[Clinical photography by smartphone in plastic surgery and protection of personal data: Development of a secured platform and application on 979 patients]. / Photographie clinique par smartphone en chirurgie plastique et protection des données personnelles : développement d'une plateforme sécurisée et application sur 979 patients.

BACKGROUND: The clinical photography in plastic and reconstructive surgery has known a numerical breakthrough. The storage of online data, massive means of analysis such as facial recognitions algorithms poses a serious issue when it comes to the protection of personal data. We will assess a platform's benefits in connection with the computerized medical record, which will allow keeping the photos filed and centralized in a smart and secure manner. METHOD: We interviewed 300 plastic surgeons about the role of smartphone in their clinical practice. Concomitantly, we developed an innovative platform called Surgeon©, a secure way to index, file and send photographs with a smartphone on our hospital's server. Each photographic sequence was qualified using a specific form. We then collected prospectively, between May 1st 2017 and March 30th 2018, the number of patients photographed, the number of sequences and photographs taken and the average number of sequences per patient. RESULTS: Out of 86 French plastic surgeons surveyed, 81% say that they could not go on with their daily practice today without their smartphone. Photographs taken were stored in their smartphones (50%) or synced with virtual storage (25.6%). A majority (80.2%) would use a dedicated secured smartphone application. Our application allowed us to photograph 979 patients, or 2345 sequences and 8112 photographs, with an average of 2.28 sequences per patient. CONCLUSION: Thanks to its ergonomics and security, this platform can be set up in a hospital ward and beyond.

Radiomics: A primer for the radiation oncologist.

PURPOSE: Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS: A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS: A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION: Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

[Basis and perspectives of artificial intelligence in radiation therapy]. / Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology.

[Artificial intelligence applied to radiation oncology]. / Intelligence artificielle appliquée à la radiothérapie.

Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies.

An architecture for genomics analysis in a clinical setting using Galaxy and Docker.

Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe.

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

[Vertebral trabecular network analysis on CT images]. / Analyse de la trame osseuse vertébrale sur coupes scanographiques.

PURPOSE: Comparing texture analysis, density measurement and visual quantification of trabecular network on spine CT images, to better evaluate bone architecture in osteoporosis. METHOD AND MATERIALS: Seventeen patients, aged 19 to 84 years, were included. One patient presented osteoporotic fractures. High resolution computed tomographic (HR-CT) images of the third lumbar vertebra were acquired using a Somatom 4 plus CT (Siemens) in a strict axial orientation with FOV of 12 cm and slice thickness of 1 mm. The size of the Region Of Interest was 1,6 cm(2). Three analyses were performed on this ROI: Density (in Hounsfield Unity), texture analysis (run length) and features inspired from bone histomorphometry (Bone Volume/Tissue Volume). RESULTS: Density measurement, run length methods and BV/TV provided consistent results with regards to age. Indeed density, run length and BV/TV results were lower for older patients with more advanced bone trabeculra alterations. CONCLUSION: Only BV/TV and run length parameters seemed to show additional information on trabecular network architecture. The contribution of these two measurements to diagnose and classify osteoporosis will be the goal of a clinical study.

Clinical data integration model. Core interoperability ontology for research using primary care data.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance. OBJECTIVES: TRANSFoRm's general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care. METHODS: TRANSFoRm utilizes a unified structural / terminological interoperability framework, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care. RESULTS: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm's use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM. CONCLUSION: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

Evaluation of the Unified Medical Language System as a medical knowledge source.

