Machine Learning Algorithm Improves the Prediction of Transplant Hepatic Artery Stenosis or Occlusion: A Single-Center Study.
Document Type
Article
Publication Date
6-1-2023
Identifier
DOI: 10.1097/RUQ.0000000000000624
Abstract
The aim of this study was to determine if machine learning can improve the specificity of detecting transplant hepatic artery pathology over conventional quantitative measures while maintaining a high sensitivity.This study presents a retrospective review of 129 patients with transplanted hepatic arteries. We illustrate how beyond common clinical metrics such as stenosis and resistive index, a more comprehensive set of waveform data (including flow half-lives and Fourier transformed waveforms) can be integrated into machine learning models to obtain more accurate screening of stenosis and occlusion. We present a novel framework of Extremely Randomized Trees and Shapley values, we allow for explainability at the individual level.The proposed framework identified cases of clinically significant stenosis and occlusion in hepatic arteries with a state-of-the-art specificity of 65%, while maintaining sensitivity at the current standard of 94%. Moreover, through 3 case studies of correct and mispredictions, we demonstrate examples of how specific features can be elucidated to aid in interpreting driving factors in a prediction.This work demonstrated that by utilizing a more complete set of waveform data and machine learning methodologies, it is possible to reduce the rate of false-positive results in using ultrasounds to screen for transplant hepatic artery pathology compared with conventional quantitative measures. An advantage of such techniques is explainability measures at the patient level, which allow for increased radiologists' confidence in the predictions.
Journal Title
Ultrasound quarterly
Volume
39
Issue
2
First Page
86
Last Page
94
MeSH Keywords
Humans; Hepatic Artery; Constriction, Pathologic; Vascular Diseases; Algorithms; Machine Learning; Retrospective Studies
Keywords
Hepatic Artery; Pathologic Constriction; Vascular Diseases; Algorithms; Machine Learning; Retrospective Studies
Recommended Citation
Feldman K, Baraboo J, Dinakarpandian D, Chan SS. Machine Learning Algorithm Improves the Prediction of Transplant Hepatic Artery Stenosis or Occlusion: A Single-Center Study. Ultrasound Q. 2023;39(2):86-94. Published 2023 Jun 1. doi:10.1097/RUQ.0000000000000624
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