Graphical abstract Efficient remote monitoring of the patient infected with coronavirus without spread to healthcare workers is the need of the hour. An effectual and faster communication system must be established wherein the healthcare workers at the remote quarantine ward can communicate with healthcare professionals present in specialty hospitals. Incidentally, there is a need to establish a contactless smart cloud-based connection between a specialty hospital and quarantine wards during pandemic situation. This paper proposes an initial contactless web-based tele-health clinical decision support system that integrates near-field communication (NFC) tags and a smart cloud-based structuring tool that enables the quick diagnosis of patients with COVID-19 symptoms and monitors the remotely located quarantine wards during the recent pandemic. The proposed framework consists of three-stages: (i) contactless health parameter extraction from the patient using an NFC tag; (ii) converting medical report into digital text using optical character recognition algorithm and extracting values of relevant medical-parameters using natural language processing; and (iii) smart visualization of key medical parameters. The accuracy of the proposed system from NFC reader until analysis using a novel structuring algorithm deployed in the cloud is more than 94%. Several capabilities of the proposed web-based system were compared with similar systems and tested in an authentic mock clinical setup, and the physicians found that the system is reliable and user friendly. Supplementary Information The online version contains supplementary material available at 10.1007/s11517-021-02456-1.

Hip fractures due to osteoporosis are increasing progressively across the globe. It is also difficult for those fractured patients to undergo dual-energy X-ray absorptiometry scans due to its complicated protocol and its associated cost. The utilisation of computed tomography for the fracture treatment has become common in the clinical practice. It would be helpful for orthopaedic clinicians, if they could get some additional information related to bone strength for better treatment planning. The aim of our study was to develop an automated system to segment the femoral neck region, extract the cortical and trabecular bone parameters, and assess the bone strength using an isotropic volume construction from clinical computed tomography images. The right hip computed tomography and right femur dual-energy X-ray absorptiometry measurements were taken from 50 south-Indian females aged 30-80 years. Each computed tomography image volume was re-constructed to form isotropic volumes. An automated system by incorporating active contour models was used to segment the neck region. A minimum distance boundary method was applied to isolate the cortical and trabecular bone components. The trabecular bone was enhanced and segmented using trabecular enrichment approach. The cortical and trabecular bone features were extracted and statistically compared with dual-energy X-ray absorptiometry measured femur neck bone mineral density. The extracted bone measures demonstrated a significant correlation with neck bone mineral density (r > 0.7, p < 0.001). The inclusion of cortical measures, along with the trabecular measures extracted after isotropic volume construction and trabecular enrichment approach procedures, resulted in better estimation of bone strength. The findings suggest that the proposed system using the clinical computed tomography images scanned with low dose could eventually be helpful in osteoporosis diagnosis and its treatment planning.

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Presence of polyps is the root cause of colorectal cancer, hence identification of such polyps at an early stage can help in advance treatments to avoid complications to the patient. Since there are variations in the size and shape of polyps, the task of detecting them in colonoscopy images becomes challenging. Hence our work is to leverage an algorithm for segmentation and classification of the polyp of colonoscopy images using Deep learning algorithms. In this work, we propose PolypEffNetV1, a U-Net to segment the different pathologies present in the colonoscopy frame and EfficientNetB5 to classify the detected pathologies. The colonoscopy images for the segmentation process are taken from the open-source dataset KVASIR, it consists of 1000 images with “ground truth” labeling. For classification, combination of KVASIR and CVC datasets are incorporated, which consists of 1612 images with 1696 polyp regions and 760 non-polyp inflamed regions. The proposed PolypEffNetV1 produced testing accuracy of 97.1%, Jaccard index of 0.84, dice coefficient of 0.91, and F1-score of 0.89. Subsequently, for classification to evidence whether the segmented region is polyp or non-polyp inflammation, the developed classifier produced validation accuracy of 99%, specificity of 98%, and sensitivity of 99%. Hence the proposed system could be used by gastroenterologists to identify the presence of polyp in the colonoscopy images/videos which will in turn increase healthcare quality. These developed models can be either deployed on the edge of the device to enable real-time aidance or can be integrated with existing software-application for offline review and treatment planning.

The Scientometric study examines the research contribution of India towards pollution control by date indexed in Scopus database for 12 years from 2003 to 2014 using different qualitative and quantitative measures. Related relevant literature was reviewed. It has been identified that a total number of 28445 research publications were published during the above cited period. It is identified that 160 research institutions of India were responsible for placing India in the 3 rd place for publishing 1551 publications and also to highlight the h-index gained by the top 15 institutions. Further, the analysis revealed the wise country publications with ranking and the share of India towards research publications with citations along with the type of documents. The impact of relative research effort analyzed through Publication Efficiency Index. The funding agencies are requested to allocate more funds to do many more research on Pollution Control for the betterment of the society.