Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study

Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.Objectives: The r...

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Autores principales:	Chubin Ou, Jiahui Liu, Yi Qian, Winston Chong, Dangqi Liu, Xuying He, Xin Zhang, Chuan-Zhi Duan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	intracranial aneurysm AutoML endovascular treatment machine learning stroke Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/72a0a387dcfb46f6aad06cd082ba06b4
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spelling	oai:doaj.org-article:72a0a387dcfb46f6aad06cd082ba06b42021-12-01T11:56:16ZAutomated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study1664-229510.3389/fneur.2021.735142https://doaj.org/article/72a0a387dcfb46f6aad06cd082ba06b42021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fneur.2021.735142/fullhttps://doaj.org/toc/1664-2295Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction.Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC).Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models.Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.Chubin OuChubin OuJiahui LiuYi QianWinston ChongDangqi LiuXuying HeXin ZhangChuan-Zhi DuanFrontiers Media S.A.articleintracranial aneurysmAutoMLendovascular treatmentmachine learningstrokeNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neurology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intracranial aneurysm AutoML endovascular treatment machine learning stroke Neurology. Diseases of the nervous system RC346-429
spellingShingle	intracranial aneurysm AutoML endovascular treatment machine learning stroke Neurology. Diseases of the nervous system RC346-429 Chubin Ou Chubin Ou Jiahui Liu Yi Qian Winston Chong Dangqi Liu Xuying He Xin Zhang Chuan-Zhi Duan Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
description	Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction.Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC).Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models.Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.
format	article
author	Chubin Ou Chubin Ou Jiahui Liu Yi Qian Winston Chong Dangqi Liu Xuying He Xin Zhang Chuan-Zhi Duan
author_facet	Chubin Ou Chubin Ou Jiahui Liu Yi Qian Winston Chong Dangqi Liu Xuying He Xin Zhang Chuan-Zhi Duan
author_sort	Chubin Ou
title	Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
title_short	Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
title_full	Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
title_fullStr	Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
title_full_unstemmed	Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study
title_sort	automated machine learning model development for intracranial aneurysm treatment outcome prediction: a feasibility study
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/72a0a387dcfb46f6aad06cd082ba06b4
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Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study

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