RESUMO
PURPOSE: Selective retina therapy (SRT) is a laser treatment targeting specific posterior retinal layers. It is focused on inducing damage to the retinal pigment epithelium (RPE), while sparing other retinal tissue compared to traditional photocoagulation. However, the targeted RPE layer is invisible with most imaging modalities and induced SRT lesions cannot be monitored. In this work, imaging scans acquired from an experimental setup that couples the SRT laser beam with an optical coherence tomography (OCT) beam are analyzed in order to evaluate the treatment as they occur. METHODS: We isolated a small part of the time-resolved scan corresponding to the end of the treatment, for which we have microscopic evidence of the SRT outcome. We then use a convolutional neural network to correspond each scan to the treatment result. We explore which aspects of the scan convey more valuable information for a robust therapy evaluation. By only using this adequately small part, we can achieve an online estimation, while being resilient to eye movement. RESULTS: The available dataset consists of time- resolved OCT scans of 98 ex vivo porcine eyes, treated with different energy levels. The proposed method yields high performance in the task of predicting whether the applied energy was adequate for SRT treatment, by focusing on the immediate OCT signal acquired during treatment time. CONCLUSIONS: We propose a strategy toward online noninvasive SRT treatment assessment, able to provide a satisfying evaluation of a treatment status, that therefore could be used for the planning of the treatment continuation.
