Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays

Author:

Berlin Clara1ORCID,Adomeit Sonja2,Grover Priyanka3,Dreischarf Marcel3,Halm Henry1,Dürr Oliver3,Obid Peter4

Affiliation:

1. Spine Surgery and Scoliosis Center,Schön Klinik Neustadt, Germany

2. Heidelberg University, Interdisciplinary Center for Scientific Computing, Germany

3. Research and Development, RAYLYTIC GmbH, Germany

4. Department of Orthopaedics and Traumatology, Freiburg University Hospital, Germany

Abstract

Study design Retrospective, mono-centric cohort research study. Objectives The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS). Methods An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics. Results The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°). Conclusions The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.

Publisher

SAGE Publications

Subject

Neurology (clinical),Orthopedics and Sports Medicine,Surgery

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