Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone

Abstract

Background/aimsTo validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.MethodsA training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC).ResultsThe AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < −12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras.ConclusionThe usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.

Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study

“…Smartphones are increasingly used in clinical settings to provide high quality images ( 35 , 36 ). Coupled with DL algorithms, smartphones may be used for detecting ocular diseases.…”

“…Coupled with DL algorithms, smartphones may be used for detecting ocular diseases. For example, smartphone based anterior segment photographs and retinal images for cataract grading, glaucoma and diabetic retinopathy detection have been reported ( 12 , 36 , 37 ). There are plenty of advantages for smartphones used in clinics.…”

Recent developments of telemedicine in glaucoma

“…One study looked into whether smartphone-based fundus photographs could be analyzed using deep learning to diagnose glaucoma [33 ▪▪ ]. A total of 3132 images taken by a fundus camera (Nonmyd WX-3D [Kowa Company, Aichi, Japan]) were used as the training set.…”

The application of artificial intelligence in glaucoma diagnosis and prediction

“…Experts took advantage of VF and optic disc information from 1,636 participants collected by the ocular hypertension treatment study over an average of 10 years to train the ResNet50 model and achieve high specificity on the test set beyond the study endpoint committee. To facilitate patient applications, DL algorithms have also been developed for smartphones using ResNet6 ( Nakahara et al, 2022 ), which requires an accompanying D-Eye lens for fundus photo capture. Although it has some effectiveness in advanced glaucoma, its usage requires the flash to be continuously lit for 1 min against a dilated eye, which needs to be updated.…”