OBJECTIVE: The authors evaluated the use of the Unified Medical Language System (UMLS) as a medical knowledge source for the representation of medical procedures in the MAOUSSC system. DESIGN: MAOUSSC, a multiaxial coding system, was used for the representation of 1500 procedures from 15 clinical specialties, using UMLS concepts (augmented by full sources for three new vocabularies being added to the UMLS) and relationships whenever possible. Evaluation criteria for the UMLS included (1) completeness of representation of concepts and of inter-concept relationships, (2) consistency in the categorization of both concepts and inter-concept relationships, and (3) usability, including adaptability of the UMLS to a foreign language (French), its suitability to a geographic region with different medical practices than the USA, and issues relative to the annual update changes in the test vocabularies. RESULTS: During the MAOUSSC trial, the number of missing concepts or relationships identified in the augmented UMLS sources was deemed to be inconsequential relative to overall project goals. "Missing" UMLS inter-concept relationships were identified, although they were small in number. Some inconsistencies in the UMLS were noted, especially in the area of hierarchic relationships. CONCLUSION: After UMLS was used for five years as a knowledge source for representing 1500 complex medical procedures in MAOUSSC, its value is considered significant. Future editions of the UMLS are expected to improve representation of inter-concept relationships and global consistency.

A Web terminology server using UMLS for the description of medical procedures.

The Model for Assistance in the Orientation of a User within Coding Systems (MAOUSSC) project has been designed to provide a representation for medical and surgical procedures that allows several applications to be developed from several viewpoints. It is based on a conceptual model, a controlled set of terms, and Web server development. The design includes the UMLS knowledge sources associated with additional knowledge about medico-surgical procedures. The model was implemented using a relational database. The authors developed a complete interface for the Web presentation, with the intermediary layer being written in PERL. The server has been used for the representation of medico-surgical procedures that occur in the discharge summaries of the national survey of hospital activities that is performed by the French Health Statistics Agency in order to produce inpatient profiles. The authors describe the current status of the MAOUSSC server and discuss their interest in using such a server to assist in the coordination of terminology tasks and in the sharing of controlled terminologies.

Automated coding of patient discharge summaries using conceptual graphs.

In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

Computer assisted medical diagnosis using the Web.

The ADM (Aide au diagnostic Medical) project was started 15 years ago and was the first telematic project for physicians in France using the MINITEL terminal. The knowledge base contains information on more than 10000 diseases from all pathological fields, using more than 100000 signs or symptoms. The ADM system has two main functionalities for physicians: consultation of diseases descriptions and list of diseases containing one or more symptoms. The ADM knowledge base is supported by a relational database management system (DBMS ORACLE) and we developed a Web interface using the Perl language to produce HTML pages for the web server. We will describe our experience on redesigning a large existing medical knowledge base for diffusion on the web Internet.

A network of web multimedia medical information servers for a medical school and university hospital.

Modern medicine requires a rapid access to information including clinical data from medical records, bibliographic databases, knowledge bases and nomenclature databases. This is especially true for University Hospitals and Medical Schools for training as well as for fundamental and clinical research for diagnosis and therapeutic purposes. This implies the development of local, national and international cooperation which can be enhanced via the use and access to computer networks such as Internet. The development of professional cooperative networks goes with the development of the telecommunication and computer networks and our project is to make these new tools and technologies accessible to the medical students both during the teaching time in Medical School and during the training periods at the University Hospital. We have developed a local area network which communicates between the School of Medicine and the Hospital which takes advantage of the new Web client-server technology both internally (Intranet) and externally by access to the National Research Network (RENATER in France) connected to the Internet network. The address of our public web server is http:(/)/www.med.univ-rennesl.fr.

[Analysis of medical catalog terms using the MAOUSSC model: application to Gyncecology-Obstetrics]. / Analyse des libellés des actes du catalogue des actes médicaux à l'aide du modèle MAOUSSC: application à la Gynècologie-Obstétrique.

BACKGROUND: The Model of Assistance and Orientation of a User within a System of Coding (MAOUSSC) used to describe activity in human medicine was used to analyse French medical nomenclature in Gynecology and Obstetrics. METHODS: French medical nomenclature for gynecology and obstetrics was translated with the multiaxial model (MAOUSSC) to allow critical analysis. RESULTS: All the 119 medical acts in the French nomenclature involving the female genital tract and obstetrics could be translated with the model. 24% of the acts were imprecise, ambiguous or implicit: the nature of the procedure was not explicit in 7 cases, the surgical route was not given in 86 and the surgical instrumentation not named in 75. Activities involving numerous medical specialties and ambulatory activity can be described with the MOUSSC model. CONCLUSION: The MAOUSSC model is still in the experimental stage. It is however easy to implement, has a high potential for describing various medical acts and is suitable for the description of gynecological and obstetrical activity both in terms of economical and medical efficacy.

