睑板腺
体内
共焦显微镜
医学
共焦
病理
显微镜
眼科
生物
光学
物理
生物技术
眼睑
作者
Sachiko Maruoka,Hitoshi Tabuchi,Daisuke Nagasato,Hiroki Masumoto,Tai-ichiro Chikama,Akiko Kawai,Naoko Oishi,Toshi Maruyama,Yoshitake Kato,Takahiko Hayashi,Chikako Katakami
出处
期刊:Cornea
[Ovid Technologies (Wolters Kluwer)]
日期:2020-02-11
卷期号:39 (6): 720-725
被引量:32
标识
DOI:10.1097/ico.0000000000002279
摘要
Purpose: To evaluate the ability of deep learning (DL) models to detect obstructive meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images. Methods: For this study, we included 137 images from 137 individuals with obstructive MGD (mean age, 49.9 ± 17.7 years; 44 men and 93 women) and 84 images from 84 individuals with normal meibomian glands (mean age, 53.3 ± 19.6 years; 29 men and 55 women). We constructed and trained 9 different network structures and used single and ensemble DL models and calculated the area under the curve, sensitivity, and specificity to compare the diagnostic abilities of the DL. Results: For the single DL model (the highest model; DenseNet-201), the area under the curve, sensitivity, and specificity for diagnosing obstructive MGD were 0.966%, 94.2%, and 82.1%, respectively, and for the ensemble DL model (the highest ensemble model; VGG16, DenseNet-169, DenseNet-201, and InceptionV3), 0.981%, 92.1%, and 98.8%, respectively. Conclusions: Our network combining DL and in vivo laser confocal microscopy learned to differentiate between images of healthy meibomian glands and images of obstructive MGD with a high level of accuracy that may allow for automatic obstructive MGD diagnoses in patients in the future.
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