Multidimensional imaging model for diagnosing primary open-angle glaucoma using fundus photographs and optical coherence tomography images.

ABSTRACT:

Aims

To develop a multidimensional imaging model to detect primary open-angle glaucoma (POAG) by combining fundus photographs with the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) thickness maps and deviation maps.

Methods

1054 eyes of 546 patients were included. Images were randomly divided into training, validation and test datasets at 64%, 16% and 20%. Four basic convolutional neural network algorithms were used to develop the multidimensional imaging model by combining fundus photographs, RNFL and GCIPL deviation maps, with RNFL and GCIPL thickness maps. POAG structure damage was defined as RNFL and GCIPL thinning with neuroretinal rim thinning.

Results

Multidimensional imaging model obtained an area under the receiver operating

SUBMITTER: Qiu J 

PROVIDER: S-EPMC12551473 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature