Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements.

Holden Archer¹, Seth Reine², Shuda Xia², Louis Camilo Vazquez², Oganes Ashikyan², Parham Pezeshk², Ajay Kohli², Yin Xi², Joel E Wells³, Allan Hummer⁴, Matthew Difranco⁴, Avneesh Chhabra⁵

¹University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. Electronic address: https://twitter.com/@HoldenArcher.

²University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA.

³Baylor Scott & White, 5220 W University Dr, McKinney, TX 75071, USA. Electronic address: https://twitter.com/@Joelwellsmd.

⁴IB Lab GmbH, Zehetnergasse 6/2/2, 1140 Vienna, Austria.

⁵University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. Electronic address: avneesh.chhabra@utsouthwestern.edu.

PubMed DOI

Abstract

PURPOSE: Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI to…

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.