EXPERT LEARNING-SYSTEM NETWORK FOR DIAGNOSIS OF BREAST CALCIFICATIONS

被引:23
作者
PATRICK, EA
MOSKOWITZ, M
MANSUKHANI, VT
GRUENSTEIN, EI
机构
[1] UNIV CINCINNATI,CTR BREAST CANC DETECT,CINCINNATI,OH 45204
[2] UNIV CINCINNATI,DEPT MOLEC GENET,CINCINNATI,OH 45204
[3] UNIV CINCINNATI,DEPT RADIOL,CINCINNATI,OH 45204
关键词
ARTIFICIAL INTELLIGENCE; BREAST CALCIFICATIONS; BREAST CANCER DIAGNOSIS; IMAGE PROCESSING; NEURAL NETS; STATISTICAL PATTERN RECOGNITION; THE OUTCOME ADVISER;
D O I
10.1097/00004424-199106000-00004
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Breast calcification diagnosis was studied by using clinical findings and computerized image processing of a mammogram in a network of trained expert learning systems (Outcome Advisor(R) [OA]). The system was tested with records not used for training and performance was compared with radiologists. The network was 72% accurate in classifying clusters of calcifications as malignant or benign over a set of test cases radiologists had considered "hard-to-diagnose calcifications," and referred for biopsy. The radiologists had decided to conduct biopsy by selecting an equal number of positive and negative cases for the test group; thus the radiologists' performance with respect to categories of benign versus malignant was constrained to be 50/50. Statistical analysis shows only a 2% probability that the observed accuracy of 72% was a chance performance in recognizing whether a cluster is benign or malignant. The feasibility of developing a network of OAs for diagnosing breast cancer integrating digital image processing of mammograms is promising.
引用
收藏
页码:534 / 539
页数:6
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