Type 1 Diabetes (T1D) is a chronic metabolic disease characterized by elevated blood glucose (BG) concentrations, resulting from the immune-mediated destruction of insulin-producing $\beta$-cells in the pancreas. Effective management of T1D greatly benefits from constant monitoring of BG levels, achievable in real-time using minimally invasive continuous glucose monitoring (CGM) devices. These devices provide data streams that can be leveraged by forecasting algorithms to predict BG levels minutes in advance, enabling timely therapeutic interventions to prevent adverse events, such as hypo/hyperglycemia. With the increasing availability of data, deep learning (DL) algorithms have emerged as the state-of-the-art for BG forecasting, owing to their ability to autonomously learn complex nonlinear relationships, such as those underlying the glucoregulatory system. Despite a growing body of research, a comprehensive review specifically focusing on DL applications for BG prediction is still lacking. To address this gap, a systematic review was conducted following the PRISMA guidelines, involving extensive searches across PubMed, Scopus, and Web of Science databases. A total of 26 studies satisfied the inclusion criteria and were evaluated based on dataset characteristics, model inputs, training paradigm, prediction horizon, model architecture, evaluation metrics, performance, and baseline comparators. While DL models show great promise, several challenges persist-particularly in ensuring physiological fidelity and interpretability, both essential for clinical adoption. To overcome these barriers, future research should prioritize the integration of explainable AI (XAI) techniques to improve model reliability and safety, ultimately supporting the effective deployment of DL models in real-time T1D management.

Continuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy.

Type 1 diabetes (T1D) management in pediatric patients presents unique challenges due to the evolving physiology and the need for close monitoring and intervention. This article presents the system architecture of TWIN, a novel personalized Decision Support System (DSS) for managing multiple daily injections (MDI) therapy in pediatric T1D patients. TWIN incorporates advanced technologies such as continuous glucose monitoring (CGM) devices, smart insulin pens, and wearable physical activity trackers to collect large amounts of user-generated data. At its core, the system leverages an open-source digital twinning methodology called ReplayBG to create virtual clones of patients, representing their unique physiological characteristics. These virtual clones are then utilized in multiple simulation iterations to personalize and optimize therapy parameters. To promote the transparency of the generated results, TWIN employs a large-scale linguistic model based on a Generative Pre-trained Transformer (GPT). This model provides clear explanations and contextual information regarding the recommended therapy parameters. Finally, the proposed TWIN system architecture integrates these algorithms within a state-of-the-art digital platform. This platform offers a user-friendly interface for patients and healthcare providers, enabling effective management and tuning of MDI therapy. Future work will focus on testing and validating TWIN to assess its efficacy and usability in pediatric T1D management.

Continuous Glucose Monitors are minimally-invasive portable sensors that are revolutionizing the management of Type 1 Diabetes (T1D). A common issue encountered in their daily use is related to the presence of pressure-induced sensor attenuations (PISAs), temporary faults of the devices, resulting in false low blood glucose readings that can impact and compromise the reliability of CGMs. In this work, we explore the application of matched filters (MFs), a powerful pattern recognition technique, for the retrospective identification of PISAs failures. A MF is designed for the detection of a signal with a specific shape, associated with the occurrence of a PISA episode. The proposed algorithm is tested in-silico on a dataset generated with a state-of-art T1D patient simulator. MFs achieve a recall of 0.75 with about 1 false alarm every 5 days, outperforming other state-of-art algorithms proposed for the same purpose, including one based on a Random Forest classifier (RF). Moreover, when embedded as additional feature within a RF it improves the performance by granting a recall of 0.83 and 1 false alarm raised in 10 days. The encouraging outcomes in the simulated scenario pave the way for future investigations involving real-world data, as well as potential enhancements in detecting different types of sensors’ failures.

Type 1 diabetes (T1D) is a chronic condition characterized by elevated blood glucose (BG) levels resulting from the pancreas’ inability to secrete insulin. To keep their BG levels within the safe range of [70−180] mg/dL, individuals affected by T1D must adhere to a lifelong therapy, which involves multiple daily actions and may negatively impact patients’ quality of life.To address these challenges, decision support systems (DSSs) utilizing continuous glucose monitoring sensors have become vital in managing T1D. These tools assist individuals suggesting therapeutic actions like carbohydrate intake and insulin injections. In this context, accurate algorithms predicting future BG levels are essential for proactive interventions, improving glucose control and enhancing patient well-being.This paper presents a new algorithm for DSSs that recommends corrective insulin boluses (CIBs). The core of the proposed DSS is a BG predictive algorithm based on a long short-term memory (LSTM) neural network which has been trained on 12 patients with T1D monitored for 10 weeks. The proposed algorithm, named LSTM-CIB, has been retrospectively evaluated on an external test set composed by 30 T1D patients monitored in free-living conditions. Compared to a state-of-art heuristic-based strategy for hyperglycemia correction, LSTM-CIB significantly reduces time spent in hyperglycemia (33.26% vs. 39.42%) and increases time spent in euglycemia (65.29% vs. 59.16%), also improving patient safety.

Abstract The presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline implementation for further upcoming releases of additional and more advanced AI models and supporting pipelines (such as for ALS and MS progression prediction, patient stratification, and ambiental exposure modelling) in the following development.

Mealtime insulin dosing is a major challenge for people living with type 1 diabetes (T1D). This task is typically performed using a standard formula that, despite containing some patient-specific parameters, often leads to sub-optimal glucose control due to lack of personalization and adaptation. To overcome the previous limitations here we propose an individualized and adaptive mealtime insulin bolus calculator based on double deep Q-learning (DDQ), which is tailored to the patient thanks to a personalization procedure relying on a two-step learning framework. The DDQ-learning bolus calculator was developed and tested using the UVA/Padova T1D simulator modified to reliably mimic real-world scenarios by introducing multiple variability sources impacting glucose metabolism and technology. The learning phase included a long-term training of eight sub-population models, one for each representative subject, selected thanks to a clustering procedure applied to the training set. Then, for each subject of the testing set, a personalization procedure was performed, by initializing the models based on the cluster to which the patient belongs. We evaluated the effectiveness of the proposed bolus calculator on a 60-day simulation, using several metrics representing the goodness of glycemic control, and comparing the results with the standard guidelines for mealtime insulin dosing. The proposed method improved the time in target range from 68.35% to 70.08% and significantly reduced the time in hypoglycemia (from 8.78% to 4.17%). The overall glycemic risk index decreased from 8.2 to 7.3, indicating the benefit of our method when applied for insulin dosing compared to standard guidelines.

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.

BACKGROUND: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods. RESULTS: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes. CONCLUSIONS: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.