According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

The disclosure generally relates to the artificial intelligence (AI) automatic methods, computer program product, and systems and methodology for dietary and medical treatment planning, food waste estimation, analyzing three-dimensional food image construction, measurement, nutrient estimation, nutritional assessment, evaluation, prediction and management. More particularly, the embodiments described herein relate to utilizing an AI-based algorithm that can automatically, detect food items from images acquired by cameras for dietary assessment, dietary planning, and for estimating food waste. In one aspect, the method may include food calorie estimation techniques using machine learning and computer vision techniques for dietary assessment. In another aspect, the tools may apply to personalized nutrition. The method may also include the automation of nutrition planning. In yet another aspect, the tools may apply to medical treatment planning, wherein meals and treatment plans are individualized explicitly for each user according to several unique characteristics associated with that user.

A system for generating an alimentary instruction set, the system comprising a computing device; a diagnostic engine operating on the computing device, wherein the diagnostic engine is configured to assemble a first training set, the first training set comprising a plurality of diagnostic outputs describing a plurality of health conditions and a plurality of correlated alimentary instruction sets; parse the first training set into at least a vector; train, using the at least a vector a machine learning model; receive an input to the trained machine learning model containing a diagnostic output; and generate an output to the trained machine learning model containing an alimentary instruction set.