Comparisons with other Anatomy Sources

In the domain of anatomy, the Foundational Model of Anatomy ontology is a new kind of knowledge source, that cannot be represented in hard copy; it is inherently machine-based. The FMA cannot be equated with traditional sources of anatomical information, such as atlases, textbooks, dictionaries, thesauri or term lists.

Both atlases and textbooks target a particular user group. Different anatomy atlases and textbooks are published, for example, for nursing and medical students, or surgeons and radiologist, even though the same information is presented in each publication, albeit at different levels of granularity and from different viewpoints. The FMA, by contrast, is designed to provide anatomical information needed by any user group and is intended to accommodate any viewpoint.

Atlases represent anatomical information primarily through annotated images. Although images can be linked to the FMA, the essence of the FMA is that it uses non-graphical symbols (terms and numerical identifiers) for representing anatomical realities that are graphically represented in volumetric data sets, such as the Visible Human, or as images, such as those in atlases of macroscopic and microscopic anatomy.

Anatomy textbooks include non-anatomical information (e.g., physiologic function, pathological lesions, clinical cases) in order to illustrate the relevance of anatomical knowledge to clinical practice. The FMA, by contrast, is strictly constrained to “pure” anatomy, i.e., the structural organization of the body. This means that the modeling is in a deliberately structural context. Non-anatomical information may be associated with the anatomical content of the FMA in applications developed for specific user groups (e.g., medical students).

Dictionaries are term oriented and compile their content in alphabetical order, regardless of the meaning of their terms. Moreover, in a dictionary, different terms are defined in different contexts, i.e., from different viewpoints. The FMA, by contrast, is class or type rather than term oriented (for explanation see Foundational Model Explorer/Conducted Tour/Terms and Concepts) and arranges its classes in an inheritance hierarchy or taxonomy in a strictly structural context. The classes or types of this taxonomy are established on the basis of the structural properties shared by members of a class.

Thesauri organize their content according to the meaning of their terms. However, since these terms are not explicitly defined, the meanings have to be implied by each user on the basis of perceived similarities and differences between terms. The FMA, by contrast explicitly defines the classes of its taxonomy, and links all these classes through an inheritance hierarchy to a single root: Anatomical Entity.

The time-honored term list for anatomy is Terminologia Anatomica (a successor of Nomina Anatomica published more than a hundred years ago). Its purpose is to standardize anatomical terminology, rather than represent knowledge. Its potential and short comings with regard to serving as a basis for a computer-based knowledge source are discussed in Publications/Terminologia. Unlike Terminologia Anatomica, the intent of the FMA is to accomodate all current naming conventions, rather than attempting to standardize terminology. Therefore, the FMA incorporates all of, but is not limited to, the terms of Terminologia Anatomica.

Summary: Textbooks and atlases are similar to computer applications in that they target specific user groups. Dictionaries, thesauri and term lists are intended for general audiences. Of the latter resources only dictionaries represent knowledge explicitly through natural language definitions; any knowledge in thesauri and term lists is implied by the grouping of the terms. All these traditional sources have been conceived of with the printed page in mind as a medium for their dissemination. Although their content may be transferred to electronic media, these traditional sources are unsuitable for supporting machine-based intelligence (i.e., inference). The FMA, by contrast, is a hybrid between these traditional sources of anatomical information: its intent is to encode anatomical knowledge that can be reused for any application to serve the needs of any user group. Moreover, it is qualitatively distinct from traditional sources in that it encodes anatomical knowledge in a way that can support machine-based inference, a requirement for the development of next-generation, “smart” applications in education, clinical practice and research.