1. Deep Learning and ALS
- Author
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Keiko Imamura, Takayo Arisato, Takako Enami, Kayoko Tsukita, Mitsuya Morita, Yuishin Izumi, Masanori Nakagawa, Ayako Nagahashi, Akihiro Kawata, Ryosuke Takahashi, Hideshi Kawakami, Yuichiro Yada, and Haruhisa Inoue more...
- Subjects
Male ,0301 basic medicine ,Induced Pluripotent Stem Cells ,Brief Communication ,Convolutional neural network ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Healthy control ,Humans ,Medicine ,Amyotrophic lateral sclerosis ,Induced pluripotent stem cell ,Aged ,Motor Neurons ,business.industry ,Deep learning ,Amyotrophic Lateral Sclerosis ,Middle Aged ,medicine.disease ,Early Diagnosis ,030104 developmental biology ,Neurology ,Female ,Neurology (clinical) ,Artificial intelligence ,Prospective research ,Brief Communications ,business ,Neuroscience ,030217 neurology & neurosurgery - 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, Deep LearningとALS iPS細胞を用いた疾患予測テクノロジー --人工知能のALS検知・診断への応用--. 京都大学プレスリリース. 2021-02-24., Deep learning amyotrophic lateral sclerosis by taking pictures. 京都大学プレスリリース. 2021-02-24. more...
- Published
- 2021