Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery.

Explaining a model predicting quality of surgical practice: a first presentation to and review by clinical experts.

Purpose: Surgical Data Science (SDS) is an emerging research domain offering data-driven answers to challenges encountered by clinicians during training and practice. We previously developed a framework to assess quality of practice based on two aspects: exposure of the surgical scene (ESS) and the surgeon's profile of practice (SPP). Here, we wished to investigate the clinical relevance of the parameters learned by this model by (1) interpreting these parameters and identifying associated representative video samples and (2) presenting this information to surgeons in the form of a video-enhanced questionnaire.

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still debated. This work presents the development of a computer platform called Image and Radiation Dose BioBank (IRDBB) to manage research data produced in the context of the MEDIRAD project, a European project focusing on research on low doses in the context of medical procedures.

A multicentric IT platform for storage and sharing of imaging-based radiation dosimetric data.

Purpose: The MEDIRAD project is about the effects of low radiation dose in the context of medical procedures. The goal of the work is to develop an informatics service that will provide the researchers of the MEDIRAD project with a platform to share acquired images, along with the associated dosimetric data pertaining to the radiation resulting from the procedure.

Methods: The authors designed a system architecture to manage image data and dosimetric data in an integrated way.