Asset Details
Do you wish to reserve the book?
Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities
Do you wish to request the book?
Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities
2024
Overview
Introduction
The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader’s expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs.
Materials and methods
In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland–Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA.
Results
In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81–0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95–0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, − 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, − 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort.
Conclusion
he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results.
Level of evidence
Diagnostic Level III.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
Subject
