Design of a Web Portal for Interdisciplinary Image Retrieval from Multiple Online Image Resources

Kammerer, Ferdinand J.; Frankewitsch, Thomas; Prokosch, H.-U.

doi:10.3414/me0581

Cited by 2 publications

(1 citation statement)

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“…Semantic indexing of MLOs is a prerequisite of useful repositories of MLOs, and semantic networks (SNs) provide a sound basis for consistent semantic indexing [ 4 ]. While SNs have worked their way into medical classification systems and e-learning, the usability of those networks has been sparsely investigated [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

Tix

Gießler²,

Ohnesorge-Radtke³

et al. 2015

JMIR Medical Education

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BackgroundThe Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms.ObjectiveThe purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM.MethodsIn this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged.ResultsThe study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users.ConclusionsIt is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively.

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Section: Introductionmentioning

confidence: 99%

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

Tix

Gießler²,

Ohnesorge-Radtke³

et al. 2015

JMIR Medical Education

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Role of domain knowledge in developing user‐centered medical‐image indexing

Wang

Erdelez

Allen

et al. 2011

J. Am. Soc. Inf. Sci.

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An efficient and robust medical-image indexing procedure should be user-oriented. It is essential to index the images at the right level of description and ensure that the indexed levels match the user's interest level. This study examines 240 medical-image descriptions produced by three different groups of medical-image users (novices, intermediates, and experts) in the area of radiography. This article reports several important findings: First, the effect of domain knowledge has a significant relationship with the use of semantic image attributes in image-users' descriptions. We found that experts employ more high-level image attributes which require highreasoning or diagnostic knowledge to search for a medical image (Abstract Objects and Scenes) than do novices; novices are more likely to describe some basic objects which do not require much radiological knowledge to search for an image they need (Generic Objects) than are experts. Second, all image users in this study prefer to use image attributes of the semantic levels to represent the image that they desired to find, especially using those Received July 3, 2011; revised September 16, 2011; accepted September 16, 2011 © 2011 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21686 specific-level and scene-related attributes. Third, image attributes generated by medical-image users can be mapped to all levels of the pyramid model that was developed to structure visual information. Therefore, the pyramid model could be considered a robust instrument for indexing medical imagery. IntroductionMedical images provide vital clinical data and are considered a powerful educational resource due to their immediate, informative, and illustrative nature. Medical images can be used by clinicians for their daily practice of medicine, such as making diagnoses, planning treatment, and monitoring responses to therapy as well as for medical education and research (Cleveland & Cleveland, 2009;Kalpathy-Cramer & Hersh, 2010;Müller, Michoux, Bandon, & Geissbuhler, 2004). Past studies have reported significant learning improvements when using medical images during classes and self-education for medical students and residents (Dawes, Vowler, Allen, & Dixon, 2004; KalpathyCramer & Hersh, 2010). A single hospital radiology department alone produced 50,000 images per day in 2006 (Müller , 2007). With the dramatic explosion of digital image collections in medicine, it is important to develop advanced techniques for effective and efficient management of this information, enabling users quick and easy access in a clinically meaningful way.Image information systems (e.g., picture archiving and communication systems) provide rapid access to digitalized film images and allow users to access medical-image databases based on combinations of a patient's identification, visit dates, and study characteristics (e.g., modality and study description) (Müller et al., 2004). However, to fulfill users' various requirements under different contexts of u...

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Design of a Web Portal for Interdisciplinary Image Retrieval from Multiple Online Image Resources

Abstract: The integration of the images from different sources into the UMLS semantic network offers a quick and an easy-to-use learning environment.

Cited by 2 publications

References 11 publications

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

Role of domain knowledge in developing user‐centered medical‐image indexing

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