Medical-Risk Avoidance Based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project, Kyoto, Japan.
Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan.
Ann Neurol. 2021 Jun;89(6):1226-1233. doi: 10.1002/ana.26047. Epub 2021 Feb 25.
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.
在肌萎缩侧索硬化症（ALS）中，早期诊断对于当前和潜在的治疗都至关重要。为了找到一种支持诊断的方法，我们使用诱导多能干细胞（iPSC）构建了基于人工智能的 ALS 预测模型。通过卷积神经网络分析了来自健康对照受试者和 ALS 患者 iPSC 的脊髓运动神经元图像，该算法对健康对照和 ALS 的分类实现了 0.97 的曲线下面积。这种基于 iPSC 技术的深度学习算法预测模型可以支持诊断，并可能通过未来的前瞻性研究为 ALS 提供主动治疗。ANN NEUROL 2021;89:1226-1233.
