PDF(Pubmed)
Abstract：
Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.
摘要：
深度学习(DL)应用于许多生物医学领域。我们对医学遗传学中的DL进行了范围审查。我们首先评估了14,002篇文章，其中133涉及医学遗传学中的DL。在研究期间，医学遗传学中的DL迅速增加。在医学遗传学中，DL已在很大程度上应用于具有遗传条件的受影响个体的小数据集（平均值=95，中位数=29）（研究了71种不同的遗传条件；24篇文章研究了多种条件）。医学遗传学中使用了多种数据类型，包括放射学（20%），眼科（14%），显微镜（8％），和基于文本的数据(4%);最常见的数据类型是患者面部照片(46%).DL作者和研究主题过度代表某些地理区域(美国，亚洲,和欧洲）。卷积神经网络(89%)是最常见的方法。在31%的研究中，结果与人类表现进行了比较。总的来说,51%的文章提供数据访问；16%的文章发布了源代码。为了进一步探索基因组学中的DL，我们进行了额外的分析，其结果突出了DL在医学遗传学中的未来机会。最后,我们预计未来DL应用将会增加。为了帮助数据管理，我们评估了一个DL，随机森林,和基于规则的分类器对文章摘要进行分类。
