Does real-time artificial intelligence-based visual pathology enhancement of three-dimensional optical coherence tomography scans optimise treatment decision in patients with nAMD? Rationale and design of the RAZORBILL study

The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus

PURPOSE OF REVIEW

The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic.

RECENT FINDINGS

Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations.

SUMMARY

It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.

Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning

Purpose

Using deep learning (DL)-based technique, we identify risk factors and create a prediction model for refractory neovascular age-related macular degeneration (nAMD) characterized by persistent disease activity (PDA) in spectral domain optical coherence tomography (SD-OCT) images.

Materials and methods

A total of 671 typical B-scans were collected from 186 eyes of 186 patients with nAMD. Spectral domain optical coherence tomography images were analyzed using a classification convolutional neural network (CNN) and a fully convolutional network (FCN) algorithm to extract six features involved in nAMD, including ellipsoid zone (EZ), external limiting membrane (ELM), intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelium detachment (PED), and subretinal hyperreflective material (SHRM). Random forest models were probed to predict 1-year disease activity (stable, PDA, and cured) based on the quantitative features computed from automated segmentation and evaluated with cross-validation.

Results

The algorithm to segment six SD-OCT features achieved the mean accuracy of 0.930 (95% CI: 0.916–0.943), dice coefficients of 0.873 (95% CI: 0.847–0.899), a sensitivity of 0.873 (95% CI: 0.844–0.910), and a specificity of 0.922 (95% CI: 0.905–0.940). The six-metric model including EZ and ELM achieved the optimal performance to predict 1-year disease activity, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.980, the accuracy of 0.930, the sensitivity of 0.920, and the specificity of 0.962. The integrity of EZ and ELM significantly improved the performance of the six-metric model than that of the four-metric model.

Conclusion

The prediction model reveals the potential to predict PDA in nAMD eyes. The integrity of EZ and ELM constituted the strongest predictive factor for PDA in nAMD eyes in real-world clinical practice. The results of this study are a significant step toward image-guided prediction of long-term disease activity in the management of nAMD and highlight the importance of the automatic identification of photoreceptor layers.

Artificial Intelligence and Imaging Processing in Optical Coherence Tomography and Digital Images in Uveitis

INTRODUCTION

Computer vision, understood as the area of science that trains computers to interpret digital images through both artificial intelligence (AI) and classical algorithms, has significantly advanced the analysis and interpretation of optical coherence tomography (OCT) in retina research. The aim of this review is to summarise the recent advances of computer vision in imaging processing in uveitis, with a particular focus in optical coherence tomography images.

MATERIAL AND METHODS

Literature review.

RESULTS

The development of computer vision to assist uveitis diagnosis and prognosis is still undergoing, but important efforts have been made in the field.

CONCLUSION

The automatising of image processing in uveitis could be fundamental to establish objective and standardised outcomes for future clinical trials. In addition, it could help to better understand the disease and its progression.