Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound
Journal
Journal of Medical Ultrasonics
Date Issued
2023-01-01
Author(s)
Fujioka, Tomoyuki
Kubota, Kazunori
Hsu, Jen Feng
Sawada, Terumasa
Ide, Yoshimi
Taruno, Kanae
Hankyo, Meishi
Kurita, Tomoko
Nakamura, Seigo
Tateishi, Ukihide
Takei, Hiroyuki
Abstract
Purpose: This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. Methods: The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. Result: The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). Conclusion: The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.
Subjects
Artificial intelligence | Breast cancer | Computer-aided detection | Deep learning | Ultrasound
Type
journal article