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. 2024 Jul;14(6):1728-1737.
doi: 10.1177/21925682231154543. Epub 2023 Jan 28.

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

Clara Berlin  1 , Sonja Adomeit  2 , Priyanka Grover  3 , Marcel Dreischarf  3 , Henry Halm  1 , Oliver Dürr  3 , Peter Obid  4
Affiliations

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

Clara Berlin et al. Global Spine J. 2024 Jul.

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.

Keywords: X-ray; adolescent idiopathic scoliosis; artificial intelligence; coronal alignment; coronal balance; deep learning; spinal deformity; surgical planning.

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Conflict of interest statement

Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Schematic representation of the pipeline of the presented algorithm showing an AP full spine X-ray as input (left); segmentation of anatomical structures of interest (middle left); computation of parameters based on the segmentation results (middle right); visualization of the coronal parameters (right).
Figure 2.
Figure 2.
(a) Midpoints of all VB segmentations; (b) spline fit through midpoints; (c) extremum (blue) and points of inflection (yellow) derived from the spline fit and considered end vertebra according to the Lenke Cobb area classification (green).
Figure 3.
Figure 3.
Scatterplots exemplarily displaying the correlation between the measurements of rater 1A and the AI-algorithm for all 100 evaluations.
See this image and copyright information in PMC

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