Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification.

ABSTRACT:

Background

Osteoarthritis (OA) is a global healthcare problem. The increasing population of OA patients demands a greater bandwidth of imaging and diagnostics. It is important to provide automatic and objective diagnostic techniques to address this challenge. This study demonstrates the utility of unsupervised domain adaptation (UDA) for automated OA phenotype classification.

Methods

We collected 318 and 960 three-dimensional double-echo steady-state magnetic resonance images from the Osteoarthritis Initiative (OAI) dataset as the source dataset for phenotype cartilage/meniscus and subchondral bone, respectively. Fifty three-dimensional turbo spin echo (TSE)/fast spin echo (FSE) MR images from our institute were collected as the target datasets. For each patient, the degree

SUBMITTER: Zhong J 

PROVIDER: S-EPMC10644135 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature