Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

In the paper

“Indeed, in contrast to SMBG, CGM sensors allow to track glucose dynamics and they ensure prompt detection of even asymptomatic high/low glucose events, which are difficult to capture with SMBG.”

What the sources say

• Not checked yetcites [10] Cappon, 2019, Continuous glucose monitoring sensors for diabetes management: A review of technologies and applications, Diabetes Metab. J. source

In the paper

“The DSSs for patient use proposed in the literature receive input information about glucose concentration, diet, insulin administrations and physical activity and primarily perform three tasks: Personalized insulin bolus calculation, adaptive tuning of BC parameters and glucose prediction (Figure 1).”

In the paper

“The SF was designed when SMBG was the only way to measure glucose concentration at home. In fact, the SF only uses information about the current glucose concentration (Gc), ignoring the information on glucose rate-of-change (ROC) that is now available with modern CGM sensors.”

In the paper

“In conclusion, machine learning methods can be used to design new data-driven formulae for insulin dosing that are able to naturally account for CGM information (including glucose ROC) and which could improve glycemic control in T1D.”

In the paper

“Although, on average, the NN-based BC did improve BG control when compared to the SF, in a non-negligible number of cases the dose provided by the NN-based BC led to worse glycemic control than did the SF.”

What the sources say

• Not checked yetcites [37] Cappon, 2018, A Neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring, J. Diabetes Sci. Technol. source

In the paper

“These DSSs for patients offer an alternative to closed-loop systems. Indeed, many users do not feel confident about using the artificial pancreas [28,29], as they are concerned about possible errors occurring in the insulin pump and so prefer an open-loop therapy (i.e., a therapy in which the patient manually sets the insulin doses), which can be assisted by a DSS.”

What the sources say

• Not checked yetcites [28] Hendrieckx, 2017, “It is definitely a game changer”: A qualitative study of experiences with in-home overnight closed-loop technology among adults with type 1 diabetes, Diabetes Technol. Ther. source
• Not checked yetcites [29] Kropff, 2016, Psychological outcomes of evening and night closed-loop insulin delivery under free living conditions in people with Type 1 diabetes: A 2-month randomized crossover trial, Diabet. Med. source

In the paper

“The results obtained from a simulated experiment suggested that R2R offers a promising approach to optimizing SF parameters [41].”

What the sources say

• Not checked yetcites [41] Herrero, 2015, Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability, Comput. Methods Programs Biomed. source

In the paper

“Since 2015, some rtCGM devices have also received regulatory approval for therapeutic use, which means that rtCGM measurements may be used for making therapeutic decisions, such as insulin dosing [19].”

What the sources say

• Not checked yetcites [19] Edelman, 2016, Regulation Catches Up to Reality, J. Diabetes Sci. Technol. source

In the paper

“Herrero et al. [41] proposed an approach to automatically adjust CR and CF using CGM measurements. This method updates the parameters values every day in a run-to-run (R2R) fashion.”

What the sources say

• Not checked yetcites [41] Herrero, 2015, Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability, Comput. Methods Programs Biomed. source

In the paper

“However, an in silico head-to-head comparison of these methods has shown that their efficacy depends on the pre-meal scenario and that not one of these methodologies globally outperforms the others [36].”

What the sources say

• Not checked yetcites [36] Cappon, 2018, In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change, J. Diabetes Sci. Technol. source

In the paper

“rtCGM devices are able to provide, in real-time, not only the glucose concentration present but also information about the glucose trend (typically displayed to the user by an arrow), which can be used for making more informed therapeutic decisions.”

In the paper

“Indeed, the SF is intrinsically suboptimal, and using it as a starting point to be corrected might prove to be an obstacle in the training process of NN [36].”

What the sources say

• Not checked yetcites [36] Cappon, 2018, In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change, J. Diabetes Sci. Technol. source

In the paper

“For example, PEPPER (Patient Empowerment through Predictive PERsonalised decision support), a project funded by the European Commission under the Horizon 2020 program, recently developed a DSS for personalized T1D management that includes an adaptive bolus advisor, based on case-based reasoning, as well as a system for carbohydrate recommendations based on a predictive model [31].”

What the sources say

• Not checked yetcites [31] Herrero, P., Lopez, B., and Martin, C. (2016, January 30). PEPPER: Patient empowerment through predictive personalized decision support. Proceedings of the AID: Artificial Intelligence for Diabetes: 1st ECAI Workshop on Artificial Intelligence for Diabetes at the 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, The Netherlands.

In the paper

“The results obtained in a cohort of 20 virtual subjects showed that the adoption of R2R+CBR significantly improved glycemic control, achieving better results than the stand-alone R2R algorithm.”

What the sources say

• Not checked yetcites [42] Herrero, 2014, Advanced insulin bolus advisor based on run-to-run control and case-based reasoning, IEEE J. Biomed. Health Inform.

In the paper

“The study demonstrated the safety and effectiveness of ABC4D, which made it possible to reduce the occurrence of postprandial hypoglycemia.”

What the sources say

• Not checked yetcites [45] Reddy, 2016, Clinical safety and feasibility of the advanced bolus calculator for type 1 diabetes based on case-based reasoning: A 6-week nonrandomized single-arm pilot study, Diabetes Technol. Ther. source

In the paper

“Current research in diabetes is putting considerable effort into developing algorithms and software applications to enhance T1D therapy by exploiting the latest technologies and their interoperability.”

In the paper

“The encouraging results obtained in a simulated study suggest that reinforcement learning could be a promising approach, one able to both personalize insulin therapy parameters and improve glycemic control.”

What the sources say

• Not checked yetcites Sun et al. [48] Sun, 2019, A dual mode adaptive basal-bolus advisor based on reinforcement learning, IEEE J. Biomed. Health Inform. source

In the paper

“The NN hyper-parameters (i.e., the number of neurons per layer and the activation function type) were tuned using a five-fold CV procedure, in other words, the training set was divided into five groups of data, four of which were used for training the NN and the remaining one used as a validation set to compute the mean squared error (MSE).”

What the sources say

• Not checked yetcites [37] Cappon, 2018, A Neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring, J. Diabetes Sci. Technol. source

In the paper

“The increasing availability of information offered by wearable devices, such as CGM sensors, insulin pumps, smart pens, and activity trackers, is making the decision-making process increasingly complex.”

In the paper

“One fundamental issue of R2R is that it assumes that the process that is to be controlled, in our case the BG level, is repetitive. This assumption is somewhat unrealistic in many cases, thus it limits R2R applicability.”

In the paper

“The most recent CGM sensors adopt very simple prediction algorithms, e.g., linear extrapolation, to predict future interstitial glucose concentrations (e.g., 15–30 min in advance) in real-time and generate predictive alerts when a hypo/hyperglycemic event is foreseen [49].”

What the sources say

• Not checked yetcites [49] Abraham, S.B., Arunachalam, S., Zhong, A., Agrawal, P., Cohen, O., and McMahon, C.M. (2019). Improved real-world glycemic control with continuous glucose monitoring system predictive alerts. J. Diabetes Sci. Technol. source

In the paper

“The proposed R2R+CBR algorithm was able to tackle intrasubject variability and external perturbations, and it was shown to be robust to uncertainty in carbohydrate input (i.e., carb-counting error) and noise in CGM measurements.”

What the sources say

• Not checked yetcites [42] Herrero, 2014, Advanced insulin bolus advisor based on run-to-run control and case-based reasoning, IEEE J. Biomed. Health Inform.

In the paper

“The results obtained in a virtual cohort of 100 adults, showed that, compared to the SF, the XGB-based BC significantly improved time in euglycemia (from 61.98% to 67.00%) without increasing time in hypoglycemia.”

What the sources say

• Not checked yetcites Cappon et al. [59] Cappon, G., Facchinetti, A., Sparacino., G., Georgiou, P., and Herrero, P. (2019). Classification of postprandial glycemic status with application to insulin dosing in type 1 diabetes—An in silico proof-of-concept. Sensors, 19. source

In the paper

“Most of the prediction algorithms in the literature approach the problem of glucose prediction as a regression problem, in other words they seek to predict the value of interstitial glucose concentration with a prediction horizon (PH) typically below 60 min (30 min is the most frequent PH value) [52].”

