Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas

Godoy, José Luis; Sereno, Juan E.; Rivadeneira, Pablo S.

doi:10.1016/j.bspc.2021.102715

Cited by 5 publications

(18 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature search identified a total of 1,561 potentially relevant publications. Nineteen publications reporting data from 17 studies (for 2 studies, 2 publications each were relevant, see Table 1 ), including 311 participants, met the inclusion criteria ( 59 – 66 , 68 , 70 , 71 , 73 – 75 , 82 – 85 , 87 ). Figure 1 shows the process of the literature search, screening, and selection in a PRISMA-style flow diagram ( 88 ).…”

Section: Resultsmentioning

confidence: 99%

“…The publications covered a wide age range, including pediatric ( 62 , 73 ), adolescent ( 62 , 64 , 65 ), and adult ( 59 – 61 , 63 , 66 , 68 , 70 , 71 , 74 , 75 , 82 – 85 , 87 ) populations. Fourteen publications included participants with T1DM ( 59 , 62 – 66 , 68 , 70 , 71 , 73 , 74 , 84 , 85 , 87 ), one publication included a sample of participants with T1DM or T2DM ( 75 ), one publication included participants with prediabetes or moderately controlled T2DM ( 83 ) and three publications included participants without diabetes ( 60 , 61 , 82 ). Publications included both controlled/inpatient ( 59 , 62 – 65 , 68 , 74 , 82 , 85 ) and free-living settings ( 60 , 61 , 66 , 70 , 71 , 73 , 75 , 83 , 87 ).…”

Section: Resultsmentioning

confidence: 99%

“…Fourteen publications included participants with T1DM ( 59 , 62 – 66 , 68 , 70 , 71 , 73 , 74 , 84 , 85 , 87 ), one publication included a sample of participants with T1DM or T2DM ( 75 ), one publication included participants with prediabetes or moderately controlled T2DM ( 83 ) and three publications included participants without diabetes ( 60 , 61 , 82 ). Publications included both controlled/inpatient ( 59 , 62 – 65 , 68 , 74 , 82 , 85 ) and free-living settings ( 60 , 61 , 66 , 70 , 71 , 73 , 75 , 83 , 87 ).…”

Section: Resultsmentioning

confidence: 99%

“…First, the number and type of performance metrics reported for the tested approaches differed substantially. Commonly reported performance metrics included the number of true and/or false positives and/or negatives (including frequencies per day and rates) ( 64 , 66 , 68 , 70 , 73 , 75 , 84 , 85 , 87 ), sensitivity ( 60 , 61 , 70 , 73 – 75 , 84 , 87 ), specificity ( 60 , 61 , 70 ), accuracy ( 61 , 70 ), precision ( 60 , 61 , 66 ), F1-score ( 60 , 61 , 66 ), Matthew’s correlation coefficient ( 60 , 61 ), Pearson’s correlations ( 83 ), detection time or time until an insulin bolus was delivered ( 59 , 62 – 66 , 70 , 73 – 75 , 84 ), change in glucose concentrations ( 62 , 65 , 84 , 85 ), and area under the curve ( 82 ). However, even when the same metrics were reported, their definition was sometimes inconsistent across publications.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Brummer,

Glasbrenner,

Hechenbichler Figueroa

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

BackgroundAccurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).ObjectiveIn a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?MethodsWe systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.ResultsOf 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.ConclusionSeveral CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Brummer,

Glasbrenner,

Hechenbichler Figueroa

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

show abstract

“…Inspired by this approach, the carbohydrate amount (unknown input) is estimated based on a feedback scheme where measured blood glucose (BG) and a predicted BG are compared. Glycemic behavior is predicted using a personalized model considering some parameters like insulin sensitivity factor (ISF), basal insulin rate (BIR), and insulin on board (IOB) [ 44 ] [ Figure 9 ].…”

Section: Meal Detection and Estimation Techniquesmentioning

confidence: 99%

Artificial Pancreas: A Review of Meal Detection and Carbohydrates Counting Techniques

Rodriguez

Villamizar

2022

Review of Diabetic Studies

View full text Add to dashboard Cite

OBJECTIVE: The development of an artificial pancreas is an open research problem that faces the challenge of creating a control algorithm capable of dosing insulin automatically and driving blood glucose to healthy levels. Many of these approaches, including artificial intelligence, are based on techniques that could result in and undesirable outcome because most of them include neither detect meal intake or meal size information. To overcome that issue, some meal count-detection algorithms reported in scientific publications have shown not only a good performance on blood glucose regulation but fewer hypoglicemia and hyperglycemia events too. METHODS: We reviewed the most relevant authors and publications and main databases (particularly SCOPUS and Google Scholar), focusing on algorithms of detection and estimation of meal intake from multiple approaches. RESULTS: A wide range of approaches and proposals have been found. The majority of them include trials on in silico patients rather than in vivo ones. Most of procedures require as inputs glucose samples from continuous glucose monitoring devices as basal insulin and bolus as well. Most of approaches could be grouped by 2 categories: mathematical model based and not model based. CONCLUSION: A combination of methods seems to reach better results.

show abstract

A step forward in food science, technology and industry using artificial intelligence

Esmaeily,

Razavi,

Razavi

2024

Trends in Food Science & Technology

View full text Add to dashboard Cite

Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas

Cited by 5 publications

References 26 publications

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Artificial Pancreas: A Review of Meal Detection and Carbohydrates Counting Techniques

A step forward in food science, technology and industry using artificial intelligence

Contact Info

Product

Resources

About