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Measurement of sugar levels in blood is the main means of diagnosing for diabetes and other complications of blood sugar levels. The established principle of the methodology for this is the extraction of blood from the subject and submission of the blood sample to chemical tests that determine the presence of substances, such as glucose, that indicate blood sugar levels. This principle is inherently intrusive; R&D into methods with this principle has the goal of improving convenience and minimizing amount of sampling needed, while maintaining reliable accuracy. There is also R&D into developing non-intrusive methods that estimate blood sugar levels without blood sampling, with the aim of producing results that can be comparable with intrusive methods. The common goal of either approach is making blood sugar measurement more convenient for as many people as possible. At this time of writing, non-intrusive methods have yet to replace the gold standard. A breakthrough in this matter can facilitate the implementation of machine learning in interpreting blood sugar levels.
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Wai Ti Chan (2025) studied this question.
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