Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms?

Autor:	Mu N; Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America., Rezaeitaleshmahalleh M; Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America., Lyu Z; Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America., Wang M; Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonino, TX, United States of America., Tang J; Department of Health Administration and Policy, George Mason University, Fairfax, VA, United States of America., Strother CM; Department of Radiology, University of Wisconsin, Madison, WI, United States of America., Gemmete JJ; Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America., Pandey AS; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, United States of America., Jiang J; Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America.; Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, United States of America.
Jazyk:	angličtina
Zdroj:	Biomedical physics & engineering express [Biomed Phys Eng Express] 2023 Mar 10; Vol. 9 (3). Date of Electronic Publication: 2023 Mar 10.
DOI:	10.1088/2057-1976/acb1b3
Abstrakt:	Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP) algorithms to explain and analyze 6 Ml algorithms. All models performed comparably: LR area under the curve (AUC) was 0.71; SVM AUC was 0.76; RF AUC was 0.73; XGBoost AUC was 0.78; MLPNN AUC was 0.73; BART AUC was 0.73. Our interpretability analysis demonstrated consistent results across all the methods; i.e., the utility of the top 12 features was broadly consistent. Furthermore, contributions of 9 important features (aneurysm area, aneurysm location, aneurysm type, wall shear stress maximum during systole, ostium area, the size ratio between aneurysm width, (parent) vessel diameter, one standard deviation among time-averaged low shear area, and one standard deviation of temporally averaged low shear area less than 0.4 Pa) were nearly the same. This research suggested that ML classifiers can provide explainable predictions consistent with general domain knowledge concerning IA rupture. With the improved understanding of ML algorithms, clinicians' trust in ML algorithms will be enhanced, accelerating their clinical translation. (Creative Commons Attribution license.)
Databáze:	MEDLINE
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