Artificial intelligence: competitor or best friend of breast cancer radiologists?
DOI:
https://doi.org/10.58445/rars.184Keywords:
artificial intelligence, breast cancer, cancer, radiologyAbstract
Around the world, over half a million women die of breast cancer every year. Screening mammograms were introduced to aid in early breast cancer diagnosis more than 30 years ago. Traditionally, disease determination from mammograms has been dependent upon radiologist interpretation. However, technologies have been developed to improve the sensitivity and specificity of mammograms beyond the capabilities of humans. Artificial intelligence (AI) using deep learning and convolutional neural networks has been employed in the past few years to improve early detection of breast cancer in digital mammography and breast tomosynthesis. Here, we aggregated data from past studies comparing the performance of experienced breast cancer radiologists, AI, and AI-informed radiologists. Our review showed that the performance of the AI system was greater than that of radiologists alone and that the combination of AI and radiologist input showed the greatest promise in detecting breast cancer. Our review demonstrates the potential of AI while also acknowledging potential pitfalls of this technology. Prospective studies in hospital settings are needed to fully establish the potential benefits of utilizing AI in breast cancer screening.
References
Bray, F et al. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.
NelsonHD, et al.Screening for breast cancer: an update for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151(10):727-737
Sadoughi, F., Kazemy, Z., Hamedan, F., Owji, L., Rahmanikatigari, M., & Azadboni, T. T. (2018). Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer: Targets and Therapy, 219-230.
Lehman,CD, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828-1837.
Rodriguez-Ruiz et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-31
Rodriguez-Ruiz et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-31
Conant E et al. Radiol Artif Intell. 2019 Jul; 1(4): e180096
Sechopoulos I et al. Stand-alone artificial intelligence - The future of breast cancer screening?
Breast 2020 Feb;49:254-260
Schaffter T et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to
Interpret Screening Mammograms. JAMA Network Open.
Wu N et al. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer
Screening. EE Trans Med Imaging. 2020 Apr;39(4):1184-1194.
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