Clinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5.94 seconds and 0.9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series. We could retrieve all patients and imaging tests of the prostate cancer cohort with a precision of 0.95 and a recall of 1. By leveraging the CMOVE method, our approach based on the Dicom protocol is scalable and domain-neutral. Future improvement will focus on performance optimization and de identification.

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. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

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. More precisely, the paper describes a semantic database linking dosimetric data (such as absorbed doses to organs) to the images corresponding to X-rays exposure (such as CT images) or scintigraphic images (such as SPECT or PET images) that allow measuring the distribution of a radiopharmaceutical. The main contributions of this work are: 1) the implementation of the semantic database of the IRDBB system and 2) an ontology called OntoMEDIRAD covering the domain of discourse involved in MEDIRAD research data, especially many concepts from the DICOM standard modelled according to a realist approach.

. Clinical data warehouses (CDW) allow the reuse of care data in a research context. Designing and operating CDWs require addressing interoperability, data enrichment and data modeling problems, among others. This work concerns the management of medical imaging data in CDWs. It proposes a data-driven approach for classifying radiological procedures using an ontology-based approach. This approach relies on the RadLex ontology and an imaging procedures terminology called RadLex Playbook, both developed by RSNA. We first created an ontology of the radiological procedures by merging the Playbook with the relevant extract of the RadLex ontology and enriched it with French terms using the UMLS meta thesaurus. Then, we developed a proof of concept of a radiological procedures data classifier that exploits the richness of RadLex ontology and the ontological reasoning and we assessed it using medical imaging data retrieved from two different facilities. Our results demonstrate feasibility and relevance of the approach. They also highlight differences in the methods of filling imaging procedure data in the two institutions, as well as some problems in the RadLex ontology. Based on this experience, this proof of concept will be refined to evolve towards a routinely usable classification tool supporting medical imaging data management in CDWs.

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. Twenty-nine patients underwent Sleeve Gastrectomy performed by two confirmed surgeons in a monocentric study. Features were extracted from spatial and procedural annotations of the videos, and an expert surgeon evaluated the quality of the surgical exposure at specific instants. The features were used as input of a classifier (linear discriminant analysis followed by a support vector machine) to predict the expertise indicator. Features selected in different configurations of the algorithm were compared to understand their relationships with the surgical exposure and the surgeon’s practice. The optimized algorithm giving the best performance used spatial features as input ($$\mathrm{Acc}=0.68, \mathrm{Sn}=0.72, \mathrm{Sp}=0.7$$). It also predicted equally the two classes of the indicator, despite their strong imbalance. Analyzing the selection of input features in the algorithm allowed a comparison of different configurations of the algorithm and showed a link between the surgical exposure and the surgeon’s practice. This preliminary study validates that a prediction of the surgical exposure from spatial features is possible. The analysis of the clusters of feature selected by the algorithm also shows encouraging results and potential clinical interpretations.

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. DICOM and non-DICOM data are stored in separated repositories, and the link between the two is provided through a semantic database, i.e., a database whose information schema in aligned with an ontology.RESULTS:The system currently supports CT, PET, SPECT, and NM images as well as dose reports. Currently, two workflows for non-DICOM data generated from dosimetric calculations have been taken into account, one concerning Monte Carlo-based calculation of organ doses in Chest CT, and the other estimation of doses in nontarget organs in 131I targeted radionuclide therapy of the thyroid.CONCLUSION:The system is currently deployed, thus providing access to image and related dosimetric data to all MEDIRAD users. The software was designed in such a way that it can be reused to support similar needs in other projects.

With the current advancement in the medical world, surgeons are faced with the challenge of handling many sources of medical information in more and more complex and technological Operating Rooms (ORs). Obviously, in the next generation ones, there will be an increasing number of video flows during the surgery (e.g. endoscopes, cameras, ultrasounds, etc.), which can be also displayed all over the OR in order to facilitate the task for the surgeon and to avoid any adverse events or problems related to inadequate communication in the OR. Additionally, other information needs to be shared, pre/post/during an operation, such as the history of the digital images related to the patient in the PACS and the metadata coming from medical sensors. Moreover, these medical videos captured from the OR can be either displayed on a large screen in the OR in order to provide the surgeon with more visibility, in this case via DICOM-RTV, or streamed outside the OR via a P2P solution. The latter one can serve various purposes such as for teaching medical student in real-time or for remote-expertise with a remote senior surgeons. Hence, this paper addresses the challenges of streaming DICOM-RTV video and metadata flows live from the operating room, typically during an ongoing surgery, in real-time to the outside world. A Proof of Concept is also presented in order to demonstrate the feasibility of our solution.

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. Objectives: The aim of the study was to assess acceptability and usability of a new VR simulator for procedural skill training among scrub nurses, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Participants: The simulator training system was tested with a convenience sample of 16 non-expert users and 13 expert scrub nurses from the neurosurgery department of a French University Hospital. Methods: The scenario was designed to train scrub nurses in the preparation of the instrumentation table for a craniotomy in the operating room (OR). Results: Acceptability of the VR simulator was demonstrated with no significant difference between expert scrub nurses and non-experts. There was no effect of age, gender or expertise. Workload, immersion and simulator sickness were also rated equally by all participants. Most participants stressed its pedagogical interest, fun and realism, but some of them also regretted its lack of visual comfort. Conclusion: This VR simulator designed to teach surgical procedures can be widely used as a tool in initial or vocational training.

This paper provides technical details regarding the worldwide premiere implementation of the DICOM Real-Time Video (DICOM-RTV), in which an experimental setup was deployed at the Rennes University Hospital in France during five Urological surgeries. The solution allows the real-time retransmission of medical images from different equipment in the operating room in a perfectly synchronized way. In this paper, we show how we have been able to transport and synchronize the video signal from a camera recording the hand gestures of the surgeon, and from an endoscope used by the same surgeon. This paper also demonstrates the interest of using the new DICOM-RTV standard in order to improve the surgical gestures precision and to provide precise, real-time and synchronized instructions to the different operators during a surgery, toward the next generation operation rooms.

In order to overcome the challenges of managing real-time transfer of video, and/or audio, and associated medical metadata inside the medical theaters (e.g., operating room), a new DICOM communication service standard emerged. Its main objective is to deliver synchronized videos (potentially synchronized with their corresponding metadata) in real-time to surgeons during a surgery inside an operating room. Moreover, it allows, on one side, the transmission of real-time videos to subscribers with a quality of service comparable to the one inside the operating room, and on the other side, provides a standard that will allow the interoperability between the different medical equipment that produce/consume the media essences. This new DICOM extension is called DICOM Real-Time Video (DICOM-RTV). This paper is an introduction to this extension. It mainly presents the different challenges solved by this extension, illustrates it with relevant use cases, and provides the global architecture of the DICOM-RTV system.

PurposeThe 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 initiativeOntoSPM Collaborative Actionwith the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative.MethodsA workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM.ResultsThe workshop led to the OntoSPM Collaborative Actionlaunched in mid-2016with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use.ConclusionThe OntoSPM Collaborative Action has been in operation for 24months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

Imaging biomarkers refer to radiological measurements that characterize biological processes of imaged subjects and help clinicians particularly in the assessment of therapeutic responses and the early prediction of pathologies. Several imaging features (size of a lesion, volume of a tumor, blood perfusion in a specific anatomical region, anisotropic water diffusion in a particular tissue region, etc.) are quantified and reported in the clinical practice. The growth of the number of research studies addressing imaging biomarkers and the increasing use of these measurements in the radiological routine necessitates the use of semantic research tools. The use of semantic technologies will enable to efficiently retrieve imaging-related data and to enhance the interoperability in the biomedical field. While many efforts have been conducted regarding the definition of a standardized vocabulary to support the sharing of the imaging biomarker knowledge, the definition of the term “imaging biomarker” stills inconsistent. In this paper, we introduce our motivation for semantically describing this concept and we outline shortcomings of the state-of-the-art methods. Here, we propose a semantic representation of the imaging biomarker concept that is based on the articulation of its three main semantic axes, namely the measured quality, the measurement tool and the decision tool. The developed ontology is called the Imaging Biomarker Ontology (IBO) and uses existing biomedical ontologies. A preliminary use case is studied to illustrate the utility of IBO in annotating quantitative and qualitative imaging data from the TCGA (The Cancer Genome Atlas) collection.

Background: The future increase of chronic diseases justifies the development of telemedicine for following up patients outside of the hospital. However, current telemedicine applications are disease-specific whereas chronic diseases are often associated with comorbidities. Methods: We show that the use of interoperability standards for telemedicine systems makes it possible to build a telemedicine platform (with several medical devices) that can be shared between several diseases workflows. To remotely follow up the patient's vital signs, health professionals need to access to the context description and the relevant background information. The implementation of a health data model that meets the needs of practitioners seems to be an appropriate solution to this problem. Results: To validate our architecture model of telemedicine application, we conducted various communication tests of vital signs to prove the interoperability of the patient system. The second experiment, we have developed one HL7 CDA document which collects several vital signs to a medical data exchange system among healthcare providers. This led us to propose a telemedicine application model, which is not only in conformity with the Health Information Systems Interoperability Framework (HIS-IF) of the "Agence des Systemes d'Information Partages de Sante" (ASIP), but also constitutes a proposed extension of this framework to the patient's home. Conclusion: For the remote monitoring of patients with Multiple chronic conditions, we developed a generic architecture that allows different telemedicine applications associated with specific diseases to share a common technical platform. This work was to propose at first, a health data model for the patient's vital signs, and, on the other hand second, a study of communication standards to achieve an interoperable system. (C) 2018 AGBM. Published by Elsevier Masson SAS. All rights reserved.

The main objective of this work is to facilitate the identification, sharing and reasoning about cerebral tumors observations via the formalization of their semantic meanings in order to facilitate their exploitation in both the clinical practice and research. We have focused our analysis on the VASARI terminology as a proof of concept, but we are convinced that our work can be useful in other biomedical imaging contexts. In this paper, we propose (1) a methodology, a domain ontology and an annotation tool for providing unambiguous formal definitions of neuroimaging data, (2) an experimental work on the REMBRANDT dataset to demonstrate the added value of our work over existing methods, namely DICOM SR and the AIM model.

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. This article presents the different technologies created in the context of our project and their relations as other components of our workflow.

L’étude de la connectivité cérébrale est possible grâce aux séquences de diffusion en IRM cérébrale [1]. Dans le cadre du développement de la stimulation thalamique pour traiter les patients épileptiques pharmaco-résistants, nous nous sommes intéressés aux connections entre le cortex dorsolatéral préfrontal (CDLPF), impliqué dans les fonctions cognitives [2], et le thalamus, noyau intégrateur. Le but de l’étude était d’évaluer la connectivité du CDLPF avec le thalamus à l’aide d’outils de tractographie chez des sujets sains. Le CDLPF et les noyaux du thalamus ont été segmentés sur 18 IRM de sujets sains [3], IRM issues du Human Connectome Project. À l’aide de tractographie probabiliste [4], la connectivité fut évaluée par un index de connectivité (IC) évaluant la force de la connexion entre deux structures, et par un index d’asymétrie (IA) pour analyser la variabilité inter-hémisphérique. L’analyse du IC a mis en évidence des connections préférentielles et significatives entre le CDLPF et 3 noyaux thalamiques : le noyau dorsomédial, le groupe nucléaire antérieur et le noyau centromédian droit (p < 0,05). La plupart des sujets (15 sur 18) avaient une prédominance droite pour les connections thalamo-corticales du CDLPF sans lien avec la latéralité manuelle. Malgré un grand nombre de faisceaux autour du thalamus, la méthode de tractographie probabiliste a montré une connectivité préférentielle du CDLPF avec les groupes nucléaires antérieurs et médio-dorsaux. Malgré des biais méthodologiques, les résultats obtenus sont concordants avec les études anatomiques [5]. Cette étude préliminaire a permis de mettre au point l’analyse de tractographie probabiliste, qui sera ensuite utilisée en pratique clinique pour l’analyse des effets secondaires de la stimulation du noyau antérieur du thalamus chez les patients épileptiques sévères.

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. The DLPFC and the thalamic nuclear groups were defined using the combination of atlases, sulcogyral anatomy and cytoarchitectonic data. Probabilistic tractography was performed from the DLPFC to the thalamus. The patterns of connectivity were assessed using two indexes: (1) a connectivity index (CI) which evaluate the strength of connection (2) an asymmetry index (AI) which explores the inter-hemispheric variability. RESULTS:The analysis of CI showed significant connections between the DLPFC and the dorsomedial nuclei (p < 0.05), the anterior nuclear groups (p < 0.05) and the right centromedian nucleus (p < 0.05). No link was found between handedness and AI (p > 0.05). Most of subjects (15/18) had a right predominance of the thalamo cortical connections of the DLPFC. CONCLUSIONS:Probabilistic tractography appears as a valuable non-invasive tool for the exploration of the thalamocortical connections between the dorsolateral prefrontal cortex and thalamic nuclei. It allowed to show different inter-hemispheric patterns of connectivity, and highlighted the centromedian nucleus as a key subcortical relay of executive functions.

In surgical training, Virtual Reality systems are mainly focused on technical surgical skills, leaving out procedural aspects. Our project aims at providing a novel approach to the use of Virtual Reality addressing this point. In our project, we propose an innovative workflow to integrate a generic model of the procedure, generated from real case surgery observation, as the scenarios model in the virtual reality training system. In this article we present how the generic procedure model is generated and its integration in the virtual environment.