Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

Abstract

<p>Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs.</p>

Artificial Versus Human Intelligence in the Diagnostic Approach of Ophthalmic Case Scenarios: A Qualitative Evaluation of Performance and Consistency

“…Similarly, ChatGPT-4 outperformed glaucoma specialists and was comparable with retina specialists in diagnostic and treatment accuracy of glaucoma and retina cases [8]. By contrast, ChatGPT exhibited reasonable but inferior diagnostic accuracy than human experts in cornea [9], uveitis [10,11], and neuro-ophthalmology [12] cases. Furthermore, in another study, performance of ChatGPT-3.5 in diagnosing hospitalized ophthalmic patients with various, sometimes complex, eye conditions was poorer than that of residents and attending ophthalmologists [13].…”

Medical ChatGPT – A systematic Meta-Review

“…They allow researchers to quickly grasp the key aspects of a topic without having to sift through numerous individual studies. The increasing popularity of ChatGPT among healthcare professionals and researchers alike leads to a surge in research focusing on the evaluation of ChatGPT's performance in different application scenarios, such as medical consultation [1], research [2], education [3], or different medical specialties, such as neurology [4,5], pediatric [6,7], cosmetic surgery [8,9] and dermatology [10,11]. Each of these fields presents unique challenges and opportunities for ChatGPT.…”