Huemer, Josef;
Kronschläger, Martin;
Ruiss, Manuel;
Sim, Dawn;
Keane, Pearse A;
Findl, Oliver;
Wagner, Siegfried K;
(2022)
Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification.
BMJ Open Ophthalmology
, 7
(1)
, Article e000992. 10.1136/bmjophth-2022-000992.
Preview |
PDF
e000992.full.pdf - Published Version Download (1MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1136/bmjophth-2022-000992 |
| Publisher version: | https://doi.org/10.1136/bmjophth-2022-000992 |
| Language: | English |
| Additional information: | © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. |
| Keywords: | diagnostic tests/investigation, imaging, lens and zonules, Area Under Curve, Capsule Opacification, Deep Learning, Humans, Retrospective Studies, Vision Disorders |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156774 |
Archive Staff Only
 |
View Item |