What the sources say

• Not checked yetcites [52] Oviedo, 2016, A review of personalized blood glucose prediction strategies for T1DM patients, Int. J. Numer. Methods Biomed. Eng. source

In the paper

“Most studies on glucose prediction have focused on forecasting future glucose concentration levels using a regression approach, usually with a short PH (e.g., 30 min).”

In the paper

“Although each module has been independently validated on retrospective data, a clinical validation of the proposed DSS is needed to prove its efficacy as a tool for improving glycemic control in subjects with T1D.”

What the sources say

• Not checked yetcites [61] Vehi, 2019, Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning, Health Inform. J. source

In the paper

“As reported in recent systematic reviews [25,54], AI techniques, especially supervised machine learning techniques, are extensively used for building black-box models for glucose prediction. The most used method is NN, but other machine learning methodologies, such as random forest, support vector regression, regression algorithms and deep-learning techniques.”

What the sources say

• Not checked yetcites [25] Contreras, 2018, Artificial intelligence for diabetes management and decision support: Literature review, J. Med. Internet Res. source
• Not checked yetcites [54] Woldaregay, 2019, Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes, Artif. Intell. Med. source

In the paper

“The models showing best performance in terms RMSE were ARX, Ridge regression, linear SVR, LSTM, and TCN, with RMSE between 19 and 20 mg/dL.”

What the sources say

• Not checked yetcites [58] Xie, J., and Wang, Q. (2020). Benchmarking machine learning algorithms on blood glucose prediction for Type 1 Diabetes in comparison with classical time-series models. IEEE Trans. Biomed. Eng., 1. source

In the paper

“On the other hand, predicting the exact glucose concentration level over such a long PH would be a very challenging task.”

In the paper

“Considering both of the metrics, the models that performed better were linear SVR and TCN with RMSE around 20 mg/dL and a TG of about 6 min.”

What the sources say

• Not checked yetcites [58] Xie, J., and Wang, Q. (2020). Benchmarking machine learning algorithms on blood glucose prediction for Type 1 Diabetes in comparison with classical time-series models. IEEE Trans. Biomed. Eng., 1. source

In the paper

“Another option is to develop subject-specific algorithms, where a different model is trained for each subject, in order to take into account the large inter-individual variability characterizing individuals with T1D [52].”

What the sources say

• Not checked yetcites [52] Oviedo, 2016, A review of personalized blood glucose prediction strategies for T1DM patients, Int. J. Numer. Methods Biomed. Eng. source

In the paper

“However, one limitation of this study is that all the data were collected in a controlled inpatient environment. Further validation of the approach with data gathered under free-living conditions, and with different types of exercise, is required.”

What the sources say

• Not checked yetcites Reddy et al. [60] Reddy, 2019, Prediction of hypoglycemia during aerobic exercise in adults with type 1 diabetes, J. Diabetes Sci. Technol. source

In the paper

“The maximum TG was achieved for Gradient boosting trees and LSTM, which made it possible to predict the CGM data, on average, 7 min in advance.”

What the sources say

• Not checked yetcites [58] Xie, J., and Wang, Q. (2020). Benchmarking machine learning algorithms on blood glucose prediction for Type 1 Diabetes in comparison with classical time-series models. IEEE Trans. Biomed. Eng., 1. source

In the paper

“The data collected by activity trackers can be used to automatically detect exercise sessions [65], predict exercise-induced hypoglycemia [60], and if necessary, recommend the consumption of carbohydrates to avoid hypoglycemia.”

What the sources say

• Not checked yetcites [65] Dasanayake, 2015, Early detection of physical activity for people with type 1 diabetes mellitus, J. Diabetes Sci. Technol. source
• Not checked yetcites [60] Reddy, 2019, Prediction of hypoglycemia during aerobic exercise in adults with type 1 diabetes, J. Diabetes Sci. Technol. source

In the paper

“Amongst the possible applications, AI-enabled DSSs are promising tools that could potentially reduce the patient burden in diabetes management and improve glucose control.”

In the paper

“A prospective clinical validation proving the safety and effectiveness of such techniques is required to support the adoption of AI-based DSS applications in T1D management.”