Methods for exploring the semantics of the relationships between co-occurring UMLS concepts.

OBJECTIVE: To characterize the relationships among UMLS concepts that co-occur as MeSH descriptors in MEDLINE citations (1990-1999). DESIGN: 18,485 UMLS concepts involved in 7,928,608 directed pairs of co-occurring concepts were studied. For each directed pair of concepts C1-C2: (i) the "family" of C1 was built, using the UMLS Metathesaurus, and we tested whether or not C2 belonged to C1's family; (ii) we used the semantic categorization of Metathesaurus concepts through the UMLS Semantic Network and Semantic Groups to represent the semantics of the relationships between C1 and C2. RESULTS: In 6.5% of the directed pairs, the co-occurring concept C2 was found within the "family" of C1. Detailed results are given. The most frequent co-occurrences involved "Chemicals and Drugs" and "Chemicals and Drugs", as well as "Disorders" and "Chemicals and Drugs". DISCUSSION: This work takes advantage of both symbolic and statistical information represented in the UMLS, and analyzes their overlap. Further research is suggested.

Aggregating UMLS semantic types for reducing conceptual complexity.

The conceptual complexity of a domain can make it difficult for users of information systems to comprehend and interact with the knowledge embedded in those systems. The Unified Medical Language System (UMLS) currently integrates over 730,000 biomedical concepts from more than fifty biomedical vocabularies. The UMLS semantic network reduces the complexity of this construct by grouping concepts according to the semantic types that have been assigned to them. For certain purposes, however, an even smaller and coarser-grained set of semantic type groupings may be desirable. In this paper, we discuss our approach to creating such a set. We present six basic principles, and then apply those principles in aggregating the existing 134 semantic types into a set of 15 groupings. We present some of the difficulties we encountered and the consequences of the decisions we have made. We discuss some possible uses of the semantic groups, and we conclude with implications for future work.

A method for an automated decomposition of a complex term into its conceptual components.

The paper deals with the improvement of the MAOUSSC model (Modèle d'Aide et d'Orientation d'un Utilisateur au Sein des Systèmes de Codage) and system. Its specific purpose is the automation of the description of medical and surgical procedures. We have developed an automatic decomposition method using a linguistic and conceptual approach based on the UMLS knowledge base. This work concerns the processing of 100 procedure wordings from the digestive surgery domain. We introduce a prototype of such a system automating the decomposition through a web interface.

Design of a CSCL environment for clinical reasoning learning and problem-based learning in medicine.

The medical curriculum is changing, student-centered learning is currently used in medical schools. Problem-based Learning and Clinical Reasoning Learning develop the students' reasoning strategies. CSCW (Computer-Supported Cooperative Work) technology is used in Problem-Based Learning systems. We have designed a CSCL (Computer-Supported Collaborative Learning) environment for improving group coordination and communication in Clinical Reasoning Learning sessions. To support these new educative technologies, a prototype has been developed.

Automatic enrichment of the unified medical language system starting from the ADM knowledge base.

The Unified Medical Language System (UMLS) project aims to provide a repository of terms, concepts and relationships from several medical classifications. This work describes the possibility to enrich automatically with meaningful links the UMLS database by using description of diseases from another knowledge base, in our case ADM (Aide au Diagnostic Medical). In spite of the constraints and the difficulties to qualify the interconcept links, the results show that it is possible to find and create new links from a french knowledge database to the UMLS one. One of the interests of this work is that the automated learning of the connections could be used with others knowledge databases like expert system databases.

Sharing medical data for patient path analysis with data mining method.

The Agora Data project started in October 1997 in France. The objective was to share medical data between several medical institutions to analysis medical care pathways for patients that suffer from low back pain. The analysis of the medical records decomposed in three steps allowed us to produce knowledge on medical contacts of patients with the health care system. In order to study the relations between these contacts, we created medical path of patients within the framework of the possible contacts we had isolated. This work relates the implementation and the first results of the pilot study.