ISSN (Print): 3139-891X ISSN (Online): 3048-5568
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Innovative Journal of Medical Imaging

Official Journal of AARAIS

Open Access Journal

Short Communication

Comparative Performance of AI-Based CAD vs Traditional CAD in Mammography

Innovative Journal of Medical Imaging | Volume 1, Issue 3 | December 2024
Authors: Kallal Das
Received: Oct 18, 2024 Accepted: Nov 22, 2024 Published: Dec 28, 2024
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Abstract

Traditional computer-aided detection (CAD) systems have supported radiologists in mammographic interpretation for over two decades, yet their contribution to diagnostic improvement has been limited by high false-positive rates and inconsistent performance across breast densities. Recent advances in artificial intelligence (AI), particularly deep learning–based CAD systems, have redefined image interpretation by enabling automated feature learning from large datasets. This manuscript provides a technical comparison of traditional CAD and AI-based CAD in mammography, focusing on diagnostic accuracy, workflow efficiency, and robustness across diverse imaging environments. Evidence shows that AI-based CAD demonstrates superior sensitivity, specificity, false-positive reduction, and radiologist support, reflecting substantial progress toward reliable early breast cancer detection. Despite challenges involving transparency, dataset bias, and clinical implementation, AI-based CAD is positioned to become an essential component of modern screening programs.

Keywords: Artificial Intelligence; Computer-Aided Detection; Mammography

Article Information
DOI: 10.62502/ijmi/v1i3art2
Journal: Innovative Journal of Medical Imaging
Abbreviation: Innov. J. Med. Imaging
ISSN (Print): 3139-891X
ISSN (Online): 3048-5568
Volume/Issue: 1(3)
Pages: 9-11
References
  1. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35. doi:10.1186/s41747-018-0061-6.
  2. Rodríguez-Ruiz A, Lång K, Gubern-Mérida A, Broeders M, Gennaro G, Clauser P, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. 2019;111(9):916-22. doi:10.1093/jnci/djy222.
  3. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. doi:10.1038/s41586-019-1799-6.
  4. Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A deep learning model to triage screening mammograms: a simulation study. Radiology. 2019;293(1):38-46. doi:10.1148/radiol.2019182908.
  5. Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828-37. doi:10.1001/jamainternmed.2015.5231.
  6. Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, Lindholm P, et al. Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload. Lancet Digit Health. 2020;2(9):e468-e474. doi:10.1016/S2589-7500(20)30151-1.
  7. Freer PE. Artificial intelligence in screening mammography: current status and future directions. Radiol Clin North Am. 2021;59(1):1–11. doi:10.1016/j.rcl.2020.08.001.
  8. Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020;2(3):e138–e148. doi:10.1016/S2589-7500(20)30003-7.
  9. Salim M, Wåhlin E, Dembrower K, Eklund M, Strand F, Smith K. External evaluation of an artificial intelligence system for breast cancer detection in screening mammography. Eur Radiol. 2020;30(11):6327–6335. doi:10.1007/s00330-020-06948-0.
  10. Aiello M, Cavaliere C, D'Albore A, et al. Artificial intelligence in breast imaging: current applications and future perspectives. Diagnostics (Basel). 2023;13(5):892. doi:10.3390/diagnostics13050892.
How to Cite
Vancouver Style:
Das K. Comparative Performance of AI-Based CAD vs Traditional CAD in Mammography. Innov. J. Med. Imaging 2024;1(3):9-11. doi: 10.62502/ijmi/v1i3art2
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Article Details

Article Type:
Short Communication

Volume/Issue:
1(3)

Year:
2024

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