Albarova-Corral Isabel, Segovia-Burillo José, Malo-Urriés Miguel, Ríos-Asín Izarbe, Asín Jesús, Castillo-Mateo Jorge, Gracia-Tabuenca Zeus, Morales-Hernández Mario
PhysiUZerapy Health Sciences Research Group, Department of Physiatry and Nursing, University of Zaragoza, 50009 Zaragoza, Spain.
Fluid Mechanics, Instituto de Investigación en Ingeniería de Aragón (I3A), University of Zaragoza, 50018 Zaragoza, Spain.
Diagnostics (Basel). 2024 May 21;14(11):1067. doi: 10.3390/diagnostics14111067.
Ultrasound is widely used for tendon assessment due to its safety, affordability, and portability, but its subjective nature poses challenges. This study aimed to develop a new quantitative analysis tool based on artificial intelligence to identify statistical patterns of healthy and pathological tendons. Furthermore, we aimed to validate this new tool by comparing it to experts' subjective assessments. A pilot database including healthy controls and patients with patellar tendinopathy was constructed, involving 14 participants with asymptomatic ( = 7) and symptomatic ( = 7) patellar tendons. Ultrasonographic images were assessed twice, utilizing both the new quantitative tool and the subjective scoring method applied by an expert across five regions of interest. The database contained 61 variables per image. The robustness of the clinical and quantitative assessments was tested via reliability analyses. Lastly, the prediction accuracy of the quantitative features was tested via cross-validated generalized linear mixed-effects logistic regressions. These analyses showed high reliability for quantitative variables related to "Bone" and "Quality", with ICCs above 0.75. The ICCs for "Edges" and "Thickness" varied but mostly exceeded 0.75. The results of this study show that certain quantitative variables are capable of predicting an expert's subjective assessment with generally high cross-validated AUC scores. A new quantitative tool for the ultrasonographic assessment of the tendon was designed. This system is shown to be a reliable and valid method for evaluating the patellar tendon structure.
由于超声具有安全性、可承受性和便携性，因此被广泛用于肌腱评估，但其主观性带来了挑战。本研究旨在开发一种基于人工智能的新型定量分析工具，以识别健康和病理性肌腱的统计模式。此外，我们旨在通过将这种新工具与专家的主观评估进行比较来验证它。构建了一个包括健康对照者和髌腱病患者的试点数据库，涉及14名参与者，其中无症状髌腱者（n = 7）和有症状髌腱者（n = 7）。超声图像通过新的定量工具和专家在五个感兴趣区域应用的主观评分方法进行了两次评估。该数据库每张图像包含61个变量。通过可靠性分析测试了临床和定量评估的稳健性。最后，通过交叉验证的广义线性混合效应逻辑回归测试了定量特征的预测准确性。这些分析表明，与“骨骼”和“质量”相关的定量变量具有高可靠性，组内相关系数（ICC）高于0.75。“边缘”和“厚度”的ICC有所不同，但大多超过0.75。本研究结果表明，某些定量变量能够以通常较高的交叉验证曲线下面积（AUC）分数预测专家的主观评估。设计了一种用于肌腱超声评估的新型定量工具。该系统被证明是评估髌腱结构的一种可靠且有效的方法。
