Annual-Report-2019

Dr Eric Lespessailles
CONSORTIUM COORDINATOR
SMART LOIRE VALLEY Programme
Eric Lespessailles M.D, HDR, Ph.D, is the Director of the Multimodal and multiscale Imaging of Bone and Cartilage
laboratory (EA 4708-I3MTO) and Vice scientific Director of the Translational Medicine Research Platform (Regional
Hospital of Orleans).
He is working as MD in the rheumatology department at Hospital Regional of Orleans in France. He is an Associate
Professor and received his Ph.D degree in Sciences and Techniques of Physical Activities and Sports at the University of
Orleans. He has qualified as an associate professor in Physiology.
KNEE OSTEOARTHRITIS PREDICTIVE IMAGING CONSORTIUM
Thanks to the large amount of studies, it is now established that imaging markers have the potential to provide predictive
models for knee osteoarthritis. The major issue with the rich literature is the lack of a consensus about a predictive
model. Nevertheless, it also has been shown that several algorithms might provide comparable results when applied
following common guidelines.
This consortium aims to unify the multiple models that have been developed and published over the past years. The
gathering of leading research teams studying the knee osteoarthritis aims to provide both a unified predictive tool but
also the guidelines for its establishment and the physiologic explanation of its components.
The first objective of this consortium is to provide a full and clear state of the art in the KOA predictive imaging area.
However, contrary to what is casually done, the aim is to apply every one of the previous methodologies on a unified
database, with common case / control definitions and using the native algorithms. This first milestone aims to provide
both with:
- A unified and extensively documented test database with identified subjects
- Guidelines for modeling generation and statistical analysis
- A standardised comparison of the existing algorithms / processing pipelines
Life & Health Sciences 2019
The second objective is to melt the knowledge accumulated by the original review / comparison into a single predictive
model. Such tool has to be defined not only based on radiographic imaging features, but also according to multi-modal
pathology-related knowledge. In addition, such modeling tool variations has to be investigated and reported along with
the proper model.
- A predictive model with explicitly documented and correlated features.
- A full analysis of the model variations (based on the input data, feature extraction methodology, algorithm
implementation…)
Finally, the ultimate objective is to package the consensus model into an open-access front-ended software module. This
part would be the achievement of the consortium, providing to both clinicians, scientists and pharmaceutical industries
a concrete reference tool for KOA prediction.
The consortium group already met twice in 2019 in June and November to pursue its mission and to work on the
preparation of a proposal to ANR-PRCE - AAPG2020 for a project entitled “MIMOSA” (Machine learnIng and Multimodal
imaging for knee OSteoArthritis prediction).
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