Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells

Overview

Journal Ann Neurol

Specialty Neurology

Date 2021 Feb 10

PMID 33565152

Citations 15

Authors

Keiko Imamura

Yuichiro Yada

Yuishin Izumi

Mitsuya Morita

Akihiro Kawata

Takayo Arisato

Ayako Nagahashi

Takako Enami

Kayoko Tsukita

Hideshi Kawakami

Masanori Nakagawa

Ryosuke Takahashi

Haruhisa Inoue

Affiliations

Soon will be listed here.

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.

Citing Articles

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