Estimating time-to-total knee replacement on radiographs and MRI: a multimodal approach using self-supervised deep learning.

ABSTRACT:

Purpose

Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions. A survival analysis model for predicting time-to-TKR was developed using features from medical images and clinical measurements.

Methods

From the Osteoarthritis Initiative dataset, all knees with clinical variables, MRI scans, radiographs, and quantitative and semiquantitative assessments from images were identified. This resulted in 895 knees that under

SUBMITTER: Cigdem O 

PROVIDER: S-EPMC11687945 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature