Séminaire LIGM - Surgical Data Science

Le prochain séminaire de l'équipe A3SI du LIGM (unité mixte de recherche de l'Université Paris Est) aura lieu le jeudi 19 janvier de 13h30 à 14h30, ESIEE Paris (salle 260).

Surgical Data Science for Decision Making Support and Knowledge Discovery in Deep Brain Stimulation
Pierre Jannin
MediCIS, INSERM, Université de Rennes

Abstract: High frequency and continuous electrical stimulation of deep brain structures (DBS) has been demonstrated as an efficient minimally invasive surgical treatment for motor related diseases and recently for severe neuropsychological diseases. The quality of the clinical improvement, as well as the occurrence of motor, neuropsychological or psychiatric side effects strongly depend on the location of the electrodes. However, even though DBS provides excellent clinical results, there is no consensus in the neurosurgical community about the optimal location of the area to be stimulated as well as corresponding electrical parameters. It is also expected that this is different among patients. The choice of the best target is usually based on a combination of patient specific and generic anatomical, functional and clinical information and knowledge. Patient specific data and information are based on multimodal medical images, clinical and electrophysiological data, whereas most of the generic information and knowledge are implicit. To make it explicit, some groups suggested digital atlases; some atlases were computed from population analysis.

In this talk, I will introduce the surgical data science approach we studied, implemented and validated in the context of Deep Brain Stimulation. The main characteristics of our approach include: 1) computation of pre, intra and post-operative patient-specific models from multimodal medical images, clinical and electrophysiological data, 2) analysis of patient population for outlining common patterns and outcome, and 3) computation of generic models from population analysis to help pre, intra and post operative decisions and actions. It aims both at assisting surgical planning, performance and post operative programming and evaluation, as well as better understanding neurological phenomenon for knowledge discovery. Our approach is based on numeric and symbolic surgical data analysis. The clinical motivation is to improve targeting and post operative evaluation for better outcome and reduced side effects.