Image-guided thermal ablation in the management of symptomatic adenomyosis: a systematic review and meta-analysis

Objective To evaluate the clinical effects of image-guided thermal ablation for the treatment of symptomatic adenomyosis (AD). Data sources We searched PubMed, Web of Science, Cochrane Library, EMBASE, ClinicalTrials.gov and Google Scholar for literature from January 2000 to September 2020. Methods of study selection We included all studies reporting clinical outcomes of image-guided thermal ablation for AD, involving high-intensity focused ultrasound (HIFU), percutaneous microwave ablation (PMWA) and radiofrequency ablation (RFA). Two independent researchers performed study selection according to the screening criteria. Results A total of 38 studies representing 15,908 women were included. Compared with those at baseline, the visual analog scale scores, the symptom severity scores and the menorrhagia severity scores decreased significantly after these thermal ablation therapies. The mean ablation time was 92.18 min, 24.15 min and 31.93 min during HIFU, PMWA and RFA, respectively. The non-perfused volume ratio of AD was 68.3% for HIFU, 82.5% for PMWA and 79.2% for RFA. The reduction rates of uterine volume were 33.6% (HIFU), 46.8% (PMWA) and 44.0% (RFA). The reduction rates of AD volume were 45.1% (HIFU), 74.9% (PMWA) and 61.3% (RFA). The relief rates of dysmenorrhea were 84.2% (HIFU), 89.7% (PMWA) and 89.2% (RFA). The incidence of minor adverse events was 39.0% (HIFU), 51.3% (PMWA) and 3.6% (RFA). The re-intervention rates were 4.0% (HIFU) and 28.7% (RFA). The recurrence rate was 10.2% after HIFU. The pregnancy rates were 16.7% (HIFU), 4.93% (PMWA) and 35.8% (RFA). Conclusion Image-guided HIFU, PMWA and RFA may be effective and safe minimally invasive therapies for symptomatic AD

Biosignal Compression Toolbox for Digital Biomarker Discovery

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data

Assessment of ownership of smart devices and the acceptability of digital health data sharing

Abstract Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18–25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies