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

Abstract

ObjectiveTo 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 analysisFor 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).ResultsIntraobserver 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.ConclusionThis 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.

Improvements to a GLCM‐based machine‐learning approach for quantifying posterior capsule opacification

“…Regarding the quantitative analysis of PCO, only a limited number of software programs (e.g., AQUA II) have introduced machine‐learning based methods to extract texture features and assess the severity of opacification 25 . Huemer et al utilized code‐free deep learning to improve the diagnostic accuracy of PCO 26 . However, the effectiveness of these methods can still be improved.…”

“… 25 Huemer et al utilized code‐free deep learning to improve the diagnostic accuracy of PCO. 26 However, the effectiveness of these methods can still be improved. The aim of this study was to describe and validate the improvements made to the grey level co‐occurrence matrix (GLCM)‐based machine‐learning approach and compare its validity with clinical assessment for evaluating PCO.…”