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

Abstract

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.