Objective. Carotid distensibility (CD) is a measure of carotid artery elasticity that has been introduced as a risk factor for cardiovascular disease. Information regarding reproducibility of sonographic CD measures is limited. The objective of this study was to evaluate the inter-reader reliability of sonographic measurements of common carotid artery (CCA) diameters and derived metrics of CD. Methods. Two independent readers (R1 and R2) measured the systolic diameter (SD) and diastolic diameter (DD) for the right CCA from the B/M-mode sonographic registrations among 118 subjects. The derived CD metrics (strain, elastic modulus [E], stiffness [β], and CD) were calculated. The inter-reader type 3 intraclass correlation coefficients (ICC3,1) for carotid diameters were calculated. Results. The mean SDs ± standard deviation were 7.15 ± 1.43 mm for R1 and 7.24 ± 1.43 mm for R2. The mean DDs were 6.71 ± 1.36 mm for R1 and 6.68 ± 1.41 mm for R2. The mean differences of SD and DD between R1 and R2 were 0.08 ± 0.40 mm (paired t test, P = .04) and 0.03 ± 0.43 mm (paired t test, P = .46), respectively. Inter-reader type 3 intraclass correlation coefficients were 0.96 for SD and 0.95 for DD. We observed a significant association of demographics with carotid diameters but not with derived CD metrics or risk factors. Conclusions. Our results suggest good reproducibility of CCA diameters measured with B/Mmode sonography. However, very small changes in linear measurements of carotid diameters can have big effects on estimates of arterial mechanical properties such as strain and Young's modulus. The standard boundary identification methods may not be precise and reproducible enough for use in a clinical setting.

BackgroundPlanning for mass gatherings often includes temporary healthcare systems to address the needs of attendees. However, paper-based record keeping has traditionally precluded the timely application of collected clinical data for epidemic surveillance or optimization of healthcare delivery. We evaluated the feasibility of harnessing ubiquitous mobile technologies for conducting disease surveillance and monitoring resource utilization at the Allahabad Kumbh Mela in India, a 55-day festival attended by over 70 million people.MethodsWe developed an inexpensive, tablet-based customized disease surveillance system with real-time analytic capabilities, and piloted it at five field hospitals.ResultsThe system captured 49 131 outpatient encounters over the 3-week study period. The most common presenting complaints were musculoskeletal pain (19%), fever (17%), cough (17%), coryza (16%) and diarrhoea (5%). The majority of patients received at least one prescription. The most common prescriptions were for antimicrobials, acetaminophen and non-steroidal anti-inflammatory drugs. There was great inter-site variability in caseload with the busiest hospital seeing 650% more patients than the least busy hospital, despite identical staffing.ConclusionsMobile-based health information solutions developed with a focus on user-centred design can be successfully deployed at mass gatherings in resource-scarce settings to optimize care delivery by providing real-time access to field data.

We propose a self-supervised learning method to jointly reason about spatial and temporal context for video recognition. Recent self-supervised approaches have used spatial context [9,34] as well as temporal coherency [32] but a combination of the two requires extensive preprocessing such as tracking objects through millions of video frames [59] or computing optical flow to determine frame regions with high motion [30]. We propose to combine spatial and temporal context in one self-supervised framework without any heavy preprocessing. We divide multiple video frames into grids of patches and train a network to solve jigsaw puzzles on these patches from multiple frames. So the network is trained to correctly identify the position of a patch within a video frame as well as the position of a patch over time. We also propose a novel permutation strategy that outperforms random permutations while significantly reducing computational and memory constraints. We use our trained network for transfer learning tasks such as video activity recognition and demonstrate the strength of our approach on two benchmark video action recognition datasets without using a single frame from these datasets for unsupervised pretraining of our proposed video jigsaw network.

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