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Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells
Author(s) -
Imamura Keiko,
Yada Yuichiro,
Izumi Yuishin,
Morita Mitsuya,
Kawata Akihiro,
Arisato Takayo,
Nagahashi Ayako,
Enami Takako,
Tsukita Kayoko,
Kawakami Hideshi,
Nakagawa Masanori,
Takahashi Ryosuke,
Inoue Haruhisa
Publication year - 2021
Publication title -
annals of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.764
H-Index - 296
eISSN - 1531-8249
pISSN - 0364-5134
DOI - 10.1002/ana.26047
Subject(s) - amyotrophic lateral sclerosis , induced pluripotent stem cell , neuroscience , medicine , deep learning , convolutional neural network , artificial intelligence , physical medicine and rehabilitation , machine learning , computer science , psychology , pathology , biology , disease , embryonic stem cell , biochemistry , gene
In amyotrophic lateral sclerosis (ALS), early diagnosis is essential for both current and potential treatments. To find a supportive approach for the diagnosis, we constructed an artificial intelligence‐based prediction model of ALS using induced pluripotent stem cells (iPSCs). Images of spinal motor neurons derived from healthy control subject and ALS patient iPSCs were analyzed by a convolutional neural network, and the algorithm achieved an area under the curve of 0.97 for classifying healthy control and ALS. This prediction model by deep learning algorithm with iPSC technology could support the diagnosis and may provide proactive treatment of ALS through future prospective research. ANN NEUROL 2021;89:1226–1233