Original research: Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification

BMJ Open Ophthalmology

article

Josef Huemer¹  Martin Kronschläger³  Manuel Ruiss³  Dawn Sim¹  Pearse A Keane¹  Oliver Findl³  Siegfried K Wagner¹ 

[1]Department of Medical Retina , Moorfields Eye Hospital NHS Foundation Trust

[2]NIHR Biomedical Research Centre , Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology

[3]VIROS-Vienna Institute for Research in Ocular Surgery, a Karl Landsteiner Institute , Hanusch Hospital

[4]Institute of Ophthalmology

关键词: diagnostic tests/investigation;    imaging;    lens and zonules;   

DOI  :  10.1136/bmjophth-2022-000992

学科分类：工业工程学

来源: BMJ Publishing Group

【 摘 要 】

Objective To train and validate a code-free deep learning system (CFDLS) on classifying high-resolution digital retroillumination images of posterior capsule opacification (PCO) and to discriminate between clinically significant and non-significant PCOs.Methods and analysis For this retrospective registry study, three expert observers graded two independent datasets of 279 images three separate times with no PCO to severe PCO, providing binary labels for clinical significance. The CFDLS was trained and internally validated using 179 images of a training dataset and externally validated with 100 images. Model development was through Google Cloud AutoML Vision. Intraobserver and interobserver variabilities were assessed using Fleiss kappa (κ) coefficients and model performance through sensitivity, specificity and area under the curve (AUC).Results Intraobserver variability κ values for observers 1, 2 and 3 were 0.90 (95% CI 0.86 to 0.95), 0.94 (95% CI 0.90 to 0.97) and 0.88 (95% CI 0.82 to 0.93). Interobserver agreement was high, ranging from 0.85 (95% CI 0.79 to 0.90) between observers 1 and 2 to 0.90 (95% CI 0.85 to 0.94) for observers 1 and 3. On internal validation, the AUC of the CFDLS was 0.99 (95% CI 0.92 to 1.0); sensitivity was 0.89 at a specificity of 1. On external validation, the AUC was 0.97 (95% CI 0.93 to 0.99); sensitivity was 0.84 and specificity was 0.92.Conclusion This CFDLS provides highly accurate discrimination between clinically significant and non-significant PCO equivalent to human expert graders. The clinical value as a potential decision support tool in different models of care warrants further research.

【 授权许可】

CC BY-NC|CC BY|CC BY-NC-ND