Poor quality nutritional data

The problem

Poor quality nutritional data

The important role of food intake for disease prevention and treatment remains poorly understood.

Skepticism about nutritional science is widespread both in academia and in the public. A key contributor to the problem is how food intake is measured in the first place: food-frequency questionnaires (FFQs) and diet recalls are still the standards, despite their well-known weaknesses such as imprecision, dependence on human memory, lack of associated data such as the timing of food intake, etc.

While other aspects of health and behaviour measurements have evolved and improved steadily over the past decades (genomics, metagenomics, sensors, etc.), nutrition measurement has been stuck in time: it is still done the same way it was done decades ago.

The solution

The AI For Nutrition project

The AI For Nutrition project provides a comprehensive, scalable, and demonstrated solution to digital diet logging specifically designed for use in research settings. The technology platform has been developed at the Digital Epidemiology Lab at the Swiss Institute of Technology in Lausanne (EPFL) and has received support by multiple foundations including the Kristian Gerhard Jebsen Foundation and Leenaards Foundation. It combines three essential parts:

  • MyFoodRepo

    A mobile app (Android and iOS) for individuals to track food by picture taking or barcode scanning.

  • The Open Food Repo
    The Open Food Repo

    A community-driven open database for barcoded food products.

  • MyFoodRepo image analysis
    MyFoodRepo image analysis

    An annotation framework based on AI and human expertise for image-based food recognition.

See how digital diet logging will revolutionise nutritional science

Proof of principle


Proof of principle

Proof of principle was established by Digital Epidemiology Lab at EPFL in Switzerland prior to Santorio Foundation's involvement. The platform is being used by cohort participants on a daily basis.

  • Ongoing studies

    3 ongoing; 2 in the pipeline

  • App usage

    Used by study participants daily

  • Barcode database

    40k+ products (foodrepo.org)

  • Image recognition AI algorithm

    Trained on 50k+ images

The progress

Achieved milestones

Project goals for 2020 - 2023

The future

Project goals for 2020 - 2023

  • Improve accuracy

    Steady development of the MyFoodRepo AI Benchmark for best possible food image recognition.

  • Enhance ease of use

    Further development of the mobile application to provide a user-friendly experience.

  • Scale up internationally

    Establish MyFoodRepo in at least 6 new countries.

  • Reach self-sustaniability

    Ensure MyFoodRepo will become a non-profit service platform able to sustain itself.

The team

Project grantees and collaborators

The grantees and collaborators work together towards the goal of establishing the
AI For Nutrition platform as an open gold standard for the acquisition of accurate nutritional data.

How can you help create impact?


How can you help create impact?

With more resources, AI For Nutrition can be accelerated in different ways:

  • Expand into new countries
  • Use for specific clinical studies looking at nutrition
  • Improve the digital infrastructure
  • Improve user-friendlyness
  • Improve AI algorithms
Exploration phase
Theoretical feasibility
Prototyping / PoC
Scaling and implementation
AI for nutrition
is currently on this stage