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
Highlights
NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made
Images of spinal motor neurons derived from healthy control subject and amyotrophic lateral sclerosis (ALS) patient induced pluripotent stem cells (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
Tensorflow/Keras[12] was used to construct a VGG-16 network[15] pretrained with ImageNet[16] dataset and to conduct transfer learning for classifying the images of healthy control motor neurons and of ALS motor neurons
Summary
NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. 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. We hereby propose a prediction model of ALS using deep learning with images of motor neurons derived from patient induced pluripotent stem cells (iPSCs) to support ALS diagnosis. Deep learning algorithms have recently been shown to perform well in classification in certain fields such as skin cancer diagnosis by pictures of skin legions,[4] lung cancer diagnosis by computed tomographic (CT) imaging,[5] pathological diagnosis of cancers for prediction of therapeutic efficacy,[6] and biological science.[7]
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