Centre National De La Recherche Scientifique

Ecole Polytechnique

Institut National De La Sante Et De La Recherche Medicale

Assistance Publique-hopitaux De Paris

Sorbonne Université

Institut Du Cerveau Et De La Moelle Epiniere

Inria Institut National De Recherche En Informatique Et an Automatique

Université De Paris 5 René Descartes

Inria Institut National De Recherche En Informatique Et En Automatique

Stéphanie Allassonniere

Erwan Le Pennec

Stanley Durrleman

Jean-Baptiste Schiratti

Remi Besson

Olivier Colliot

A real-time diagnosis aid method including: (a) providing a decision-support system for autonomous medical diagnosis to a user of a medical system, the system comprising a display module and software modules embodied on a computer readable medium; (b) generating on the display module an ordered list of diagnostic clues to look at by the user according to the diagnostic clues already filled in as absent or present, the ordered list being based on the relevance of looking at respective diagnostic clues to quickly lead to a diagnosis by the user; (c) receiving from the user, information on presence or absence of one or more diagnosis clues listed in the ordered list; and (d) processing the received information to update the ordered list and provide on the display module an indication of the probability of each possible diagnosis.

Real-time diagnosis aid method and decision-support for medical diagnosis to a user of a medical system

Provided herein is a method for determining the temporal progression of a biological phenomenon which may affect a studied subject, the method including the steps of providing first data relative to biomarkers for the studied subject, the biomarkers being relative to the progression of the biological phenomenon, providing a numerical model, converting the first data into at least one point on the same Riemann manifold, and using a numerical model to determine a temporal progression for the biological phenomenon for the studied subject, the numerical model being a function in a Riemann manifold, the numerical model associating to values of biomarkers a temporal progression trajectory for the biological phenomenon and data relative to the dispersion of the progression trajectory for the biological phenomenon among a plurality of subjects, the numerical model being obtained by using a stochastic approximation in an expectation-maximization technique on data relative to biomarkers.

Method for determining the temporal progression of a biological phenomenon and associated methods and devices

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Paris, France

Stéphanie Allassonniere