Improving Breast Cancer Detection Accuracy of Mammography with the                     Concurrent Use of an Artificial Intelligence Tool

Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

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Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art

Seminars in Cancer Biology ◽

10.1016/j.semcancer.2020.06.002 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ioannis Sechopoulos ◽

Jonas Teuwen ◽

Ritse Mann

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Cancer Detection ◽

State Of The Art ◽

Digital Breast Tomosynthesis ◽

Breast Cancer Detection ◽

Breast Tomosynthesis

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Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence

Lecture Notes in Electrical Engineering - InECCE2019 ◽

10.1007/978-981-15-2317-5_43 ◽

2020 ◽

pp. 505-515

Author(s):

Bifta Sama Bari ◽

Sabira Khatun ◽

Kamarul Hawari Ghazali ◽

Md. Moslemuddin Fakir ◽

Wan Nur Azhani W. Samsudin ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Early Breast Cancer ◽

Cancer Detection ◽

Wide Band ◽

Breast Cancer Detection ◽

Ultra Wide Band

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Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djy222 ◽

2019 ◽

Vol 111 (9) ◽

pp. 916-922 ◽

Cited By ~ 68

Author(s):

Alejandro Rodriguez-Ruiz ◽

Kristina Lång ◽

Albert Gubern-Merida ◽

Mireille Broeders ◽

Gisella Gennaro ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Cancer Detection ◽

Ground Truth ◽

Histopathological Analysis ◽

Detection Accuracy ◽

Breast Radiologist ◽

Screening Accuracy

Abstract Background Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. Methods Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists’ assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. Results The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = −0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. Conclusions The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

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