Journal of Clinical and Diagnostic Research aims to publish findings of doctors at grass root level and post graduate students, so that all unique medical experiences are recorded in literature.

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression.

Background and Aim. Children with spastic cerebral palsy (CP) have impaired trunk control. Joint position sense, a component of proprioception, plays a crucial role in maintaining spinal mobility and stability. The current study is aimed at analysing the relationship between trunk control and trunk position sense in children with spastic CP. Methods. In this study, 24 children with spastic CP aged between 8 and 15 years were recruited. They were classified based on their functional performance using Gross Motor Function Classification System (GMFCS). Trunk control and trunk position sense were assessed using the trunk control measurement scale (TCMS) and digital goniometer, respectively. The correlation between these variables was tested using Spearmans correlation coefficient. Results. Significant negative correlation was found between trunk position sense and TCMS score. Similarly, a significant moderate correlation was found between trunk position sense and GMFCS. A strong negative correlation was also found between GMFCS and TCMS. Conclusion. Children with spastic CP with better trunk position sense had better trunk control. Similarly, children with higher functional performance had better trunk control and lesser error in trunk position sense. The current findings imply the relevance of proprioceptive training of the trunk for enhancing trunk motor control in children with spastic CP.

The purpose of the study was to assess the effects of a novel technique involving facial stretching of the unaffected side along with a structured exercise for the affected side on facial symmetry and facial functions as compared to conventional exercise. A hospital-based parallel-group randomized trial was completed among patients with acute Bell’s palsy in Mangalore, India. Participants were randomized to receive facial stretching and a structured exercise program (experimental group) or the conventional exercise regimen (conventional group). Primary outcomes were facial symmetry and voluntary movement; assessed by the Sunnybrook Facial Grading System (SFGS). Both regimens were given for 3 weeks, with baseline, 10th day, and 20th day assessments. Out of 31 participants screened, 24 were eligible and 12 participants each were assigned to experimental and conventional groups. Change scores revealed greater improvement in the SFGS score (p = 0.002) for the experimental group participants. Facial stretching and structured exercise program exhibited promising results in enhancing facial symmetry and function in acute Bell’s palsy when compared to conventional exercise regimen.