Research Associate National University of Singapore
Disclosure: Disclosure information not submitted.
Digital therapeutics has emerged as an alternative or complementary modality of treatment to drug-based therapies for various indications, such as addiction and cognitive decline. Similar to conventional drug dosing, digital therapies often rely upon either fixed, or step-wise increased difficulty. Those approaches lack the flexibility and the ability to personalize the treatment to the individual. URATE.AI - a clinically validated deterministic optimization artificial intelligence (AI) platform that has already been used to modulate optimized dosing regimens for indications ranging from oncology (solid tumor/hematologic) to infectious diseases (HIV/TB) and immunosuppression (liver). In this prospective study, CURATE.AI identified individualized N-of-1 (N.1) learning trajectory profiles of healthy volunteers (both sexes, ages 21-40) trained on the Multi-Attribute Task Battery (MATB). MATB is a flight deck simulator developed by the National Aeronautics and Space Administration (NASA) and United States Air Force (USAF). The prospective clinical trial study design is a randomized, multiphase, parallel three-arm, single-blinded, N-of-1, single-center, exploratory pilot trial with 1:1:1 allocation approved by NUS Institutional Review Board (S-17-180) and listed under Clinicaltrials.gov identifier NCT03832101. For the CURATE.AI arm of the study, five subjects were fluent in English, had no prior experience with the MATB, and no history of perceptual or memory deficits, and recruited at Yale-NUS to participate in MATB simulator experiment sessions, conducted at the Yale-NUS campus. Each subject underwent a 34-minute MATB training session composed of 17 training and testing blocks of varying intensity levels (high, medium, low). Individualized CURATE.AI profiles were calibrated from individual’s own data: performance scores (RMAN-COMM z-scores from the training blocks), performance improvement, and training intensity levels. From each individual’s prospectively obtained data, N.1 learning trajectory profiles were derived and constructed with CURATE.AI, demonstrating the unique relationship between performance, training intensity, and performance improvement. Each subject had a different performance range (-1.24 to 0.70, -1.59 to 1.04, -2.13 to 0.66) and different training intensity levels for optimal performance improvement. As identified from their CURATE.AI N.1 profiles, high-intensity training in select participants corresponded with greatest gains in performance improvement, while low-intensity training was identified for mediating similar gains in the other subjects. Based upon each individual’s unique interaction between performance and training intensity, these N.1 learning trajectory profiles provide a means of real-time optimization of performance improvement by dynamically identifying and modulating the appropriate training intensities. In this prospective in-human study, interfacing MATB with CURATE.AI revealed substantial differences between subjects’ N.1 learning profiles and the correlation between tailored training intensity on performance improvement. The ability of CURATE.AI to identify N.1 profiles represents the advancement and utilization of AI to actionably address challenges encountered in personalized learning and the emerging field of digital therapeutics.