בשל "הגנת זכויות יוצרים", מובא להלן קישור למאמר בלבד. לקריאתו בטקסט מלא, אנא פנה לספרייה הרפואית הזמינה לך.

A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.

Objective

To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.

Main Outcomes and Measures 

Area under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders.

Conclusions and Relevance 

Application of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON.

These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.