4 ways machine learning is fixing to finetune clinical nutrition AI in Healthcare – AI in Healthcare

1. Diet optimization. A machine learning model for predicting blood sugar levels after people eat a meal was significantly better at the task than conventional carbohydrate counting, the authors report. The algorithms creators used the tool to compose good (low glycemic) and bad (high glycemic) diets for 26 participants.

For the prediction arm, 83% of participants had significantly higher post-prandial glycemic response when consuming the bad diet than the good diet, Limketkai and colleagues note. This technology has since been commercialized with the Day Two mobile application on the front.

2. Food image recognition. A primary challenge in alerting dieters to likely nutritional values and risks going by photos snapped on smartphones is the sheer limitlessness of possible foods, the authors point out. An early neural-network model developed at UCLA by Limketkai and colleagues achieved impressive performance in training and validating 131 predefined food categories from more than 222,000 curated food images.

However, in a prospective analysis of real-world food items consumed in the general population, the accuracy plummeted to 0.26 and 0.49, respectfully, write the authors of the present paper. Future refinement of AI for food image recognition would, therefore, benefit on training models with a significantly broader diversity of food items that may have to be adapted to specific cultures.

3. Risk prediction. Machine learning algorithms beat out conventional techniques at predicting 10-year mortality related to cardiovascular disease in a densely layered analysis of the National Health and Nutrition Examination Survey (NHANES) and the National Death Index.

A conventional model based on proportional hazards, which included age, sex, Black race, Hispanic ethnicity, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, antihypertensive medication, diabetes, and tobacco use appeared to significantly overestimate risk, Limketkai and co-authors comment. The addition of dietary indices did not change model performance, while the addition of 24-hour diet recall worsened performance. By contrast, the machine learning algorithms had superior performance than all [conventional] models.

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4 ways machine learning is fixing to finetune clinical nutrition AI in Healthcare - AI in Healthcare