Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD
| Autor: | Alyssa T. Watanabe, Daniel Kent, Ray Cody Mayo, Megha Kapoor, Lauren Chang Sen, Jessica W. T. Leung |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial intelligence
Breast imaging CAD Breast Neoplasms Cancer detection False-positive exam Sensitivity and Specificity Article 030218 nuclear medicine & medical imaging Reduction (complexity) 03 medical and health sciences 0302 clinical medicine Medicine Humans Radiology Nuclear Medicine and imaging False Positive Reactions cardiovascular diseases Breast Retrospective Studies Computer-aided detection Radiological and Ultrasound Technology Screening mammography business.industry Mammogram Economic benefits Computer aided detection Computer Science Applications Comparison study Radiographic Image Interpretation Computer-Assisted Female business 030217 neurology & neurosurgery Mammography |
| Zdroj: | Journal of Digital Imaging |
| ISSN: | 1618-727X |
| Popis: | The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital mammograms between January 1, 2013, and March 31, 2013, and the number of marked regions of interest of two different systems was compared for sensitivity and specificity in cancer detection. The count of false-positive marks per image (FPPI) of the two systems was also evaluated as well as the number of cases that were completely mark-free. All results showed statistically significant reductions in false marks with the use of AI-CAD vs CAD (confidence interval = 95%) with no reduction in sensitivity. There is an overall 69% reduction in FPPI using the AI-based CAD as compared to CAD, consisting of 83% reduction in FPPI for calcifications and 56% reduction for masses. Almost half (48%) of cases showed no AI-CAD markings while only 17% show no conventional CAD marks. There was a significant reduction in FPPI with AI-CAD as compared to CAD for both masses and calcifications at all tissue densities. A 69% decrease in FPPI could result in a 17% decrease in radiologist reading time per case based on prior literature of CAD reading times. Additionally, decreasing false-positive recalls in screening mammography has many direct social and economic benefits. |
| Databáze: | OpenAIRE |
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