Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

ABSTRACT:

Purpose

To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained image categorisation using optical coherence tomography (OCT) images.

Methods

The development of the AI system consists of two parts, a pretraining process on a public dataset (Asia Pacific Tele-Ophthalmology Society (APTOS)), and the training process on the local dataset. The local dataset was partitioned into the training set, validation set and test set in the ratio of 6:2:2. The Split Subspace Attention Network (SSA-Net) architecture was adopted to train independent models to detect the seven pathological patterns of DME: intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), hyper-reflective reti

SUBMITTER: Ye X 

PROVIDER: S-EPMC12004464 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature