Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study
Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study
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DOI：
10.1136/bmjopen-2019-031313
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发表时间：
2019-09-01
期刊：
影响因子：
2.9
通讯作者：
Miyata, Kazunori
中科院分区：
文献类型：
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作者：
Kamiya, Kazutaka;Ayatsuka, Yuji;Miyata, Kazunori
目的探讨深度学习扫描源前段光学相干断层扫描(AS-OCT)彩色编码地形图诊断圆锥角膜的准确性。设计一项诊断准确率研究，设置单中心研究。受试者共304只眼(Amsler-Krueich分级1级108只眼，2只眼(75只眼)，3只眼(42只眼)，4只眼(79只眼))和239只年龄匹配的健康眼。主要结果通过深度学习6个彩色编码地形图(前视、前曲、后视、后曲、结果对这6张图的算术平均输出数据进行深度学习，对正常眼和圆锥眼的识别准确率为0.991。在单幅地形图分析中，后高度图的准确率最高(0.993)，其次是后高度图(0.991)、前高度图(0.983)、角膜厚度图(0.982)、总屈光度图(0.978)和前曲率图(0.976)。这种深度学习也显示出对疾病分期的0.874的准确率。后曲率图(0.869)的准确率最高，其次是角膜厚度图(0.845)、前曲率图(0.836)、总屈光度图(0.836)、后高度图(0.829)和前高度图(0.820)。结论利用AS-OCT获得的颜色编码图进行深度学习可以有效地区分圆锥角膜和正常角膜，进而对疾病的级别进行分类。提示这将有助于在日常工作中提高圆锥角膜的诊断准确率。
Objective To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).Design A diagnostic accuracy study.Setting A single-centre study.Participants A total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes.Main outcome measures The diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map).Results Deep learning of the arithmetical mean output data of these six maps showed an accuracy of 0.991 in discriminating between normal and keratoconic eyes. For single map analysis, posterior elevation map (0.993) showed the highest accuracy, followed by posterior curvature map (0.991), anterior elevation map (0.983), corneal pachymetry map (0.982), total refractive power map (0.978) and anterior curvature map (0.976), in discriminating between normal and keratoconic eyes. This deep learning also showed an accuracy of 0.874 in classifying the stage of the disease. Posterior curvature map (0.869) showed the highest accuracy, followed by corneal pachymetry map (0.845), anterior curvature map (0.836), total refractive power map (0.836), posterior elevation map (0.829) and anterior elevation map (0.820), in classifying the stage.Conclusions Deep learning using the colour-coded maps obtained by the AS-OCT effectively discriminates keratoconus from normal corneas, and furthermore classifies the grade of the disease. It is suggested that this will become an aid for improving the diagnostic accuracy of keratoconus in daily practice.