Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Abstract

To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them. Methods:Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions. Results:The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region. Conclusions:Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell-inner plexiform layer images of SD-OCT.Translational Relevance: This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

“…However, they employed an Inception-v3 CNN instead of an SVM to predict the 24-2 VF map, reporting a root mean squared error (RMSE) of 4.79 dB across the 24-2 map. A follow-up study by Shin and colleagues 42 investigated the advantage of thickness maps generated by swept-source OCT (SS-OCT) versus spectral domain OCT (SD-OCT). RMSE was significantly lower at 4.51 dB for SS-OCT when compared to 5.29 dB for SD-OCT.…”

Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma

“…Although DL has been used for diagnosing glaucoma in several studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 27 , 28 , 29 , 30 , 31 ], only a few have applied DL to UWF fundus images [ 31 ]. To the best of our knowledge, our study is the first to apply DL to true-colour confocal scanning images for diagnosing glaucoma.…”

“…Many efforts have been made to incorporate deep learning (DL) into various imaging methods for diagnosing ophthalmologic diseases [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. There are many reports on the application of DL methods to conventional fundus imaging; [ 16 , 26 ] recently, it was reported that when optical coherence tomography (OCT) and DL methods are combined, excellent glaucoma diagnostic results can be achieved [ 27 , 28 , 29 ].…”

A review of deep learning in structure and function in glaucoma

“…Subsequent studies from the same group compared different architectures for the DL model and different OCT technologies (SD and swept-source [SS] OCT). 16,17 Among the architectures tested, Inception-ResNet-v2 was superior to Inception-v3 and Inception-v4. Although the prediction errors were significantly lower, they were probably not clinically relevant.…”