662-03-011 ISMRM Abstract

Optimizing Early Breast Cancer Diagnosis: Radiographers’ Readiness for AI-Enhanced Breast MRI in Kenya

Primary: Body - Breast

Secondary: Acquisition & Reconstruction - AI methods

662-03-011 · Classification: Other Cancer · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row C

Keywords: Breast MRI Low-Resource Imaging Settings Artificial Intelligence (AI) Early Cancer Detection Readiness and Awareness

Accepted

Lydia B Mong'are ¹

¹RADIOLOGY, JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY, NAIROBI, Kenya

Presenting Author: Lydia B Mong'are

Synopsis

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References

1. Botwe, B.O., Mustapha, S.K., Anim-Sampong, S. & Ofori, E. (2023) ‘Radiographers’ knowledge and perceptions of artificial intelligence in medical imaging: A cross-sectional survey’, BJR Open, 5(1), p. 20220058. DOI: 10.1259/bjro.20220058 [doi]

2. Nyagah, L. et al. (2022) ‘Breast cancer in sub-Saharan Africa: Rising burden, limited resources’, The Lancet Oncology, 23(2), pp. e60–e70. PMID: 34986157 [pmid]

3. Snaith, B. et al. (2020) ‘Radiographer reporting and advanced practice: A systematic review’, Radiography, 26(2), pp. 98–107.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/662-03-011