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Advancing Digital Health using AI and Machine Learning Solutions for Early Ultrasonic Detection of Breast Disorders in Women

Author

Listed:
  • Majd Oteibi

    (Validus Institute Inc)

  • Adam Tamimi

    (Validus Institute Inc)

  • Kaneez Abbas

    (Athreya Med Tech)

  • Gabriel Tamimi

    (Validus Institute Inc)

  • Danesh Khazaei

    (Portland State University)

  • Hadi Khazaei

    (Portland State University/ Athreya Med Tech)

Abstract

Background: Breast cancer is a significant global health concern accounting for 685,000 deaths in 2020 and 2.3 million cases worldwide. By 2070, the cases are expected to rise to 4.4 million, because it is usually discovered at a later stage when it is too late to help the patients. For the past two decades, innovations made in mobile health have improved the lives of people and accessibility in multiple disciplines. This abstract explores the feasibility of using a portable ultrasound device integrated with artificial intelligence (AI) technology for the purpose of early screening and detection of breast cancer in women living in remote and rural areas, between the ages of 18 years to 75 years. Intervention: Training healthcare professionals to use this portable ultrasound with the integration of AI technology will provide convenience and accuracy. This technology can provide high-resolution information regarding anatomic and tissue changes and holds promise for early detection of lumps in the breast. This is a critical screening and diagnostic tool for females living in rural and remote areas. The results will be compared with images of mammography testing for accuracy and patients will then be referred for further evaluation and biopsy of their lesions.

Suggested Citation

  • Majd Oteibi & Adam Tamimi & Kaneez Abbas & Gabriel Tamimi & Danesh Khazaei & Hadi Khazaei, 2024. "Advancing Digital Health using AI and Machine Learning Solutions for Early Ultrasonic Detection of Breast Disorders in Women," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(11), pages 518-527, November.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:11:p:518-527
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    References listed on IDEAS

    as
    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
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    3. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    4. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
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    Cited by:

    1. Majd Oteibi & Adam Tamimi & Kaneez Abbas & Gabriel Tamimi & Danesh Khazaei & Behrooz Khajehee & Hadi Khazaei, 2025. "AI-Assisted Point of Care Ultrasound (POCUS) Vs. Mammography for Early Breast Cancer Detection: A Comparative Review," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(10), pages 1409-1423, October.

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