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新型人工智能算法:一种准确且独立的脊柱骨盆参数测量方法。

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

机构信息

1Department of Research, National Spine Health Foundation, Reston.

2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia.

出版信息

J Neurosurg Spine. 2022 Jul 8;37(6):893-901. doi: 10.3171/2022.5.SPINE22109. Print 2022 Dec 1.

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.

摘要

目的

通过测量脊柱骨盆参数对矢状位平衡进行分析,这一方法已在全球脊柱外科医生中得到广泛应用,而矢状位失衡是导致生活质量下降的一个公认原因。这些测量既耗时又必要,因此越来越需要一种自动分析工具,该工具能够快速、精确和可重复地测量脊柱骨盆参数,而无需依赖用户输入。本研究介绍并评估了一种基于人工智能(AI)的算法,该算法可全自动测量脊柱骨盆参数。

方法

回顾性分析了 100 例接受腰椎融合术患者的 200 例侧位腰椎 X 线片(术前和术后图像),由脊柱外科医生进行数字化测量,包括腰椎前凸角、骨盆入射角、骨盆倾斜角和骶骨倾斜角。还使用新的 AI 算法来测量相同的参数。为了评估人工测量与 AI 自动测量之间的一致性,计算了平均误差(95%置信区间,标准差),并使用 2 路随机单测量组内相关系数(ICC)评估了组内可靠性。ICC 值大于 0.75 被认为是优秀的。

结果

AI 算法能够在 98%的术前图像和 95%的术后图像中确定所有参数,ICC 值非常高(术前范围为 0.85-0.92,术后范围为 0.81-0.87)。骨盆入射角的平均误差在术前和术后都最小(术前为-0.5°[95%置信区间-1.5°至 0.6°],术后为 0.0°[95%置信区间-1.1°至 1.2°]),而骶骨倾斜角在术前最大(-2.2°[95%置信区间-3.0°至-1.5°]),腰椎前凸角在术后最大(3.8°[95%置信区间 2.5°至 5.0°])。

结论

人工智能的进步转化为医学影像学分析领域。这种在脊柱 X 光片上测量脊柱骨盆参数的方法具有与专家人类评估者相当的可靠性。这种应用允许用户自动准确地获得关键的脊柱骨盆测量值,可应用于临床实践。该解决方案可以通过节省医生的日常工作时间和避免易错的手动测量来帮助医生。

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