Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review

Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.

Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features

The objective of this study was to identify a female-specific prognostic biomarker for peripheral artery disease (PAD) and develop a prediction model for 2-year major adverse limb events (MALE). Patients with/without PAD were recruited (n=461). Plasma concentrations of 68 circulating proteins were measured and patients were followed for 2 years. The primary outcome was MALE (composite of vascular intervention, major amputation, or acute/chronic limb threatening ischemia). We trained a random forest model using: 1) clinical characteristics, 2) female-specific PAD biomarker, and 3) clinical characteristics and female-specific PAD biomarker. Galectin-9 was the only protein to be significantly elevated in females compared to males in the discovery/validation analyses. The random forest model achieved the following AUROC's: 0.72 (clinical features), 0.83 (Galectin-9), and 0.86 (clinical features + Galectin-9). We identified Galectin-9 as a female-specific PAD biomarker and developed an accurate prognostic model for 2-year MALE using a combination of clinical features and plasma Galectin-9 levels.

Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease

Purpose of Review

Peripheral Artery Disease (PAD), a condition affecting millions of patients, is often underdiagnosed due to a lack of symptoms in the early stages and management can be complex given differences in genetic and phenotypic characteristics. This review aims to provide readers with an update on the utility of machine learning (ML) in the management of PAD.

Recent Findings

Recent research leveraging electronic health record (EHR) data and ML algorithms have demonstrated significant advances in the potential use of automated systems, namely artificial intelligence (AI), to accurately identify patients who might benefit from further PAD screening. Additionally, deep learning algorithms can be used on imaging data to assist in PAD diagnosis and automate clinical risk stratification.ML models can predict major adverse cardiovascular events (MACE) and major adverse limb events (MALE) with considerable accuracy, with many studies also demonstrating the ability to more accurately risk stratify patients for deleterious outcomes after surgical intervention. These predictions can assist physicians in developing more patient-centric treatment plans and allow for earlier, more aggressive management of modifiable risk-factors in high-risk patients. The use of proteomic biomarkers in ML models offers a valuable addition to traditional screening and stratification paradigms, though clinical utility may be limited by cost and accessibility.

Summary

The application of AI to the care of PAD patients may enable earlier diagnosis and more accurate risk stratification, leveraging readily available EHR and imaging data, and there is a burgeoning interest in incorporating biological data for further refinement. Thus, the promise of precision PAD care grows closer. Future research should focus on validating these models via real-world integration into clinical practice and prospective evaluation of the impact of this new care paradigm.

Machine learning-based gait adaptation dysfunction identification using CMill-based gait data

Background

Combining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns.

Objective

To analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal diagnostic models and key classification features.

Methods

This study was investigated with 30 stroke patients (mean age 42.69 years, 60% male) and 50 healthy adults (mean age 41.34 years, 58% male). Gait adaptability was assessed using a CMill treadmill on gait adaptation tasks: target stepping, slalom walking, obstacle avoidance, and speed adaptation. The preliminary analysis of variables in both groups was conducted using t-tests and Pearson correlation. Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. Model performance was evaluated using accuracy (ACC), sensitivity (SEN), F1-score and the area under the receiver operating characteristic (ROC) curve (AUC).

Results

The stroke group showed a significantly decreased gait speed (p = 0.000) and step length (SL) (p = 0.000), while the asymmetry of SL (p = 0.000) and ST (p = 0.000) was higher compared to the healthy group. The gait adaptation tasks significantly decreased in slalom walking (p = 0.000), obstacle avoidance (p = 0.000), and speed adaptation (p = 0.000). Gait speed (p = 0.000) and obstacle avoidance (p = 0.000) were significantly correlated with global F-A score in stroke patients. The AdaCost demonstrated better classification performance with an ACC of 0.85, SEN of 0.80, F1-score of 0.77, and ROC-AUC of 0.75. Obstacle avoidance and gait speed were identified as critical features in this model.

Conclusion

Stroke patients walk slower with shorter SL and more asymmetry of SL and ST. Their gait adaptability was decreased, particularly in obstacle avoidance and speed adaptation. The faster gait speed and better obstacle avoidance were correlated with better functional mobility. The AdaCost identifies individuals with GAD and facilitates clinical decision-making. This advances the future development of user-friendly interfaces and computer-aided diagnosis systems.

Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study

Aims

Cardiovascular disease (CVD) may not be detected in time with conventional clinical approaches. Abnormal gait patterns have been associated with pathological conditions and can be monitored continuously by gait video. We aim to test the association between non-contact, video-based gait information and general CVD status.

Methods and results

Individuals undergoing confirmatory CVD evaluation were included in a prospective, cross-sectional study. Gait videos were recorded with a Kinect camera. Gait features were extracted from gait videos to correlate with the composite and individual components of CVD, including coronary artery disease, peripheral artery disease, heart failure, and cerebrovascular events. The incremental value of incorporating gait information with traditional CVD clinical variables was also evaluated. Three hundred fifty-two participants were included in the final analysis [mean (standard deviation) age, 59.4 (9.8) years; 25.3% were female]. Compared with the baseline clinical variable model [area under the receiver operating curve (AUC) 0.717, (0.690-0.743)], the gait feature model demonstrated statistically better performance [AUC 0.753, (0.726-0.780)] in predicting the composite CVD, with further incremental value when incorporated with the clinical variables [AUC 0.764, (0.741-0.786)]. Notably, gait features exhibited varied association with different CVD component conditions, especially for peripheral artery disease [AUC 0.752, (0.728-0.775)] and heart failure [0.733, (0.707-0.758)]. Additional analyses also revealed association of gait information with CVD risk factors and the established CVD risk score.

Conclusion

We demonstrated the association and predictive value of non-contact, video-based gait information for general CVD status. Further studies for gait video-based daily living CVD monitoring are promising.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data.

A biomechanical perspective on walking in patients with peripheral artery disease

The most common symptom of peripheral artery disease (PAD) is intermittent claudication, which consists of debilitating leg pain during walking. In clinical settings, the presence of PAD is often noninvasively evaluated using the ankle-brachial index and imaging of the arterial supply. Furthermore, various questionnaires and functional tests are commonly used to measure the severity and negative effect of PAD on quality of life. However, these evaluations only provide information on vascular insufficiency and severity of the disease, but not regarding the complex mechanisms underlying walking impairments in patients with PAD. Biomechanical analyses using motion capture and ground reaction force measurements can provide insight into the underlying mechanisms to walking impairments in PAD. This review analyzes the application of biomechanics tools to identify gait impairments and their clinical implications on rehabilitation of patients with PAD. A total of 18 published journal articles focused on gait biomechanics in patients with PAD were studied. This narriative review shows that the gait of patients with PAD is impaired from the first steps that a patient takes and deteriorates further after the onset of claudication leg pain. These results point toward impaired muscle function across the ankle, knee, and hip joints during walking. Gait analysis helps understand the mechanisms operating in PAD and could also facilitate earlier diagnosis, better treatment, and slower progression of PAD.