Accession Number : ADA302474

Title :   A Digital Breast Imaging Teaching File.

Descriptive Note : Annual rept. 15 Sep 94-14 Sep 95,

Corporate Author : CALIFORNIA UNIV SAN FRANCISCO

Personal Author(s) : Sickles, Edward ; Arenson, Ronald

PDF Url : ADA302474

Report Date : OCT 1995

Pagination or Media Count : 11

Abstract : Because of inefficient utilization of current mammography teaching facilities and existing deficiencies in training resources for radiologists, there is a need to supplement or replace the traditional film-based method of training. The purpose of this research is to develop a digital breast imaging teaching file (DBITF) as an interactive training tool for learning and continuing education for radiologists. Two hypotheses are being investigated: (1) a comprehensive DBITF can be designed and implemented using current technology, and (2) this DBITF 5 interactive response-driven type of instruction is more effective than the traditional "show and tell" type of instruction used with film-based teaching files. To test these hypotheses, our current film-based breast imaging teaching file, which contains over 1,000 pathologically proved state-of-the-art imaging cases, is being converted to digital format. Current technologies in high resolution film digitization, high resolution soft-copy image display, relational database architecture, and computer-aided instruction models are being used to create a DBITF containing these cases. The effectiveness of using the DBITF to teach breast imaging interpretation will be compared with traditional passive film-based teaching. As a result of this research, we expect to develop a comprehensive DBITF as a national resource, which should be superior to other methods of teaching

Descriptors :   *TRAINING DEVICES, *COMPUTER AIDED INSTRUCTION, *COMPUTER AIDED DIAGNOSIS, *MAMMARY GLANDS, *BREAST CANCER, CONVERSION, DATA BASES, DIGITAL SYSTEMS, INTERACTIONS, FILMS, HIGH RESOLUTION, FORMATS, RESOURCES, HYPOTHESES, INSTRUCTIONS, ARCHITECTURE, ANALOG TO DIGITAL CONVERTERS, LEARNING.

Subject Categories : Medicine and Medical Research
      Government and Political Science
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE