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.

Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos.

PURPOSE : Evaluating the quality of surgical procedures is a major concern in minimally invasive surgeries. We propose a bottom-up approach based on the study of Sleeve Gastrectomy procedures, for which we analyze what we assume to be an important indicator of the surgical expertise: the exposure of the surgical scene. We first aim at predicting this indicator with features extracted from the laparoscopic video feed, and second to analyze how the extracted features describing the surgical practice influence this indicator.

Learning procedural skills with a virtual reality simulator: An acceptability study.

Background: Virtual Reality (VR) simulation has recently been developed and has improved surgical training. Most VR simulators focus on learning technical skills and few on procedural skills. Studies that evaluated VR simulators focused on feasibility, reliability or easiness of use, but few of them used a specific acceptability measurement tool.

Toward a standard ontology of surgical process models.

Purpose: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS.

Relevance of health level 7 clinical document architecture and integrating the healthcare enterprise cross-enterprise document sharing profile for managing chronic wounds in a telemedicine context.

The number of patients with complications associated with chronic diseases increases with the ageing population. In particular, complex chronic wounds raise the re-admission rate in hospitals. In this context, the implementation of a telemedicine application in Basse-Normandie, France, contributes to reduce hospital stays and transport.

Synthesis and Simulation of Surgical Process Models.

Virtual Reality for surgical training is mainly focused on technical surgical skills. We work on providing a novel approach to the use of Virtual Reality focusing on the procedural aspects. Our system relies on a specific work-flow generating a model of the procedure from real case surgery observation in the operating room.

Connections of the dorsolateral prefrontal cortex with the thalamus: a probabilistic tractography study.

Purpose: The dorsolateral prefrontal cortex (DLPFC) is a cortical area involved in higher cognitive functions, and at the center of the pathophysiology of mental disorders such as depression and schizophrenia. Considering these major roles and the development of deep brain stimulation, the object of this study was to assess the patterns of connectivity of the DLPFC with its main subcortical relay, the thalamus, with the help of probabilistic tractography.

Methods: We used T1-weighted imaging and diffusion data from 18 subjects from the Human Connectome Project.

Biomedical imaging ontologies: A survey and proposal for future work.

Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as "cell" or "image" or "tissue" or "microscope") that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

Purpose: The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial.

Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study.

Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge.

A multilayer ontology of instruments for neurological, behavioral and cognitive assessments.

Advances in neuroscience are underpinned by large, multicenter studies and a mass of heterogeneous datasets. When investigating the relationships between brain anatomy and brain functions under normal and pathological conditions, measurements obtained from a broad range of brain imaging techniques are correlated with the information on each subject's neurologic states, cognitive assessments and behavioral scores derived from questionnaires and tests. The development of ontologies in neuroscience appears to be a valuable way of gathering and handling properly these heterogeneous data - particularly through the use of federated architectures.

OntoVIP: an ontology for the annotation of object models used for medical image simulation.

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit.

Connectivity within the primary motor cortex: a DTI tractography study.

Purpose: Because of the motor function of the precentral area, the connections of the primary motor cortex by white matter fiber bundles have been widely studied in diffusion tensor imaging (DTI). Nevertheless, the connections within the primary motor cortex have yet to be explored. We have studied the connectivity between the different regions of the precentral gyrus in a population of subjects.

A virtual imaging platform for multi-modality medical image simulation.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.

Characterization of short white matter fiber bundles in the central area from diffusion tensor MRI.

Introduction: Diffusion tensor imaging and tractography allow studying white matter fiber bundles in the human brain in vivo. Electrophysiological studies and postmortem dissections permit improving our knowledge about the short association fibers connecting the pre- and postcentral gyri. The aim of this study was first to extract and analyze the features of these short fiber bundles and secondly to analyze their asymmetry according to the subjects' handedness.

The quest for standards in medical imaging.

This article focuses on standards supporting interoperability and system integration in the medical imaging domain. We introduce the basic concepts and actors and we review the most salient achievements in this domain, especially with the DICOM standard, and the definition of IHE integration profiles. We analyze and discuss what was successful, and what could still be more widely adopted by industry.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project.

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services.

SPECT and PET analysis of subthalamic stimulation in Parkinson's disease: analysis using a manual segmentation.

The subthalamic nucleus (STN) has become an effective target of deep-brain stimulation (DBS) in severely disabled patients with advanced Parkinson's disease (PD). Clinical studies have reported DBS-induced adverse effects on cognitive functions, mood, emotion and behavior. STN DBS seems to interfere with the limbic functions of the basal ganglia, but the limbic effects of STN DBS are controversial.

A hybrid system using symbolic and numeric knowledge for the semantic annotation of sulco-gyral anatomy in brain MRI images.

This paper describes an interactive system for the semantic annotation of brain magnetic resonance images. The system uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the Semantic Web standards. This knowledge is combined with graphical data, automatically extracted from the images by imaging tools.