IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v11y2024i11p518-527.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-11-issue-11/518-527.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/manuscript-name-advancing-digital-health-using-ai-and-machine-learning-solutions-for-early-ultrasonic-detection-of-breast-disorders-in-women/
    Download Restriction: no
    ---><---

    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.
    2. 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. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuming Jiang & Zhicheng Zhang & Wei Wang & Weicai Huang & Chuanli Chen & Sujuan Xi & M. Usman Ahmad & Yulan Ren & Shengtian Sang & Jingjing Xie & Jen-Yeu Wang & Wenjun Xiong & Tuanjie Li & Zhen Han & , 2023. "Biology-guided deep learning predicts prognosis and cancer immunotherapy response," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Venkat Ram Reddy Ganuthula & Krishna Kumar Balaraman, 2025. "The Paradox of Professional Input: How Expert Collaboration with AI Systems Shapes Their Future Value," Papers 2504.12654, arXiv.org.
    3. Syed Ibrar Hussain & Elena Toscano, 2025. "Enhancing Recognition and Categorization of Skin Lesions with Tailored Deep Convolutional Networks and Robust Data Augmentation Techniques," Mathematics, MDPI, vol. 13(9), pages 1-36, April.
    4. von Walter, Benjamin & Wentzel, Daniel & Raff, Stefan, 2023. "Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships," Journal of Retailing, Elsevier, vol. 99(2), pages 280-296.
    5. Mendes, Carlos Frederico S. da F. & Krohling, Renato A., 2022. "Deep and handcrafted features from clinical images combined with patient information for skin cancer diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Alexander P. L. Martindale & Carrie D. Llewellyn & Richard O. Visser & Benjamin Ng & Victoria Ngai & Aditya U. Kale & Lavinia Ferrante Ruffano & Robert M. Golub & Gary S. Collins & David Moher & Melis, 2024. "Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Mehdi Mourali & Dallas Novakowski & Ruth Pogacar & Neil Brigden, 2025. "Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-25, March.
    8. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
    9. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    10. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    12. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    13. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.
    14. Juexiao Zhou & Haoyang Li & Xingyu Liao & Bin Zhang & Wenjia He & Zhongxiao Li & Longxi Zhou & Xin Gao, 2023. "A unified method to revoke the private data of patients in intelligent healthcare with audit to forget," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. Oded Rotem & Tamar Schwartz & Ron Maor & Yishay Tauber & Maya Tsarfati Shapiro & Marcos Meseguer & Daniella Gilboa & Daniel S. Seidman & Assaf Zaritsky, 2024. "Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    16. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    17. Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothea Kübler, 2025. "Behavioral Measures Improve AI Hiring: A Field Experiment," Rationality and Competition Discussion Paper Series 532, CRC TRR 190 Rationality and Competition.
    18. Ting Wang & Boyang Zang & Chui Kong & Yigang Li & Xiaomin Yang & Yi Yu, 2025. "Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-11, June.
    19. Dyke Ferber & Georg Wölflein & Isabella C. Wiest & Marta Ligero & Srividhya Sainath & Narmin Ghaffari Laleh & Omar S. M. El Nahhas & Gustav Müller-Franzes & Dirk Jäger & Daniel Truhn & Jakob Nikolas K, 2024. "In-context learning enables multimodal large language models to classify cancer pathology images," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    20. Sonika Darshan, 2024. "Data Mining for Disease Diagnosis: A Review of Machine Learning Approaches in Healthcare," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 716-726.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:11:y:2024:i:11:p:518-527. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.