The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.

Background: Managing Type 1 diabetes (T1D) aims to optimize glucose within the target range while minimizing hyperglycemia and hypoglycemia, yet exercise complicates glycemic outcomes. Despite advances, evidence is limited on how exercise relates to glycemic variability (GV) and hypoglycemia in automated insulin delivery (AID) and non-AID users, including evidence on GV’s temporal course before and after hypoglycemia, especially following long episodes. Objective: We aimed to characterize −48 to +48 h CGM trajectories around hypoglycemia, compare commercial AID and non-AID users, and assess modifiers (exercise, episode duration/severity, gender). Methods: This study analyzes the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, assessing GV, hypoglycemia, gender, and exercise interactions in AID (n = 222) and non-AID (n = 276) users. The study examined patterns of glycemic metrics, including time below range (TBR) and glycemic variability surrounding hypoglycemia events, focusing on the 48 h before and after these events. We further assessed the impact of different hypoglycemia levels (41–50 mg/dL, 51–60 mg/dL, and 61–70 mg/dL) on post-event glucose stability. Results: Glycemic variability increased before and after hypoglycemia for up to 48 h in both AID and non-AID users, with statistically significant differences in GV metrics. TBR elevation persisted across all groups, peaking around hypoglycemic episodes. Notably, females using AID achieved significantly improved glucose stability compared to non-AID females, which is a larger within-group difference than that observed in males. Individual-level AID analyses revealed that long-duration hypoglycemia episodes (>40 min) resulted in prolonged TBR elevation, suggesting a slower recovery period despite AID intervention. Conclusions: GV trends may aid in predicting hypoglycemia over extended time periods. Integrating GV patterns into AID systems could improve glucose stability and mitigate hypoglycemia cycles, especially with the possible evaluation of hypoglycemia duration. Future research should explore hormonal influences (e.g., menstrual cycle effects) and inter-individual variability for optimized individual diabetes management.

Background: Data analysis plays a pivotal role in transforming raw data into findings that are transparent, reproducible, and clinically meaningful and useful, which is critical when analyzing insulin-related data for diabetes studies. Failing to do so will make subsequent analysis of insulin data irrelevant due to inaccuracies. Objective: Describe the end-to-end pre-processing steps used for assessing the T1DEXI dataset and why these steps are necessary before moving to data analysis. Methods: Standardize and harmonize the raw datasets, including comparing devices and adjusting per-device pre-processing steps; mitigating gaps in data; accounting for extended insulin delivery that is articulated as single timestamp records as well as similarly accounting for suspension of insulin delivery, taking into account the periods of time in which insulin was or was not delivered. Results: Insulin-related data is transformed to uniform per-5-minute intervals and is able to be used for subsequent analysis and event-based interpretation. Conclusions: As it relates to insulin delivery, pre-processing should be used to make sure that insulin delivery is uniformly analyzed down to 5-minute intervals so that all insulin delivery event types, whether those be manual up-front boluses; basal or temporary basal rates; and extended bolus events (such as square or dual/square wave boluses) and suspend events are correctly documented as a timeseries such that subsequent analysis will be accurate based on the duration of insulin activity.