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Voice-activated retrieval of mammography reference images
Journal article   Open access   Peer reviewed

Voice-activated retrieval of mammography reference images

Henry A. Swett, Pradeep G. Mutalik, Vladimir P. Neklesa, Laura Horvath, Carol Lee, Joan Richter, Irena Tocino, Paul R. Fisher and Chih-jen Lee
Journal of digital imaging, v 11(2), pp 65-73
01 May 1998
DOI: https://doi.org/10.1007/BF03168728
PMID: 9608929
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1007/bf03168728View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open
url
https://doi.org/10.1007/BF03168728View
Published, Version of Record (VoR) Open

Abstract

computer aided diagnosis Computer assisted decision making decision support systems mammography radiology reporting systems
We undertook this project to integrate context sensitive computer-based educational and decision making aids into the film interpretation and reporting process, and to determine the clinical utility of this method as a guide for further system development. An image database of 347 digital mammography images was assembled and image features were coded. An interface was developed to a computerized speech recognition radiology reporting system which was modified to translate reported findings into database search terms. These observations were used to formulate database search strategies which not only retrieved similar cases from the image database, but also other cases that were related to the index case in different ways. The search results were organized into image sets intended to address common questions that arise during image interpretation. An evaluation of the clinical utility of this method was performed as a guide for further system development. We found that voice dictation of prototypical mammographic cases resulted in automatic retrieval of reference images. The retrieved images were organized into sets matching findings, diagnostic hypotheses, diagnosis, spectrum of findings or diagnoses, closest match to dictated case, or user specified parameters. Two mammographers graded the clinical utility of each form of system output. We concluded that case specific and problem specific image sets may be automatically generated from spoken case dictation. A potentially large number of retrieved images may be divided into subsets which anticipate common clinical problems. This automatic method of context sensitive image retrieval may provide a “continuous’; form of education integrated into routine case interpretation.

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15 citations in Web of Science
16 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#5 Gender Equality
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Collaboration types
Domestic collaboration
Web of Science research areas
Radiology, Nuclear Medicine & Medical Imaging
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