Nutrigenics, an AI startup founded by MBZUAI PhD students, is developing a platform to improve dietetics. The platform features 200,000 tagged recipes and a vision language model (VLM) that analyzes meal photos with 70% accuracy. Nutrigenics aims to bridge the gap between clinical advice and daily eating habits, enabling dieticians to monitor patient progress more effectively. Why it matters: This technology can personalize nutrition at scale in the region and improve adherence to dietary guidelines, addressing a critical need in preventative healthcare.
Dr. Paolo Sassone-Corsi from UC Irvine spoke at KAUST's 2019 Winter Enrichment Program about circadian rhythms. He discussed how modern lifestyles disrupt our internal clocks, impacting our health and metabolism. Studies show that the timing of food intake affects weight gain, with eating late at night causing metabolic stress. Why it matters: This highlights the importance of circadian rhythm research for understanding and mitigating the health consequences of modern lifestyles in the region.
KAUST researchers are exploring the link between nutrition and brain-energy metabolism to address cognitive decline, dementia, and Alzheimer’s disease. Dr. Pierre Magistretti and Dr. Johannes le Coutre are collaborating on ways to merge brain-energy metabolism research into the field of nutrition. They published an article entitled “Goals in Nutrition Science 2015-2020” in the journal Frontiers in Nutrition. Why it matters: This research could lead to nutritional interventions to hinder or prevent cognitive decline, offering a new approach beyond traditional drug treatments.
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This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.