the course of treatment for a given patient with PAD. This study uses non-clinical data, such as biomechanical data and advanced machine-learning techniques, to detect PAD severity levels and enhance treatment selection to overcome this challenge. Our findings in this paper lay the groundwork for a more data-driven, patient-centric approach to PAD management, optimizing treatment strategies for better patient outcomes.

Background: Peripheral artery disease (PAD) is clinically heterogeneous, complicating identification, treatment selection, and prediction of therapeutic benefit. Gait biomechanics provide high-resolution, objective digital biomarkers that can be used in routine care. Methods: We built a computational framework for three tasks: (1) distinguish controls versus PAD; (2) predict clinician-selected treatment (nonoperative vs. revascularization); and (3) forecast post-treatment ground-reaction force (GRF) from baseline GRF features. The dataset included 55 unique patients with PAD and 42 controls. Standard classifiers and regressors were evaluated, and performance was measured by accuracy, balanced accuracy, Matthew’s correlation coefficient (MCC), and mean absolute error. Results: A single GRF feature (propulsive peak) achieved 90.9% accuracy, 86.7% balanced accuracy, and 0.868 MCC for distinguishing PAD from controls and predicting treatment, outperforming patient-reported outcomes and kinematics. For outcome forecasting, a Random Forest model predicted changes in the propulsive peak with 80% accuracy and low error. Conclusions: GRF-based models accurately identify PAD, anticipate this clinical team’s treatment selection, and forecast functional recovery within this single-center cohort; generalization to other practice patterns and settings remains untested. The approach relies on minimal features and modest computation and is readily generalized to other medical conditions that affect gait. Because it is compatible with wearable sensors, it is well suited to real-time, remote assessment and can advance personalized, data-driven decision support in vascular care.