ODIASP: Clinically Contextualized Image Analysis Using the PREDIMED Clinical Data Warehouse, Towards a Better Diagnosis of Sarcopenia - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Studies in Health Technology and Informatics Année : 2022

ODIASP: Clinically Contextualized Image Analysis Using the PREDIMED Clinical Data Warehouse, Towards a Better Diagnosis of Sarcopenia

Résumé

Big Data and Deep Learning approaches offer new opportunities for medical data analysis. With these technologies, PREDIMED, the clinical data warehouse of Grenoble Alps University Hospital, sets up first clinical studies on retrospective data. In particular, ODIASP study, aims to develop and evaluate deep learning-based tools for automatic sarcopenia diagnosis, while using data collected via PREDIMED, in particular, medical images. Here we describe a methodology of data preparation for a clinical study via PREDIMED.

Dates et versions

hal-03836846 , version 1 (02-11-2022)

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Katia Charrière, Pierre-Ephrem Madiot, Svetlana Artemova, Pungponhavoan Tep, Christian Lenne, et al.. ODIASP: Clinically Contextualized Image Analysis Using the PREDIMED Clinical Data Warehouse, Towards a Better Diagnosis of Sarcopenia. Studies in Health Technology and Informatics, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220271⟩. ⟨hal-03836846⟩
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