Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip

Author:

Stotter Christoph12,Klestil Thomas12,Röder Christoph1ORCID,Reuter Philippe1,Chen Kenneth12,Emprechtinger Robert2,Hummer Allan3,Salzlechner Christoph3,DiFranco Matthew3ORCID,Nehrer Stefan2ORCID

Affiliation:

1. Department for Orthopedics and Traumatology, Landesklinikum Baden-Mödling, 2340 Mödling, Austria

2. Department for Health Sciences, Medicine and Research, University for Continuing Education Krems, 3500 Krems, Austria

3. ImageBiopsy Lab, 1140 Vienna, Austria

Abstract

The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders.

Funder

Gesellschaft für Forschungsförderung Niederösterreich m.b.H.

Publisher

MDPI AG

Subject

Clinical Biochemistry

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