Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning

Abstract Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional ne...

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Autores principales:	Jeoung Kun Kim, Yoo Jin Choo, Hyunkwang Shin, Gyu Sang Choi, Min Cheol Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/467eca2f6d7b4b8096fc684c24cf11fd
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spelling	oai:doaj.org-article:467eca2f6d7b4b8096fc684c24cf11fd2021-12-02T18:03:26ZPrediction of ambulatory outcome in patients with corona radiata infarction using deep learning10.1038/s41598-021-87176-02045-2322https://doaj.org/article/467eca2f6d7b4b8096fc684c24cf11fd2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87176-0https://doaj.org/toc/2045-2322Abstract Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian’s assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649–0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.Jeoung Kun KimYoo Jin ChooHyunkwang ShinGyu Sang ChoiMin Cheol ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeoung Kun Kim Yoo Jin Choo Hyunkwang Shin Gyu Sang Choi Min Cheol Chang Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
description	Abstract Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian’s assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649–0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.
format	article
author	Jeoung Kun Kim Yoo Jin Choo Hyunkwang Shin Gyu Sang Choi Min Cheol Chang
author_facet	Jeoung Kun Kim Yoo Jin Choo Hyunkwang Shin Gyu Sang Choi Min Cheol Chang
author_sort	Jeoung Kun Kim
title	Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
title_short	Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
title_full	Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
title_fullStr	Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
title_full_unstemmed	Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
title_sort	prediction of ambulatory outcome in patients with corona radiata infarction using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/467eca2f6d7b4b8096fc684c24cf11fd
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Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning

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