liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
A computer-based training system for breast fine needle aspiration cytology
Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
Näslund, J., Department of Clinical Neuroscience, Occupational Therapy, and Elderly Care Research (NEUROTEC), Section for Geriatrics, Karolinska Institute, Huddinge, Sweden.
Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Molecular and Immunological Pathology .
Show others and affiliations
2002 (English)In: Journal of Pathology, ISSN 0022-3417, Vol. 196, no 1, 113-121 p.Article in journal (Refereed) Published
Abstract [en]

Fine-needle aspiration (FNA) cytology is a rapid and inexpensive technique used extensively in the diagnosis of breast disease. To remove diagnostic subjectivity, a diagnostic decision support system (DDSS) called CytoInform© has been developed, based on a Bayesian belief network (BBN) for the diagnosis of breast FNAs. In addition to acting as a DDSS, the system implements a computer-based training (CBT) system, providing a novel approach to breast cytology training. The system guides the trainee cytopathologist through the diagnostic process, allowing the user to grade each diagnostic feature using a set of on-screen reference images as visual clues. The trainee positions a slider on a spectrum relative to these images, reflecting the similarity between the reference image and the microscope image. From this, an evidence vector is generated, allowing the current diagnostic probability to be updated by the BBN. As the trainee assesses each clue, the evidence entered is compared with that of the expert through the use of a defined teaching file. This file records the relative severity of each clue and a tolerance band within which the trainee must position the slider. When all clues in the teaching case have been completed, the system informs the user of inaccuracies and offers the ability to reassess problematic features. In trials with two pathologists of different experience and a series of ten cases, the system provided an effective tool in conveying diagnostic evidence and protocols to trainees. This is evident from the fact that each pathologist only misinterpreted one case and a total of 86%/88% (experienced/inexperienced) of all clues assessed were interpreted correctly. Significantly, in all cases that produced the correct final diagnostic probability, the route taken to that solution was consistent with the expert's solution. Copyright © 2001 John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
2002. Vol. 196, no 1, 113-121 p.
Keyword [en]
Bayesian networks, Breast FNA, Computer-based training, Decision support, Knowledge management
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-47120DOI: 10.1002/path.1012OAI: diva2:268016
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-13

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Westermark, GunillaHäggqvist, Bo
By organisation
Faculty of Health SciencesCell BiologyMolecular and Immunological Pathology
In the same journal
Journal of Pathology
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 64 hits
ReferencesLink to record
Permanent link

Direct link