A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance. There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively. The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively. A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia. Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.

Russakoff DB ，Mannil SS ，Oakley JD ，Ran AR ，Cheung CY ，Dasari S ，Riyazzuddin M ，Nagaraj S ，Rao HL ，Chang D ，Chang RT ... -  《Translational Vision Science & Technology》

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

Noury E ，Mannil SS ，Chang RT ，Ran AR ，Cheung CY ，Thapa SS ，Rao HL ，Dasari S ，Riyazuddin M ，Chang D ，Nagaraj S ，Tham CC ，Zadeh R ... -  《Translational Vision Science & Technology》

The ability of macular parameters and circumpapillary retinal nerve fiber layer by three SD-OCT instruments to diagnose highly myopic glaucoma.

Akashi A ，Kanamori A ，Nakamura M ，Fujihara M ，Yamada Y ，Negi A ... -  《-》

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

Thompson AC ，Jammal AA ，Berchuck SI ，Mariottoni EB ，Medeiros FA ... -  《-》

A normative database of A-scan data using the Heidelberg Spectralis Spectral Domain Optical Coherence Tomography machine.

Meyer J ，Karri R ，Danesh-Meyer H ，Drummond K ，Symons A ... -  《PLoS One》