Luera Micheal J, Shields JoCarol E, Bozarth Emma, MacLennan Rob J, Walker Natalie P, Hernandez-Sarabia Jesus A, Estrada Carlos A, DeFreitas Jason M, Crawford Scott K
Department of Neuroscience, Tarleton State University, Stephenville, TX, United States.
Department of Health and Human Performance, Tarleton State University, Stephenville, TX, United States.
Front Rehabil Sci. 2025 Apr 3;6:1539804. doi: 10.3389/fresc.2025.1539804. eCollection 2025.
Ultrasound is a cost-effective and reliable method to determine skeletal muscle architecture. However, manual analysis of fascicle length (FL) and pennation angle (PA) can be arduous and subjective among raters, particularly among novice raters. Alternatives to manual processing have been proposed that expedite the evaluation of muscle architecture and afford more consistency. While using algorithms has provided dependable results of muscle architecture, it has often focused on variables of passive range of motion and submaximal contractions. To fully understand the impact of muscle architecture using semi-automated analysis, an investigation of a broad range of contraction intensities is needed. The purpose of this study was to develop and determine the intra-rater and inter-rater reliability of a custom, semi-automated algorithm to extract measures of muscle thickness, pennation angle, and fascicle length, and second to compare the semi-automated measures to measures extracted manually from the same novice raters while accounting for differences between contraction intensities.
Fifteen resistance-trained individuals (male: = 6, female: = 9) completed this study. Images were collected during four contraction intensities relative to maximal voluntary isometric contractions (MVIC) (at rest, 30%, 70%, and MVIC) and analyzed by three novice raters to compare the semi-automated algorithm and manual measurement in the vastus lateralis.
Intra-rater reliability for manual measures was poor for FL (ICCs: 0-0.30), poor to good for PA (ICCs: 0.46-0.77), and moderate to good for muscle thickness (MT) (ICCs: 0.55-0.84). For the semi-automated algorithm, the intra-rater reliability was good to excellent for FL (range: 0.90-0.99), PA (range: 0.88-0.99), and MT (range: 0.996-0.999) across all contraction intensities.
The findings of this study suggest that the reliability of manual measurement is lower when novice raters perform image analyses compared to the semi-automated method. Therefore, careful consideration and training should be provided when considering manual assessment of muscle architecture values, and standardized identification methods and features in algorithm development may be a better method for reproducibility.
超声是一种经济高效且可靠的确定骨骼肌结构的方法。然而，手动分析肌束长度（FL）和羽状角（PA）在评估者之间可能既费力又主观，尤其是在新手评估者中。已经提出了手动处理的替代方法，这些方法可以加快肌肉结构的评估并提供更高的一致性。虽然使用算法已经提供了可靠的肌肉结构结果，但它通常侧重于被动运动范围和次最大收缩的变量。为了使用半自动分析全面了解肌肉结构的影响，需要对广泛的收缩强度进行研究。本研究的目的是开发并确定一种自定义的半自动算法在提取肌肉厚度、羽状角和肌束长度测量值时的评估者内和评估者间可靠性，其次是在考虑收缩强度差异的情况下，将半自动测量值与同一新手评估者手动提取的测量值进行比较。
15名进行抗阻训练的个体（男性 = 6名，女性 = 9名）完成了本研究。在相对于最大自主等长收缩（MVIC）的四种收缩强度（静息、30%、70%和MVIC）下采集图像，并由三名新手评估者进行分析，以比较股外侧肌的半自动算法和手动测量。
手动测量的评估者内可靠性对于FL较差（组内相关系数：0 - 0.30），对于PA从差到良好（组内相关系数：0.46 - 0.77），对于肌肉厚度（MT）为中等至良好（组内相关系数：0.55 - 0.84）。对于半自动算法，在所有收缩强度下，评估者内可靠性对于FL良好至优秀（范围：0.90 - 0.99），对于PA（范围：0.88 - 0.99），对于MT（范围：0.996 - 0.999）。
本研究结果表明，与半自动方法相比，新手评估者进行图像分析时手动测量的可靠性较低。因此，在考虑手动评估肌肉结构值时应给予仔细考虑和培训，并且算法开发中的标准化识别方法和特征可能是实现可重复性的更好方法。
