Reliability of muscle stiffness measures in popliteus, medial and lateral gastrocnemius muscles by ultrasound shear wave elastography in participants with knee osteoarthritis accompanied by myofascial trigger points.

BACKGROUND: The objective of this investigation is to evaluate the consistency of intra-rater and inter-rater assessments utilizing ultrasound elastography to examine the muscle stiffness of the popliteus and gastrocnemius (medial and lateral heads) in patients with knee osteoarthritis accompanied by myofascial trigger points. METHODS: Thirty individuals with knee osteoarthritis accompanied by myofascial trigger points were assessed. Two examiners independently measured the muscle stiffness levels of the popliteus and gastrocnemius (medial and lateral heads) three times using ultrasound elastography in the first session. The second session was conducted one week later. RESULTS: In the initial test session, the mean shear modulus values for the popliteus and gastrocnemius (medial and lateral heads) muscles were measured as follows for tester 1 (12.75, 13.72, 14.13 kPa) and tester 2 (11.66, 12.81, 13.17 kPa). During the retest session, the previously measured variables by tester 1 and tester 2 yielded the following values: (12.61, 13.43, 14.26 kPa) and (11.62, 12.87, 13.30 kPa) respectively." Good to excellent intra-rater reliability (ICC = 0.912-0.986) and inter-rater reliability (ICC = 0.766-0.956) were reported for the shear moduli of the popliteus, medial and lateral gastrocnemius muscles. CONCLUSIONS: The assessment of muscle stiffness in the popliteus and gastrocnemius (medial and lateral heads) using ultrasound elastography is a reliable method in patients with knee osteoarthritis accompanied by myofascial trigger points.

Discovering associations between radiological features and COVID-19 patients' deterioration.

Background and Aims: Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Methods: This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results: Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion: This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.