Validation of a deep learning model for the automated detection and quantification of cystoid macular oedema on optical coherence tomography in patients with retinitis pigmentosa.

PURPOSE

Accurate assessment of cystoid macular oedema (CMO) in patients with retinitis pigmentosa (RP) on spectral-domain optical coherence tomography (SD-OCT) is crucial for tracking disease progression and may serve as a therapeutic endpoint. Manual CMO segmentation is labour-intensive and prone to variability, making artificial intelligence (AI) an appealing solution to improve accuracy and efficiency. This study aimed to validate a deep learning (DL) model for automated CMO detection and quantification on SD-OCT scans in patients with RP.

METHODS

A segmentation model based on the no-new-Unet (nnU-Net) architecture was trained on 112 OCT volumes from the RETOUCH dataset (70 for training, 42 for validation). The model was externally tested on 37 SD-OCT scans from RP patients, with annotations from three expert graders. Performance was assessed using the Dice similarity coefficient and intraclass correlation coefficient (ICC).

RESULTS

For randomly selected central B-scans, the model achieved a mean Dice score of 0.889 ± 0.002 standard deviation (SD), while observers scored 0.878 ± 0.007 SD. The ICC for the model was 0.945 ± 0.014 SD, compared to 0.979 ± 0.008 SD for observers. On manually chosen central B-scans, Dice scores were 0.936 ± 0.005 SD for the model and 0.946 ± 0.012 SD for observers, with ICC values of 0.964 ± 0.011 SD and 0.981 ± 0.011 SD, respectively.

CONCLUSIONS

This DL model reliably segments CMO in RP, achieving performance comparable to human graders. It can enhance the efficiency and precision of CMO quantification, reducing variability in clinical practice and trials.