7 research outputs found

    Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints

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    Physical exercise is an essential component of rehabilitation programs that improve quality of life and reduce mortality and re-hospitalization rates. In AI-driven virtual rehabilitation programs, patients complete their exercises independently at home, while AI algorithms analyze the exercise data to provide feedback to patients and report their progress to clinicians. To analyze exercise data, the first step is to segment it into consecutive repetitions. There has been a significant amount of research performed on segmenting and counting the repetitive activities of healthy individuals using raw video data, which raises concerns regarding privacy and is computationally intensive. Previous research on patients' rehabilitation exercise segmentation relied on data collected by multiple wearable sensors, which are difficult to use at home by rehabilitation patients. Compared to healthy individuals, segmenting and counting exercise repetitions in patients is more challenging because of the irregular repetition duration and the variation between repetitions. This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients, based on their skeletal body joints. Skeletal body joints can be acquired through depth cameras or computer vision techniques applied to RGB videos of patients. Various sequential neural networks are designed to analyze the sequences of skeletal body joints and perform repetition segmentation and counting. Extensive experiments on three publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and IntelliRehabDS, demonstrate the superiority of the proposed method compared to previous methods. The proposed method enables accurate exercise analysis while preserving privacy, facilitating the effective delivery of virtual rehabilitation programs.Comment: 8 pages, 1 figure, 2 table

    Explanted Skull Flaps after Decompressive Hemicraniectomy Demonstrate Relevant Bone Avitality-Is Their Reimplantation Worth the Risk?

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    Background: Reimplantations of autologous skull flaps after decompressive hemicraniectomies (DHs) are associated with high rates of postoperative bone flap resorption (BFR). We histologically assessed the cell viability of explanted bone flaps in certain periods of time after DH, in order to conclude whether precursors of BRF may be developed during their storage. Methods: Skull bone flaps explanted during a DH between 2019 and 2020 were stored in a freezer at either −23 °C or −80 °C. After their thawing process, the skulls were collected. Parameters of bone metabolism, namely PTH1 and OPG, were analyzed via immunohistochemistry. H&E stain was used to assess the degree of avital bone tissue, whereas the repeated assays were performed after 6 months. Results: A total of 17 stored skull flaps (8 at −23 °C; 9 at −80 °C) were analyzed. The duration of cryopreservation varied between 2 and 17 months. A relevant degree of bone avitality was observed in all skull flaps, which significantly increased at the repeated evaluation after 6 months (p p = 0.006) as well as longer storage times (p < 0.001) were identified as prognostic factors for higher rates of bone avitality in a linear mixed regression model. Conclusions: Our novel finding shows a clear benefit from storage at −80° C, which should be carefully considered for the future management and storage of explanted skull flaps. Our analysis also further revealed a significant degree of bone avitality, a potential precursor of BFR, in skull flaps stored for several weeks. To this end, we should reconsider whether the reimplantation of autologous skull flaps instead of synthetic skull flaps is still justified

    SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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    Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population
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