Promoting breast cancer screening among undeserved women: An on-site dual approach involving breast health education and AI-assisted 3D mobile mammography

Nwaiwu, Victor Chigbundu, Das, Sreemoy Kanti and Spencer, S (2025) Promoting breast cancer screening among undeserved women: An on-site dual approach involving breast health education and AI-assisted 3D mobile mammography. Trends in Telemedicine & E-health, 5. pp. 1-10. ISSN 2689-2707

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Abstract

Breast cancer is increasing globally; a major worry is the yearly continuous rise in the number of cases and deaths especially among underserved women aged 40 years and above in low-and middle-income countries. Statistical records on the mortality rate reveals that Melanesia and Africa rank highest, with Nigeria recording one of the highest age-standardized mortality rates globally and highest in Africa. Lack of early detection is a primary reason for this abysmal trend, which has been linked to several factors such as knowledge gaps, lack of infrastructure, inadequate personnel, low socioeconomic status and resource constricts. A close look at the pathophysiology of breast cancer clearly shows that early treatment prevents spread to other parts of the body, which is crucial for survival. To tackle such precarious situation in Nigeria, this educative and interesting piece, deeply rooted on up-to-date existing evidence, proposes an onsite approach involving breast health education and AI-assisted 3D mobile mammography that will definitely not miss out on breast cancer detection, even in its earliest stages. It is envisaged that more public awareness on breast cancer will be created, as well as knowledge gained on risk factors and breast self-examination following interactive teaching sessions; all interventions to follow established Health Belief Model (HBM). A mobile van is utilized to bring mammography screening services closer to many, utilizing Digital Breast Tomosynthesis (DBT) and Synthetic Mammography (SM) to obtain 3D slices, thus enhancing details and reducing the number of recalls for further imaging and biopsy. A deep learning-based Convolutional Neural Network (CNN) is applied to reduce the screening time, further improve detection rates, predict malignancy and reduce the number of requests for biopsies. Despite the extremely promising nature of this novel approach, cost appears to be a stumbling block. There is therefore need for increased funding from government, philanthropic donors, external bodies and NGOs, including multidisciplinary and international collaborations on breast cancer research to strengthen networks and promote evidence-based practice.

Item Type:	Article
Schools:	School of Health and Rehabilitation Sciences
Depositing User:	Bridget Roberts
Date Deposited:	05 Aug 2025 13:54
Last Modified:	08 Aug 2025 08:13
URI:	https://hsu.repository.guildhe.ac.uk/id/eprint/538

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