首页|Swansea University Reports Findings in Artificial Intelligence (Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monit or: A Pilot Study)
Swansea University Reports Findings in Artificial Intelligence (Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monit or: A Pilot Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Swansea, United Kingdom, by NewsRx correspondents, research stated, "Self-monitoring of glucose is important to the successful management of diabetes; however, existing monito ring methods require a degree of invasive measurement which can be unpleasant fo r users. This study investigates the accuracy of a noninvasive glucose monitorin g system that analyses spectral variations in microwave signals." Our news journalists obtained a quote from the research from Swansea University, "An open-label, pilot design study was conducted with four cohorts (N = 5/cohor t). In each session, a dial-resonating sensor (DRS) attached to the wrist automa tically collected data every 60 seconds, with a novel artificial intelligence (A I) model converting signal resonance output to a glucose prediction. Plasma gluc ose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean ab solute relative difference (MARD) between the DRS and plasma glucose values. Acc urate plasma glucose predictions were obtained across all four cohorts using a r andom sampling procedure applied to the full four-cohort data set, with an avera ge MARD of 10.3%. A statistical analysis demonstrates the quality o f these predictions, with a surveillance error grid (SEG) plot indicating no dat a pairs falling into the high-risk zones. These findings show that MARD values a pproaching accuracies comparable to current commercial alternatives can be obtai ned from a multiparticipant pilot study with the application of AI."
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