TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Berlin, Clara
A1  - Adomeit, Sonja
A1  - Grover, Priyanka
A1  - Dreischarf, Marcel
A1  - Halm, Henry
A1  - Dürr, Oliver
A1  - Obid, Peter
T1  - Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients
BT  - A Validation Study on 100 Preoperative Full Spine X-Rays
JF  - Global Spine Journal
N2  - 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.
KW  - Coronal balance
KW  - Artificial intelligence
KW  - Deep learning
KW  - Adolescent idiopathic scoliosis
KW  - X-ray
KW  - Spinal deformity
KW  - Surgical planning
KW  - Coronal alignment
Y1  - 2023
SN  - 2192-5682
SS  - 2192-5682
SN  - 2192-5690
SS  - 2192-5690
U6  - https://doi.org/10.1177/21925682231154543
DO  - https://doi.org/10.1177/21925682231154543
N1  - Corresponding author: Clara Berlin
SP  - 10
S1  - 10
PB  - SAGE Publications
ET  - OnlineFirst
ER  -