Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis
Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis
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DOI：
10.1038/s41598-020-62634-3
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发表时间：
2020-03-27
影响因子：
4.6
通讯作者：
Koike, Takao
中科院分区：
文献类型：
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作者：
Fukae, Jun;Isobe, Masato;Koike, Takao
本研究旨在探讨深度学习在类风湿关节炎(RA)诊断中的应用。RA的诊断缺乏明确的标准或直接的标志物。风湿科医生根据科学证据和临床经验的综合评估诊断类风湿性关节炎。我们的新想法是将患者的各种临床信息转换成简单的二维图像，然后使用它们来微调卷积神经网络(CNN)，以区分类风湿关节炎和非类风湿关节炎。我们将每种类型的临床信息半定量地转换为四个彩色正方形图像，并将它们排列为每个患者的一张图像。一位风湿科医生修改了每个患者的临床信息，以增加学习数据。总共使用了1037张图像(252张RA，785张非RA)来微调具有转移学习的预先训练的CNN。对于与学习数据无关的临床数据(10个RA，40个非RA)作为测试数据，我们比较了经过微调的CNN与三位风湿病专家的分类能力。我们的简单系统可以潜在地支持RA的诊断，因此可能对专科医院和普通诊所的RA筛查有用。这项研究为深入学习类风湿关节炎的诊断铺平了道路。
This research aimed to study the application of deep learning to the diagnosis of rheumatoid arthritis (RA). Definite criteria or direct markers for diagnosing RA are lacking. Rheumatologists diagnose RA according to an integrated assessment based on scientific evidence and clinical experience. Our novel idea was to convert various clinical information from patients into simple two-dimensional images and then use them to fine-tune a convolutional neural network (CNN) to classify RA or nonRA. We semi-quantitatively converted each type of clinical information to four coloured square images and arranged them as one image for each patient. One rheumatologist modified each patient's clinical information to increase learning data. In total, 1037 images (252 RA, 785 nonRA) were used to fine-tune a pretrained CNN with transfer learning. For clinical data (10 RA, 40 nonRA), which were independent of the learning data and were used as testing data, we compared the classification ability of the fine-tuned CNN with that of three expert rheumatologists. Our simple system could potentially support RA diagnosis and therefore might be useful for screening RA in both specialised hospitals and general clinics. This study paves the way to enabling deep learning in the diagnosis of RA.