Building the world’s most advanced human digital twin
Predictive, Preventive, Personalized and Proactive health
A collective mission to build the world’s most advanced human digital twin. Designed to enable predictive, preventive, personalized and proactive health.
The human digital twin
Our research vision
- 01
Connect health data
Records, sensors and imaging brought into one consented, patient-owned picture.
- 02
Build evolving models
Open-source models, trained and validated through the Lab’s challenges.
- 03
Explore possible futures
Simulate what could happen next, and act before it does.
In 10 years, 50% of the world’s population will have their digital twin, directly accessible via their smartphone, ensuring universal access to the best prevention, prediction, and health personalization tools.
Rubens ValcyFounder of MyTwinExplore the work behind the mission.
MyTwin Lab at Ynov Campus’s Scientific Council
So many health innovations exist, yet so few reach the patients who could benefit from them. At Ynov Campus’s Scientific Council, Rubens Valcy presented the vision behind MyTwin and MyTwin Lab.
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Open-source AI for a second look at mammograms
MyTwin Athlete: a Vigilance Score for tennis players
Conversations with the people advancing health.
This AI analyzes your voice to detect stress, anxiety and Alzheimer's
Rubens Valcy, founder of MyTwin, with the teams behind the technology.
Life after a heart attack: the challenge no one talks about
A simple video of your face can reveal your state of health
Medical records: too many errors! Building a complete and secure record
Engineers, doctors, researchers, students, patients, building the future of health together.
- Alix, Student, Engineer
- Antoine, Entrepreneur, Developer
- Eric, Researcher, Entrepreneur
- FAANG, Developer
- Julien et Delphine, Patients
- Lara, Researcher, Entrepreneur
- Mahdi, Professor, Developer
- Patricia, AI Engineer
- Samir, Community Manager
- Camille, Student, Developer
- Mickaël, Student, Data Engineer
- Christyl, Student, Data Engineer
- Dorian, Student, Cybersecurity
- Victor, AI Engineer
- Fabrice, Patient
- Chloé et Christine, Mother and Daughter, Patients
- Eric, Data Engineer
- Jean-Marc, ePHD, Entrepreneur
- Laurent, Former Surgeon
- Sarah, 3D Artist
- Shane, Nurse, Professor, Entrepreneur
- Thomas, Entrepreneur
- André, Medical Student
- Duncan, Developer
- Asmaa, AI Engineer
- Shriya, AI Engineer
- Kunal, AI Engineer
- Nahum, Doctor
- Shamus, AI Engineer
- Gaurav, AI Engineer
- Aurélie, Nurse
- Mathieu, Finance, IT
- Régis, Doctor
- Vincent, Media
Top three contributors
Challenges
Mammography Segmentation
Delineate lesions on a mammogram at pixel level (segmentation mask), with an open-source model evaluated on Dice/IoU.
Mammography Classification
Classify a mammogram (normal / benign / malignant) or BI-RADS with an open-source model fine-tuned on public datasets, evaluated on AUC.
How advanced is a human digital twin?
Explore ourOur open framework to measure the maturity of human digital twins.
- Personalization82%
- Multimodality68%
- Longitudinality76%
- Prediction62%
- Validation58%
- Actionability71%
Create a digital twin for yourself, your patients, or your employees.
Create Your TwinTools and resources to help the community work on health challenges.
Latest Scientific Research
Search the health literature: topics, period, open access and journal impact.
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