Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence
Reading mammograms with AI significantly enhances the detection of early cancerous changes, particularly in dense breast tissues. The AI's detection rate does not correlate with specific pathological types of breast cancer, highlighting its broad utility. Subtle mammographic changes in postmenopausal women, not corroborated by ultrasound but marked by AI, warrant further evaluation by advanced applications of digital mammograms and close interval AI-reading mammogram follow up to minimize the potential for missed breast carcinoma.
Authors
- Samar Ahmed Hussein (ORCID: https://orcid.org/0000-0001-9730-944X)
- Sahar Mansour (ORCID: https://orcid.org/0000-0002-4581-9939)
- Rasha Mohamed Kamal (ORCID: https://orcid.org/0000-0002-9344-0165)
- Mohammed Mohammed Mohammed Gomaa (ORCID: https://orcid.org/0000-0003-1670-8532)
- Sherif Nasser Taha
- Mostafa Emara
- Yomna Kassab
Institutions
- Cairo University (EG)
- Breast Cancer Research Foundation (US)
- National Water Research Center (EG)
- Children Cancer Hospital (EG)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2024-12-25
- DOI
- https://doi.org/10.1016/j.ejro.2024.100629
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.35