319 Development and Validation of an Artificial Intelligence Model to Accurately Predict Spinopelvic Parameters

OBJECTIVES/GOALS: The correction of spinopelvic parameters is associated with better outcomes in patients with adult spinal deformity (ASD). This study presents a novel artificial intelligence (AI) tool that automatically predicts spinopelvic parameters from spine x-rays with high accuracy and witho...

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Vydané v:Journal of clinical and translational science Ročník 8; číslo s1; s. 98
Hlavní autori: Harake, Edward S, Linzey, Joseph R., Jiang, Cheng, Jones, Jaes C., Joshi, Rushikesh, Zaki, Mark, Wilseck, Zachary, Joseph, Jacob, Hollon, Todd, Khalsa, Siri Sahib S., Park, Paul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cambridge Cambridge University Press 01.04.2024
Predmet:
Artificial intelligence
Informatics and Data Science
Population studies
Scholarships & fellowships
Spine (lumbar)
Surgeons
X-rays
ISSN:2059-8661, 2059-8661
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Shrnutí:OBJECTIVES/GOALS: The correction of spinopelvic parameters is associated with better outcomes in patients with adult spinal deformity (ASD). This study presents a novel artificial intelligence (AI) tool that automatically predicts spinopelvic parameters from spine x-rays with high accuracy and without need for any manual entry. METHODS/STUDY POPULATION: The AI model was trained/validated on 761 sagittal whole-spine x-rays to predict the following parameters: Sagittal Vertical Axis (SVA), Pelvic Tilt (PT), Pelvic Incidence (PI), Sacral Slope (SS), Lumbar Lordosis (LL), T1-Pelvic Angle (T1PA), and L1-Pelvic Angle (L1PA). A separate test set of 40 x-rays was labeled by 4 reviewers including fellowship-trained spine surgeons and a neuroradiologist. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test and cropped-test (i.e. lumbosacral) images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability RESULTS/ANTICIPATED RESULTS: The AI model exhibited the following median (IQR) parameter errors: SVA[2.1mm (8.5mm), p=0.97], PT [1.5° (1.4°), p=0.52], PI[2.3° (2.4°), p=0.27], SS[1.7° (2.2°), p=0.64], LL [2.6° (4.0°), p=0.89], T1PA [1.3° (1.1°), p=0.41], and L1PA [1.3° (1.2°), p=0.51]. The parameter errors on cropped lumbosacral images were: LL[2.9° (2.6°), p=0.80] and SS[1.9° (2.2°), p=0.78]. The AI model exhibited excellent reliability at all parameters in both whole-spine (ICC: 0.92-1.0) and lumbosacral x-rays: (ICC: 0.92-0.93). DISCUSSION/SIGNIFICANCE: Our AI model accurately predicts spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spine-imaging can substantially aid in patient selection and surgical planning.
Bibliografia:ObjectType-Article-1
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ISSN:2059-8661
2059-8661
DOI:10.1017/cts.2024.289