Abstract：
OBJECTIVE: Ultrasonography for scoliosis is a novel imaging method that does not expose children with adolescent idiopathic scoliosis (AIS) to radiation. A single ultrasound scan provides 3D spinal views directly. However, measuring ultrasonograph parameters is challenging, time-consuming, and requires considerable training. This study aimed to validate a machine learning method to measure the coronal curve angle on ultrasonographs automatically.
METHODS: A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed.
RESULTS: The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7 s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement.
CONCLUSIONS: The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm\'s quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.
METHODS: A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed.
RESULTS: The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7 s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement.
CONCLUSIONS: The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm\'s quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.
摘要：
目的：脊柱侧凸的超声检查是一种新颖的成像方法，不会使患有青少年特发性脊柱侧凸（AIS）的儿童暴露于辐射。单个超声扫描直接提供3D脊柱视图。然而,测量超声参数是具有挑战性的，耗时,需要相当多的培训。本研究旨在验证一种机器学习方法在超声图像上自动测量冠状曲线角度。
方法：总共提取了144张三维脊柱超声图，以训练和验证机器学习模型。在144张图片中，70个用于训练，74条包括144条测试曲线。通过将其与经验丰富的评估者进行的手动测量进行比较来验证自动冠状曲线角度测量。方法间组内相关系数(ICC2,1)，测量标准误差(SEM)，分析了在临床接受范围内（≤5°）的测量百分比。
结果：自动方法检测到125/144手动测量的曲线。125次手动和自动冠状曲线角度测量的平均值分别为22.4±8.0°和22.9±8.7°,分别。使用ICC2,1=0.81和SEM=1.4°实现了良好的可靠性。总共75%(94/125)的测量在临床接受范围内。每个超声仪的平均测量时间为36±7s。此外，该算法显示了预测的薄片中心来说明测量结果。
结论：自动算法测量冠状曲线角度的准确性适中，但可靠性良好。该算法的快速测量时间和可解释性可以使超声成为AIS儿童更易于访问的成像方法。然而,需要进一步改进才能将该方法用于临床。
方法：总共提取了144张三维脊柱超声图，以训练和验证机器学习模型。在144张图片中，70个用于训练，74条包括144条测试曲线。通过将其与经验丰富的评估者进行的手动测量进行比较来验证自动冠状曲线角度测量。方法间组内相关系数(ICC2,1)，测量标准误差(SEM)，分析了在临床接受范围内（≤5°）的测量百分比。
结果：自动方法检测到125/144手动测量的曲线。125次手动和自动冠状曲线角度测量的平均值分别为22.4±8.0°和22.9±8.7°,分别。使用ICC2,1=0.81和SEM=1.4°实现了良好的可靠性。总共75%(94/125)的测量在临床接受范围内。每个超声仪的平均测量时间为36±7s。此外，该算法显示了预测的薄片中心来说明测量结果。
结论：自动算法测量冠状曲线角度的准确性适中，但可靠性良好。该算法的快速测量时间和可解释性可以使超声成为AIS儿童更易于访问的成像方法。然而,需要进一步改进才能将该方法用于临床。
