Deep Learning-Based Artificial Intelligence for Mammography

Abstract

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.

Keywords: Artificial intelligence; Breast cancer; Computer-aided diagnosis; Deep learning; Mammography.

Fig. 1. Workflow of mammography interpretation with AI-CAD.

Mammography interpretation relies on visual analysis by radiologists, commonly using the American College of Radiology Breast Imaging Reporting and Data System categories. Computer assistance may be beneficial for mammography interpretation in parenchymal density assessment, lesion detection and characterization, and risk prediction. AI = artificial intelligence, CAD = computer-aided detection/diagnosis

Fig. 2. Representative case of a 46-year-old female who underwent mammography screening.

A, B. Conventional CAD (A, SecondLook v7.2, iCAD) shows multiple areas marked in both breasts (arrows at CAD marks) that warrant the attention of the radiologist. In comparison, AI-CAD (B, Lunit INSIGHT for Mammography, Lunit Inc.) did not mark any areas on the same image, with low abnormality scores for both breasts. The mammography of this female was interpreted as negative, and this result was confirmed during a follow-up of approximately 46 months during which no suspicious features were detected. AI = artificial intelligence, CAD = computer-aided detection/diagnosis

Fig. 3. Diagram of various workflows using AI-CAD for mammography interpretation.

Before applying AI-CAD to mammography interpretation, the following aspects should be considered: A. The impact of AI-CAD differs depending on whether the radiologists are exposed to the analytic results, i.e., sequential reading, wherein the AI-CAD result is provided after the radiologist has reached a conclusion vs. independent reading, wherein the AI-CAD result is provided with the mammography images before the radiologist has reached a conclusive interpretation. B. The impact of AI-CAD also differs according to the reading environment, i.e., single reading vs. double reading, wherein AI-CAD may not only assist radiologists, but even has the potential to replace a second reader or serve as a final consultant in cases where a consensus among multiple readers is required. C. If AI-CAD can be considered for stand-alone reading, its analytic results can be used for workload triage such that examinations with ‘negative’ results on AI-CAD will not be interpreted by radiologists. AI = artificial intelligence, CAD = computer-aided detection/diagnosis