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- Title
Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells.
- Authors
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
- Abstract
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
- Subjects
INDUCED pluripotent stem cells; AMYOTROPHIC lateral sclerosis; DEEP learning; PREDICTION models; PLURIPOTENT stem cells; SIGNAL convolution; DEATH forecasting
- Publication
Annals of Neurology, 2021, Vol 89, Issue 6, p1226
- ISSN
0364-5134
- Publication type
Article
- DOI
10.1002/ana.26047