Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa
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ML and AI techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data.
“These techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data [8–14].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [9]
At the population level, ML and AI can identify high-risk subgroups and distinct disease trajectories associated with specific combinations of early life exposures, socioeconomic factors, and health behaviors.
“At the population level, ML and AI can identify high-risk subgroups and distinct disease trajectories associated with specific combinations of early life exposures, socioeconomic factors, and health behaviors [4, 9, 16, 30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [9]
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- Authors
- Waljee, A. K., Weinheimer-Haus, E. M., Abubakar, A., Ngugi, A. K., Siwo, G. H., Kwakye, G., Singal, A. G., Rao, A., Saini, S. D., Read, A. J., Baker, J. A., Balis, U., Opio, C. K., Zhu, J., & Saleh, M. N.
- Affiliations
- University of Michigan (US)
- Aga Khan University Nairobi (KE)
- University of Notre Dame (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Michigan Medicine (US)
- Aga Khan University Hospital Nairobi (KE)
- Corresponding author
- Waljee, Akbar K
- ORCID
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2022-04-13
- Volume
- 71
- Issue
- 7
- Pages
- 1259-1265
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- PMC
- PMC9177787
- OpenAlex
- W4224006918
- Cited by
- 68
- References
- 62
- Citations by year
- 2026: 7 · 2025: 26 · 2024: 15 · 2023: 17 · 2022: 3
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Gut (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- eCommons - AKU (Aga Khan University) (submittedVersion, cc-by)
- Funders
- Fogarty International Center (U54TW012089)
- National Institutes of Health (R37CA214955)
- American Cancer Society (RSG-16-005-01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, machine learning, colorectal cancer, early detection, cancer diagnosis, sub-Saharan Africa
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Colorectal Neoplasms, Africa South of the Sahara, Early Detection of Cancer
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