Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Innovative Medical Engineering & Technology, Division of Convergence Technology, National Cancer Center, Goyang, Korea.
Acta Ophthalmol. 2019 Jun;97(4):e519-e525. doi: 10.1111/aos.13970. Epub 2018 Nov 8.
Assessment of optic disc pallor in fundus photographs may be frequently misinterpreted due to the subjective nature of interpretation. We developed a fully automatic computer-aided detection (CAD) system for optic disc pallor using colour fundus photographs and evaluated the accuracy of the system.
A newly proposed CAD system was developed for automated segmentation and image analysis of optic disc pallor, and a logistic regression model was developed for risk analysis. A total of 230 photographs with variable degree of optic disc pallor, and 123 normal optic discs confirmed by optical coherence tomography were tested for validation of the software. Sensitivity and specificity of the CAD system in automatic detection of optic disc pallor using colour fundus photographs were evaluated. The results of manual detection of optic disc pallor on fundus photographs by two independent ophthalmologists were compared with the efficacy of the CAD system.
The fully automated CAD system achieved a sensitivity of 95.3% and a specificity of 96.7% for detecting optic disc pallor in colour fundus images. The overall accuracy of the CAD system was 96.1%, which was superior to the results of manual detection by individual examiners.
We developed an automated CAD system that successfully detected optic disc pallor in fundus photographs. The proposed algorithm can assist the clinical judgement of ophthalmologists for detecting optic disc pallor in fundus photographs.
由于眼底照片的解释具有主观性,因此对眼底照片中视盘苍白的评估可能经常被误解。我们使用彩色眼底照片开发了一种用于视盘苍白的全自动计算机辅助检测(CAD)系统,并评估了该系统的准确性。
我们开发了一种新的 CAD 系统,用于对视盘苍白的自动分割和图像分析,并开发了一个逻辑回归模型进行风险分析。共测试了 230 张视盘苍白程度不同的照片和 123 张经光学相干断层扫描证实的正常视盘,以验证软件的准确性。评估了 CAD 系统在使用彩色眼底照片自动检测视盘苍白中的敏感性和特异性。比较了两名独立眼科医生对视盘苍白的眼底照片进行手动检测的结果与 CAD 系统的效果。
全自动 CAD 系统在彩色眼底图像中检测视盘苍白的敏感性为 95.3%,特异性为 96.7%。CAD 系统的总体准确性为 96.1%,优于单个检查者的手动检测结果。
我们开发了一种自动 CAD 系统,成功地检测到眼底照片中的视盘苍白。该算法可以协助眼科医生在眼底照片中检测视盘苍白的临床判断。