Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes

2021 ◽  
Vol 128 (12) ◽  
pp. 1833-1850
Author(s):  
Alyssa M. Flores ◽  
Falen Demsas ◽  
Nicholas J. Leeper ◽  
Elsie Gyang Ross
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Outcomes ◽  
Peripheral Artery Disease ◽  
Intelligent Systems ◽  
Image Interpretation ◽  
Machine Learning Algorithms ◽  
Great Promise ◽  
Peripheral Artery ◽  
Artery Disease

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.

scholarly journals The use of machine learning for the identification of peripheral artery disease and future mortality risk

2016 ◽  
Vol 64 (5) ◽  
pp. 1515-1522.e3 ◽  
Author(s):  
Elsie Gyang Ross ◽  
Nigam H. Shah ◽  
Ronald L. Dalman ◽  
Kevin T. Nead ◽  
John P. Cooke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Peripheral Artery Disease ◽  
Mortality Risk ◽  
Peripheral Artery ◽  
Artery Disease

scholarly journals Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

Biomedicines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 116
Author(s):  
Chi-Hsiao Yeh ◽  
Yi-Ju Chou ◽  
Tsung-Hsien Tsai ◽  
Paul Wei-Che Hsu ◽  
Chun-Hsien Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Antiplatelet Therapy ◽  
Peripheral Artery Disease ◽  
Cardiovascular Events ◽  
Genetic Factors ◽  
Genome Wide Association Study ◽  
Peripheral Artery ◽  
Increased Risk ◽  
Artery Disease

An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.

Abstract 122: Use of Machine Learning to Accurately Predict Adverse Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elsie G Ross ◽  
Nicholas Leeper ◽  
Nigam Shah
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Predictive Model ◽  
Predictive Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Record ◽  
Peripheral Artery ◽  
Artery Disease

Introduction: Patients with peripheral artery disease (PAD) are at high risk of major adverse cardiac and cerebrovascular events (MACCE). However, no currently available risk scores accurately delineate which patients are most likely to sustain an event, creating a missed opportunity for more aggressive risk factor management. We set out to develop a novel predictive model - based on automated machine learning algorithms using electronic health record (EHR) data - with the aim of identifying which PAD patients are most likely to have an adverse outcome during follow-up. Methods: Data were derived from patients with a diagnosis of PAD at our institution. Novel machine-learning algorithms including random forest and penalized regression predictive models were developed using structured and unstructured data that including lab values, diagnosis codes, medications, and clinical notes. Patients were matched for total follow-up time to remove length of patient records as a biasing factor in our predictive models. Results: After matching for length of follow-up, 3,807 patients were included in our models. A total of 1,269 patients had a MACCE event after PAD diagnosis. The median time to MACCE was 2.8 years after PAD diagnosis. Utilizing 1,492 different variables extracted from the EHR, our best predictive model was able to very accurately predict which patients would go on to have a MACCE event after diagnosis of PAD with an AUC of 0.98, with a sensitivity, specificity and positive predictive value of 0.90, 0.96, and 0.93, respectively. Conclusions: Hypothesis-free, machine-learning algorithms using freely available data in the EHR can accurately predict which PAD patients are most likely to go on to develop future MACCE. While these findings require validation in an independent data set, there is hope that these informatics approaches can be applied to the medical record in an automated fashion to risk stratify patients with vascular disease and identify those who might benefit from more aggressive disease management in real-time.

scholarly journals PC102. A Novel Machine Learning-Driven Clinical and Proteomic Tool for the Diagnosis of Peripheral Artery Disease

2019 ◽  
Vol 69 (6) ◽  
pp. e233-e234
Author(s):  
Joel L. Ramirez ◽  
Craig A. Magaret ◽  
Sukaynah A. Khetani ◽  
Rhonda F. Rhyne ◽  
Celine Peters ◽  
...  
Keyword(s):  
Machine Learning ◽  
Peripheral Artery Disease ◽  
Peripheral Artery ◽  
Artery Disease

scholarly journals Artificial intelligence and echocardiography

2018 ◽  
pp. R115-R125 ◽  
Author(s):  
M Alsharqi ◽  
W J Woodward ◽  
J A Mumith ◽  
D C Markham ◽  
R Upton ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Image Interpretation ◽  
Future Directions ◽  
Wide Range ◽  
Automated Interpretation ◽  
Artery Disease ◽  
Consistent Assessment ◽  
Ischaemic Burden

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

Associations of coronary and peripheral artery disease with presence, expansion, and rupture of abdominal aortic aneurysm – a grin without a cat!

VASA ◽  
2017 ◽  
Vol 46 (3) ◽  
pp. 151-158 ◽  
Author(s):  
Hisato Takagi ◽  
Takuya Umemoto
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Peripheral Artery Disease ◽  
Literature Search ◽  
Systematic Literature Search ◽  
Peripheral Artery ◽  
Epidemiological Evidence ◽  
Abdominal Aortic ◽  
Artery Disease ◽  
Meta Analyses

Abstract. Both coronary and peripheral artery disease are representative atherosclerotic diseases, which are also known to be positively associated with presence of abdominal aortic aneurysm. It is still controversial, however, whether coronary and peripheral artery disease are positively associated with expansion and rupture as well as presence of abdominal aortic aneurysm. In the present article, we overviewed epidemiological evidence, i. e. meta-analyses, regarding the associations of coronary and peripheral artery disease with presence, expansion, and rupture of abdominal aortic aneurysm through a systematic literature search. Our exhaustive search identified seven meta-analyses, which suggest that both coronary and peripheral artery disease are positively associated with presence of abdominal aortic aneurysm, may be negatively associated with expansion of abdominal aortic aneurysm, and might be unassociated with rupture of abdominal aortic aneurysm.

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