Textural Analysis and Effect of ROI Size on Infrared Thermography in Athletes With Patellar Tendinopathy. A Cross-sectional Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 54
- 试验地点
- 1
- 主要终点
- Textural analysis based on the Gray-Level Co-occurrence Matrix (GLCM)
研究概览
简要总结
Patellar tendinopathy (PT) is a common knee disorder, particularly among elite athletes, with a reported prevalence of approximately 14.2%. Athletes affected by PT may experience persistent pain, functional impairment, reduced quality of life, decreased physical performance, and even premature career termination. Diagnosing PT remains challenging due to the absence of a gold standard diagnostic method. Although imaging techniques such as ultrasonography (US) and magnetic resonance imaging (MRI) can aid in confirming the diagnosis and assessing severity, MRI is costly and less accessible, and US shows poor correlation with clinical symptoms. Consequently, diagnosis largely relies on clinical examination and medical history. Infrared thermography (IT) has emerged as a potential alternative imaging technique, offering a low-cost, reliable, and non-invasive method to detect thermal asymmetries indicative of underlying pathologies. Technological advancements have enhanced the precision of IT, reducing the thermal asymmetry threshold from 2-3 ºC in the 1970s to 0.5 ºC in current knee assessments. First-order statistics, such as mean gray intensity, and second-order features based on the gray-level co-occurrence matrix (GLCM), have been extensively used in medical image analysis, including IT, to quantify structural and textural characteristics. The size of the region of interest (ROI) is also a critical factor in thermal and texture analyses, as it can influence sensitivity and diagnostic accuracy. Given these considerations, the objectives of this study were: (1) to evaluate differences in thermal and GLCM-based textural features between athletes with PT and healthy controls; (2) to compare the diagnostic performance of IT and GLCM features applied to thermographic images; and (3) to identify the most appropriate ROI size for optimal characterization of PT using both thermal and textural analysis.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 65 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Specific functional tests.
- •Ultrasound evaluation.
- •Symptom evolution time of more than 3 months.
- •A VISA-P score of less than
- •The performance of a differential diagnosis to rule out other potential causes of anterior knee pain.
排除标准
- •Lower limb pathology.
- •Nerve or vascular disorder, or skin lesion in the knee area that could alter thermal information in the patellar tendon region.
结局指标
主要结局
Textural analysis based on the Gray-Level Co-occurrence Matrix (GLCM)
时间窗: baseline
GLCM relies on the angular relationship between neighboring pixels and the distance between them. relies on the angular relationship between neighboring pixels and the distance between them.
Energy or angular second moment (ASM)
时间窗: baseline
ASM Measures the uniformity or regularity in the distribution of image values. Higher values indicate greater uniformity in the image.
Homogeneity or inverse difference moment (IDM)
时间窗: baseline
IDM reflects the homogeneity of image composition, associated with pixel pairs. Homogeneous images with minimal variations produce high IDM values
Contrast (CON)
时间窗: baseline
CON represents the degree of local variations in gray levels within the image.If the variation increases, the contrast increases.
Textural correlation (TCOR)
时间窗: baseline
TCOR expresses linear dependencies between gray levels in the image. Regions with similar gray levels tend to exhibit higher values.
Entropy (ENT)
时间窗: baseline
Indicates the level of disorder within the image. Homogeneous images result in lower entropy values
次要结局
- Sex(baseline)
- Age (years)(baseline)
- time of evolution (months)(baseline)
- Body Mass index (BMI)(baseline)
研究者
SERGIO MONTERO NAVARRO
Clinical Professor
Cardenal Herrera University
