RESUMEN
Objective: Despite the implementation of quality assurance procedures, current clinical trial management processes are time-consuming, costly, and often susceptible to error. This can result in limited trust, transparency, and process inefficiencies, without true patient empowerment. The objective of this study was to determine whether blockchain technology could enforce trust, transparency, and patient empowerment in the clinical trial data management process, while reducing trial cost. Design: In this proof of concept pilot, we deployed a Hyperledger Fabric-based blockchain system in an active clinical trial setting to assess the impact of blockchain technology on mean monitoring visit time and cost, non-compliances, and user experience. Using a parallel study design, we compared differences between blockchain technology and standard methodology. Results: A total of 12 trial participants, seven study coordinators and three clinical research associates across five sites participated in the pilot. Blockchain technology significantly reduces total mean monitoring visit time and cost versus standard trial management (475 to 7 min; P = 0.001; 722 to 10; P = 0.001 per participant/visit, respectively), while enhancing patient trust, transparency, and empowerment in 91, 82 and 63% of the patients, respectively. No difference in non-compliances as a marker of trial quality was detected. Conclusion: Blockchain technology holds promise to improve patient-centricity and to reduce trial cost compared to conventional clinical trial management. The ability of this technology to improve trial quality warrants further investigation.
RESUMEN
PURPOSE: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable. MATERIALS AND METHODS: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and single-slice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements. RESULTS: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2 ± 0.6% for SAT, and 2.7 ± 2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation. CONCLUSION: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.