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Prediction model of ALS by deep learning with patient iPSCs

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 and ALS patient iPSCs were analyzed by a deep convolutional neural network, and the algorithm achieved an area under the curve (AUC) 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 by future prospective research.

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