Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination.

Holden Archer¹, Seth Reine¹, Shuda Xia¹, Louis Camilo Vazquez¹, Oganes Ashikyan¹, Parham Pezeshk¹, Ajay Kohli¹, Yin Xi¹, Joel E Wells², Allan Hummer³, Matthew Difranco³, Avneesh Chhabra^4,5,6,7

¹University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.

²Baylor, Scott, & White, Dallas, TX, USA.

³IB Lab GmbH, Zehetnergasse 6/2/2, 1140, Vienna, Austria.

⁴University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA. avneesh.chhabra@utsouthwestern.edu.

⁵Adjunct Faculty, Johns Hopkins University, Baltimore, MD, USA. avneesh.chhabra@utsouthwestern.edu.

⁶University of Dallas, Richardson, TX, USA. avneesh.chhabra@utsouthwestern.edu.

⁷Walton Centre for Neurosciences, Liverpool, UK. avneesh.chhabra@utsouthwestern.edu.

PubMed DOI

Abstract

PURPOSE: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to ass…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.