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David A Morton

Publications and source records attributed to David A Morton.

2 recordsLinked to original sources

Design and utility of a web-based computer-assisted instructional tool for neuroanatomy self-study and review for physical and occupational therapy graduate students.

The cadaver continues to be the primary tool to teach human gross anatomy. However, cadavers are not available to students outside of the teaching laboratory. A solution is to make course content available through computer-assisted instruction (CAI). While CAI is commonly used as an ancillary teaching tool for anatomy, use of screen space, annotations that obscure the image, and restricted interactivity have limited the utility of such teaching tools. To address these limitations, we designed a Web-based CAI tool that optimizes use of screen space, uses annotations that do not decrease the clarity of the images, and incorporates interactivity across different operating systems and browsers. To assess the design and utility of our CAI tool, we conducted a prospective evaluation of 43 graduate students enrolled in neuroanatomy taught by the Divisions of Physical and Occupational Therapy at the University of Utah, College of Health. A questionnaire addressed navigation, clarity of the images, benefit of the CAI tool, and rating of the CAI tool compared to traditional learning tools. Results showed that 88% of the respondents strongly agreed that the CAI tool was easy to navigate and overall beneficial. Eighty-four percent strongly agreed that the CAI tool was educational in structure identification and had clear images. Furthermore, 95% of the respondents thought that the CAI tool was much to somewhat better than traditional learning tools. We conclude that the design of a CAI tool, with minimal limitations, provides a useful ancillary tool for human neuroanatomy instruction.

Computer-Assisted Instruction↗

A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment.

Use of digital medical images has become common over the last several years, coincident with the release of inexpensive, mega-pixel quality digital cameras and the transition to digital radiology operation by hospitals. One problem that clinicians, medical educators, and basic scientists encounter when handling images is the difficulty of using business and graphic arts commercial-off-the-shelf (COTS) software in multicontext authoring and interactive teaching environments. The authors investigated and developed software-supported methodologies to help clinicians, medical educators, and basic scientists become more efficient and effective in their digital imaging environments. The software that the authors developed provides the ability to annotate images based on a multispecialty methodology for annotation and visual knowledge representation. This annotation methodology is designed by consensus, with contributions from the authors and physicians, medical educators, and basic scientists in the Departments of Radiology, Neurobiology and Anatomy, Dermatology, and Ophthalmology at the University of Utah. The annotation methodology functions as a foundation for creating, using, reusing, and extending dynamic annotations in a context-appropriate, interactive digital environment. The annotation methodology supports the authoring process as well as output and presentation mechanisms. The annotation methodology is the foundation for a Windows implementation that allows annotated elements to be represented as structured eXtensible Markup Language and stored separate from the image(s).

Academic Medical Centers↗