Literature DB >> 34110911

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

Alyssa M Flores1, Falen Demsas1, Nicholas J Leeper1,2,3, Elsie Gyang Ross1,4,3.   

Abstract

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.

Entities:  

Keywords:  artificial intelligence; deep learning; machine learning; peripheral artery disease; precision medicine; vascular disease

Mesh:

Year:  2021        PMID: 34110911      PMCID: PMC8285054          DOI: 10.1161/CIRCRESAHA.121.318224

Source DB:  PubMed          Journal:  Circ Res        ISSN: 0009-7330            Impact factor:   23.213


  100 in total

Review 1.  The Society for Vascular Surgery practice guidelines on follow-up after vascular surgery arterial procedures.

Authors:  R Eugene Zierler; William D Jordan; Brajesh K Lal; Firas Mussa; Steven Leers; Joseph Fulton; William Pevec; Andrew Hill; M Hassan Murad
Journal:  J Vasc Surg       Date:  2018-07       Impact factor: 4.268

Review 2.  Surrogate Markers of Abdominal Aortic Aneurysm Progression.

Authors:  Anders Wanhainen; Kevin Mani; Jonathan Golledge
Journal:  Arterioscler Thromb Vasc Biol       Date:  2015-12-29       Impact factor: 8.311

3.  Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.

Authors:  Adriaan Coenen; Young-Hak Kim; Mariusz Kruk; Christian Tesche; Jakob De Geer; Akira Kurata; Marisa L Lubbers; Joost Daemen; Lucian Itu; Saikiran Rapaka; Puneet Sharma; Chris Schwemmer; Anders Persson; U Joseph Schoepf; Cezary Kepka; Dong Hyun Yang; Koen Nieman
Journal:  Circ Cardiovasc Imaging       Date:  2018-06       Impact factor: 7.792

4.  Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.

Authors:  Caroline Caradu; Benedetta Spampinato; Ana Maria Vrancianu; Xavier Bérard; Eric Ducasse
Journal:  J Vasc Surg       Date:  2020-12-09       Impact factor: 4.268

5.  Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population.

Authors:  Andrew Ward; Ashish Sarraju; Sukyung Chung; Jiang Li; Robert Harrington; Paul Heidenreich; Latha Palaniappan; David Scheinker; Fatima Rodriguez
Journal:  NPJ Digit Med       Date:  2020-09-23

6.  DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS.

Authors:  Alejandro Schuler; Vincent Liu; Joe Wan; Alison Callahan; Madeleine Udell; David E Stark; Nigam H Shah
Journal:  Pac Symp Biocomput       Date:  2016

7.  3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0.

Authors:  Sanagala S Skandha; Suneet K Gupta; Luca Saba; Vijaya K Koppula; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Durga P Misra; Vikas Agarwal; Aditya M Sharma; Vijay Viswanathan; Vijay S Rathore; Monika Turk; Raghu Kolluri; Klaudija Viskovic; Elisa Cuadrado-Godia; George D Kitas; Andrew Nicolaides; Jasjit S Suri
Journal:  Comput Biol Med       Date:  2020-08-16       Impact factor: 4.589

8.  Association of Statin Dose With Amputation and Survival in Patients With Peripheral Artery Disease.

Authors:  Shipra Arya; Anjali Khakharia; Zachary O Binney; Randall R DeMartino; Luke P Brewster; Philip P Goodney; Peter W F Wilson
Journal:  Circulation       Date:  2018-01-12       Impact factor: 29.690

9.  Design and Rationale of the Best Endovascular Versus Best Surgical Therapy for Patients With Critical Limb Ischemia (BEST-CLI) Trial.

Authors:  Matthew T Menard; Alik Farber; Susan F Assmann; Niteesh K Choudhry; Michael S Conte; Mark A Creager; Michael D Dake; Michael R Jaff; John A Kaufman; Richard J Powell; Diane M Reid; Flora Sandra Siami; George Sopko; Christopher J White; Kenneth Rosenfield
Journal:  J Am Heart Assoc       Date:  2016-07-08       Impact factor: 5.501

10.  Machine learning for endoleak detection after endovascular aortic repair.

Authors:  Salmonn Talebi; Mohammad H Madani; Ali Madani; Ashley Chien; Jody Shen; Domenico Mastrodicasa; Dominik Fleischmann; Frandics P Chan; Mohammad R K Mofrad
Journal:  Sci Rep       Date:  2020-10-27       Impact factor: 4.996

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  8 in total

1.  Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.

Authors:  Bo Pang; Qiong Wang; Min Yang; Mei Xue; Yicheng Zhang; Xiangling Deng; Zhixin Zhang; Wenquan Niu
Journal:  Front Endocrinol (Lausanne)       Date:  2022-06-30       Impact factor: 6.055

Review 2.  Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field.

Authors:  Cristina Andreea Adam; Delia Lidia Șalaru; Cristina Prisacariu; Dragoș Traian Marius Marcu; Radu Andy Sascău; Cristian Stătescu
Journal:  Int J Mol Sci       Date:  2022-04-30       Impact factor: 6.208

3.  Peripheral Vascular Disease in 2021.

Authors:  Nicholas J Leeper; Naomi M Hamburg
Journal:  Circ Res       Date:  2021-06-10       Impact factor: 23.213

Review 4.  Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.

Authors:  Yun Xin Teoh; Khin Wee Lai; Juliana Usman; Siew Li Goh; Hamidreza Mohafez; Khairunnisa Hasikin; Pengjiang Qian; Yizhang Jiang; Yuanpeng Zhang; Samiappan Dhanalakshmi
Journal:  J Healthc Eng       Date:  2022-02-18       Impact factor: 2.682

5.  Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records.

Authors:  I Ghanzouri; S Amal; V Ho; L Safarnejad; J Cabot; C G Brown-Johnson; N Leeper; S Asch; N H Shah; E G Ross
Journal:  Sci Rep       Date:  2022-08-03       Impact factor: 4.996

6.  Risk profiling in the prevention and treatment of chronic wounds using artificial intelligence.

Authors:  Karen Cross; Keith Harding
Journal:  Int Wound J       Date:  2022-10       Impact factor: 3.099

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

Authors:  Ali Al-Ramini; Mahdi Hassan; Farahnaz Fallahtafti; Mohammad Ali Takallou; Hafizur Rahman; Basheer Qolomany; Iraklis I Pipinos; Fadi Alsaleem; Sara A Myers
Journal:  Sensors (Basel)       Date:  2022-09-30       Impact factor: 3.847

Review 8.  Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction.

Authors:  Amankeldi A Salybekov; Markus Wolfien; Shuzo Kobayashi; Gustav Steinhoff; Takayuki Asahara
Journal:  Cells       Date:  2021-11-23       Impact factor: 6.600

  8 in total

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