ABSTRACT
Background Achilles tendinopathy, a common cause of heel pain, is primarily considered mechanical in origin, but its pathogenesis and treatment lack consensus. Molecules such as collagen peptide type-1, low molecular weight chondroitin sulphate, sodium hyaluronate and vitamin C have been shown to act as building blocks of tendon structure, and oral supplementation of these have promising results in Achilles tendinopathy. Methods This study was a prospective randomized control trial to compare the effectiveness of oral diclofenac sodium versus a nutraceutical combination of collagen peptide type-1, chondroitin sulphate, sodium hyaluronate, and vitamin C in the treatment of Achilles tendinopathy on pain and ultrasonographic structures. A total of 40 patients satisfying inclusion and exclusion criteria were randomly allocated into two groups and were given the nutraceutical combination in group A and diclofenac sodium in group B. The patient evaluation was done at baseline, six-week, and 12-week intervals in terms of VAS (Visual Analogue Scale) and tendo-Achilles thickness by ultrasound. Results Both nutraceutical combination and diclofenac reduced pain in persons with Achilles tendinopathy. The nutraceutical combination had a statistically significant better outcome in reducing pain at the end of 12 weeks. On ultrasound, both the interventions reduced Achilles tendon anteroposterior and mediolateral thickness by the end of 12 weeks. Although there was no absolute significant intergroup difference, the percentage change was more in the nutraceutical group in the case of anteroposterior thickness. Conclusion Combining collagen peptide type-1, low molecular weight chondroitin sulphate, sodium hyaluronate, and vitamin C is more effective than oral diclofenac in controlling pain in Achilles tendinopathy.
ABSTRACT
OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)-positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques. METHODS: CXR of 487 patients were classified into [4] categories-normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as "normal" and "indeterminate" were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction. RESULTS: The precision of radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying "normal" CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these "normal" radiographs. CONCLUSION: This study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes. KEY POINTS: ⢠Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of radiologists in assessing CXR for possible COVID in comparison to RT-PCR. ⢠We show that AI achieves the best results in images classified as "normal" by radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. ⢠The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone.
