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Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition
Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition
REVIEWED BY Lisa McGuire, AFASA | ASA SIG: Emerging Technologies
REFERENCE | Authors: Ruey-Feng Chang, Chung-Chien Lee, Chung-Ming Lo
WHY THE STUDY WAS PERFORMED
The accurate diagnosis of rotator cuff disorders is important to determine treatment strategy. Ultrasound is operator dependent with a high interoperative variability. This study used a computer-aided tear classification system (CTC) to identify supraspinatus tears and reduce interoperator variability. Computer-aided diagnosis systems provide an objective, quantitative assessment of lesion type and grade.
HOW THE STUDY WAS PERFORMED
Data was accumulated from 136 patients. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 images of supraspinatus tears. Each case looked at each lesion with manual or semi-automatic segmentation. Quantitative features including texture and intensity were extracted and combined in a binary logistic regression classifier for lesion classification. All shoulders were imaged using an ALOKA alpha-6 ultrasound scanner. The acquisition frequency used an 8 MHz and a 4 cm scanning depth focusing on the supraspinatus. Forty-two of the 102 supraspinatus tears were full thickness tears. Ultrasound examinations were performed uniformly.
“The proposed computer-aided tear classification (CTC) system achieved an accuracy rate of 92%. Based on its diagnostic performance, the CTC system has promise for clinical use.”
Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition
INTENSITY
The grey-scale distribution of tissues in the lesion were presented by a probability distribution in the form of a histogram:
Mean: summarises the total pixel values and divides by the sum of the pixel number Variance: indicates how uniform the greyscale values are spread out Skewness: estimates symmetry of the value distribution such as bias to one side or not
Kurtosis: sharpness of a peak of a frequency distribution curve (taking normal distribution as a reference).
TEXTURE
Tendinopathies have heterogenous patterns, tears appear to have hypoechoic echogenicities. The grey-scale intensities of echogenicities and statistical correlation between pixel values provide information for differentiation of lesion types. In this study, the grey-scale co-occurrence matrices were proposed as features. The statistics revealed correlations between adjacent pixels with a different combination of grey scales. For diagnosis, 8 levels were quantified and used for pattern interpretation.
ANALYSIS
Non-normally distributed features were evaluated with a Mann-Whitney U-test. The resulting p-values < 0.05 indicated whether a feature was significant in distinguishing between the supraspinatus tendinopathy and tear.
Tear probability = 1/(1+exp(-1x(f1 xc1+…f n xC n – constant))) f1, f n are different features which multiply different C1,Cn as coefficients. Tear classification results with probabilities higher than 50% were classified to be a tear.
WHAT THE STUDY FOUND
In the performance evaluation, each case was given a probability indicating the likelihood of tears. Cases with probability values ≥ 0.5 were classified as tears, those < 0.5 were classified as tendinopathy.
The prediction model built by a logistic regression classifier achieved an accuracy of 92% for identifying rotator cuff tears and tendinopathies with sensitivity and specificity both equalling 93%.
RELEVANCE TO CLINICAL PRACTICE
The CTC system’s high accuracy suggests it will be useful for assessing the presence of rotator cuff tears. This would enhance current diagnostic practice. The proposed CTC system performed similar performance accuracy to that of experienced operators, including musculoskeletal radiologists and orthopaedic shoulder surgeons. It is proposed that interobserver variability would be improved with the introduction of the CTC system.
