The Role of Definitions in Biomedical Concept Representation

The Foundational Model (FM) of anatomy, developed as an anatomical enhancement of UMLS, classifies anatomical entities in a structural context. Explicit definitions have played a critical role in the establishment of FM classes. Essential structural properties that distinguish a group of anatomical entities serve as the differentiae for defining classes. These, as well as other structural attributes, are introduced as template slots in Protege, a frame-based knowledge acquisition system, and are inherited by descendants of the class. A set of desiderata has evolved during the instantiation of the FM for formulating definitions. We contend that 1. these desiderata generalize to non-anatomical domains and 2. satisfying them in constituent vocabularies of UMLS would enhance the quality of information retrievable through UMLS

Computational medical imaging for total knee arthroplasty using visualitzation toolkit

This project is presented as a Master Thesis in the field of Civil Engineering, Biomedical specialization. As the project of an Erasmus exchange student, this thesis has been under supervision both the Universite Livre de Bruxelles and the Universitat Politecnica de Catalunya. The purpose of this thesis to put in practice all the knowledges acquired during this Master in Industrial Engineering in UPC and to be a support for medical staff in total knee arthoplasty procedures. Prof. Emmanuel Thienpont has been working for years as orthopaedic surgeon at the Hospital Sant Luc, Brussels. His years of work and research have been mainly focused on Total Knee Arthroplasty or TKA. During one of the most important steps of this procedure, the orthopaedic surgeon has to cut the head of the femur following two perpendicular cutting planes. Nevertheless, the orientation of these planes are directly dependant of the femur constitution. This Master Thesis has been conceived in order to offer the surgeon a tool to determine the proper direction planes in a previous step before the surgical procedure. This project pretends to give the surgeon an openfree computational platform to access to patient geometrical and physiological information before involving the subject in any invasive procedure

The Digital Anatomist Foundational Model Server

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NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

Profile of On-Line Anatomy Information Resources: Design and Instructional Implications

This study is based on a review of 40 on-line anatomy web resources compiled from sites selected from our own searches as well as sites reviewed and published by an external group (Voiglio et al., 1999, Surg. Radiol. Anat. 21:65-68; Frasca et al., 2000, Surg. Radiol. Anat. 22:107-110). The purpose of our survey was to propose criteria by which anatomy educators could judge the characteristics of the currently available web-based resources for incorporation into the courses they teach. Each site was reviewed and scored based on a survey matrix that included four main categories: 1). site background information, 2). content components, 3). interactivity features, and 4). user interface design components. The average score of the reviewed sites was 3.3 of the total possible score of 10, indicating the limited use of computer-based design features by the majority of sites. We found, however, a number of programs in each of the survey categories that could serve as prototypes for designing future on-line anatomy resources. From the survey we conclude that various design features are less important than the comprehensiveness, depth, and logical organization of content. We suggest that the content should be sufficient for supporting explicitly defined educational objectives, which should target specific end-user populations. The majority of anatomy programs currently accessible on-line fall short of these requirements. There is a need for a coordinated and synergistic effort to generate a comprehensive anatomical information resource that is of sufficient quality and depth to support higher levels of learning beyond the memorization of structure names. Such a resource is a prerequisite for meaningful on-line anatomy education

The Foundational Model of Anatomy Ontology

Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Assessment in anatomy

From an educational perspective, a very important problem is that of assessment, for establishing competency and as selection criterion for different professional purposes. Among the issues to be addressed are the methods of assessment and/or the type of tests, the range of scores, or the definition of honour degrees. The methods of assessment comprise such different forms such as the spotter examination, short or long essay questions, short answer questions, true-false questions, single best answer questions, multiple choice questions, extended match questions, or several forms of oral approaches such as viva voce examinations.Knowledge about this is important when assessing different educational objectives; assessing educational objectives from the cognitive domain will need different assessment instruments than assessing educational objectives from the psychomotor domain or even the affective domain.There is no golden rule, which type of assessment instrument or format will be the best in measuring certain educational objectives; but one has to respect that there is no assessment instrument, which is capable to assess educational objectives from all domains of educational objectives.Whereas the first two or three levels of progress can be assessed by well-structured written examinations such as multiple choice questions, or multiple answer questions, other and higher level progresses need other instruments, such as a thesis, or direct observation.This is no issue at all in assessment tools, where the students are required to select the appropriate answer from a given set of choices, as in true false questions, MCQ, EMQ, etc. The standard setting is done in these cases by the selection of the true answer

Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

Terminologia Anatomica; Considered from the Perspective of Next-Generation Knowledge Sources

This report examines the semantic structure of Terminologia Anatomica, taking one randomly selected page as an example. The focus of analysis is the meaning imparted to an anatomical term by virtue of its location within the structured list. Terminologia’s structure expressed through hierarchies of headings, varied typographical styles, indentations and an alphanumeric code implies specific relationships between the terms embedded in the list. Together, terms and relationships can potentially capture essential elements of anatomical knowledge. The analysis focuses on these knowledge elements and evaluates the consistency and logic in their representation. Most critical of these elements are class inclusion and part-whole relationships, which are implied, rather than explicitly modeled by Terminologia. This limits the use of the term list to those who have some knowledge of anatomy and excludes computer programs from navigating through the terminology. Assuring consistency in the explicit representation of anatomical relationships would facilitate adoption of Terminologia as the anatomical standard by the various controlled medical terminology (CMT) projects. These projects are motivated by the need for computerizing the patient record, and their aim is to generate machineunderstandable representations of biomedical concepts, including anatomy. Because of the lack of a consistent and explicit representation of anatomy, each of these CMTs has generated it own anatomy model. None of these models is compatible with each other, yet each is consistent with textbook descriptions of anatomy. The analysis of the semantic structure of Terminologia Anatomica leads to some suggestions for enhancing the term list in ways that would facilitate its adoption as the standard for anatomical knowledge representation in biomedical informatics