Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images.

ABSTRACT:

Aim

To evaluate the efficacy of automated machine learning (AutoML) models in detecting fundus diseases using ocular B-scan ultrasound images.

Methods

Ophthalmologists annotated two B-scan ultrasound image datasets to develop three AutoML models-single-label, multi-class single-label and multi-label-on the Vertex artificial intelligence (AI) platform. Performance of these models was compared among themselves and against existing bespoke models for binary classification tasks.

Results

The training set involved 3938 images from 1378 patients, while batch predictions used an additional set of 336 images from 180 patients. The single-label AutoML model, trained on normal and abnormal fundus images, achieved an area under the precision-recall curve (AUPRC) of 0.9943. The m

SUBMITTER: Wei Q 

PROVIDER: S-EPMC11647328 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature