Novel artificial intelligence algorithm: an accurate and independent measure of spinopelvic parameters

Abstract

OBJECTIVE The analysis of sagittal alignment by measuring spinopelvic parameters has been widely adopted among spine surgeons globally, and sagittal imbalance is a well-documented cause of poor quality of life. These measurements are time-consuming but necessary to make, which creates a growing need for an automated analysis tool that measures spinopelvic parameters with speed, precision, and reproducibility without relying on user input. This study introduces and evaluates an algorithm based on artificial intelligence (AI) that fully automatically measures spinopelvic parameters. METHODS Two hundred lateral lumbar radiographs (pre- and postoperative images from 100 patients undergoing lumbar fusion) were retrospectively analyzed by board-certified spine surgeons who digitally measured lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope. The novel AI algorithm was also used to measure the same parameters. To evaluate the agreement between human and AI-automated measurements, the mean error (95% CI, SD) was calculated and interrater reliability was assessed using the 2-way random single-measure intraclass correlation coefficient (ICC). ICC values larger than 0.75 were considered excellent. RESULTS The AI algorithm determined all parameters in 98% of preoperative and in 95% of postoperative images with excellent ICC values (preoperative range 0.85–0.92, postoperative range 0.81–0.87). The mean errors were smallest for pelvic incidence both pre- and postoperatively (preoperatively −0.5° [95% CI −1.5° to 0.6°] and postoperatively 0.0° [95% CI −1.1° to 1.2°]) and largest preoperatively for sacral slope (−2.2° [95% CI −3.0° to −1.5°]) and postoperatively for lumbar lordosis (3.8° [95% CI 2.5° to 5.0°]). CONCLUSIONS Advancements in AI translate to the arena of medical imaging analysis. This method of measuring spinopelvic parameters on spine radiographs has excellent reliability comparable to expert human raters. This application allows users to accurately obtain critical spinopelvic measurements automatically, which can be applied to clinical practice. This solution can assist physicians by saving time in routine work and by avoiding error-prone manual measurements.

Analysis of sagittal parameters for easier and more accurate determination of cervical spine alignment

“…Many recent studies have been conducted on the development of AI-based software to automatically measure or assist in the numerical measurement of spine balance parameters to reduce the effort of manual measurements. [ 17 , 18 ] Considering this, our study may potentially provide the basis for such studies.…”

Proximal Junctional Failure in Adult Spinal Deformity Surgery: An In-depth Review

“…Artificial intelligence (AI) holds great promise in the management of spinal conditions such as PJK and PJF. Recent research has been focused on leveraging AI for predictive analysis, imaging assessment, and personalized treatment planning [ 74 - 79 ].…”

“…Similarly, Orosz et al [ 79 ] introduced an AI algorithm that accurately measures spinopelvic parameters on lumbar radiographs. The AI measurements displayed excellent interrater reliability and provided precise measurements, proving it to be a valuable tool for clinical practice and research.…”

AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation

“…By leveraging the capabilities of AI, such as machine learning and computer vision, it may be possible to enhance the consistency and accuracy of measurements, reduce variability between examiners and institutions, and ultimately improve patient outcomes. Furthermore, AI-based systems could offer real-time analysis and support decision-making processes, enabling clinicians to more effectively diagnose, treat, and manage spinal disorders [10].…”