Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis

It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an...

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Bibliographic Details
Published in:	Frontiers in neurology Vol. 14; p. 1295642
Main Authors:	Miyamoto, Nobukazu, Ueno, Yuji, Yamashiro, Kazuo, Hira, Kenichiro, Kijima, Chikage, Kitora, Naoki, Iwao, Yoshihiko, Okuda, Kayo, Mishima, Shohei, Takahashi, Daisuke, Ono, Kazuto, Asari, Mika, Miyazaki, Kazuki, Hattori, Nobutaka
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 14-12-2023
Subjects:	cerebral infarction k-Nearest Neighbor method leave-one-out cross-validation method multimodal artificial intelligence Neurology stroke TOAST classification k-Nearest Neighbor method leave-one-out cross-validation method cerebral infarction stroke multimodal artificial intelligence TOAST classification
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Summary:	It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment. To build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled. By utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%). In recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Tarun Singh, University of Michigan, United States Reviewed by: Giuseppe Miceli, University of Palermo, Italy; Antonino Tuttolomondo, Università degli Studi di Palermo, Italy
ISSN:	1664-2295 1664-2295
DOI:	10.3389/fneur.2023.1295642