Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells. Issue 6 (25th February 2021)

• Record Type: Journal Article
• 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
• Is Part Of: Annals of neurology. Volume 89:Issue 6(2021)
• Page Start: 1226
• Page End: 1233
• Publication Date: 2021-02-25
• Subjects: Neurology -- Periodicals; Pediatric neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; 616.8
• DOI: 10.1002/ana.26047
• Languages: English
• ISSNs: 0364-5134

Abstract

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