Editors Selection IGR 20-1

Miscellaneous: Artificial Intelligence aided Glaucoma Diagnosis

Naama Hammel
Sonia Phene

Comment by Naama Hammel & Sonia Phene on:

79224 Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images, Asaoka R; Murata H; Hirasawa K et al., American Journal of Ophthalmology, 2019; 198: 136-145

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Deep learning algorithms have been applied to produce highly accurate systems that can detect various eye conditions from fundus images,1,2 as well as optical coherence tomography (OCT) scans.3,4 Both clinically and in research settings, OCT measurements of the inner layers of the retina (including the ganglion cell layer) are used to quantify glaucomatous damage of the macula ‐ damage that may detectable via OCT in the early stages of the disease.5

In the setting of sufficient data, DLSs are more accurate than more classical techniques

Asaoka and colleagues developed a deep learning system (DLS) to distinguish early-onset glaucoma from normal eyes using macular OCT scans.Their main finding was that a DLS that uses an 8 x 8 grid macular retinal nerve fiber layer (RNFL) thickness and ganglion cell complex (GCC) layer thickness from OCT can achieve an area under receiving-operating characteristic curve (AUC) of 93.0%. They further validated that the DLS performed better than two traditional machine learning techniques, the support vector machine and the random forest. These findings validate a generally-accepted sentiment in the machine learning community6 for medical imaging: in the setting of sufficient data, DLSs are more accurate than more classical techniques.

The major limitations of the study are the size of the data set and the exclusion of difficult cases. A paper describing a DLS for the detection of DR from fundus photos found that the minimum number of images required for development was over 50,000.1 Asaoka and colleagues had a 'pre-training' data set of 4,316 OCTs, while the test set consisted of only 114 patients with early open-angle glaucoma and 82 normal patients. Thus, we postulate that the DLS developed by the authors could potentially be improved further with additional training data. In addition, the authors excluded difficult images, such as ones with tilted discs, from the training and test sets. Because such images are not uncommon in routine clinical workflows, exclusion criteria such as this may limit the ability to extrapolate the model's performance to the general clinical setting.

To conclude, Asaoka et al. have made solid initial steps to applying DLS to OCTs, which contain crucial information for diagnosing and managing glaucoma. Additional work will be needed to further validate their findings on larger datasets of a clinically diverse patient population and breadth of images.


  1. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA; 2016;316(22):2402-2410.
  2. Ting DSW, Cheung CY-L, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multi-ethnic Populations With Diabetes. JAMA 2017;318(22):2211-2223.
  3. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24(9):1342-1350.
  4. Kermany DS, Goldbaum M, Cai W, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 2018;172(5):1122-1131.e9.
  5. Hood DC, Raza AS, de Moraes CGV, Liebmann JM, Ritch R. Glaucomatous damage of the macula. Progr Retin Eye Res 2013;32:1-21. doi: 10.1016/j. preteyeres.2012.08.003
  6. Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comp Vis 2015;115(3):211-252. doi: 10.1007/s11263-015-0816-y

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