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Sahar Mansour: We learn from history that we do not learn from history

Sahar Mansour/LinkedIn

Dec 9, 2024, 18:40

Sahar Mansour: We learn from history that we do not learn from history

Sahar Mansour shared on LinkedIn:

“‘We learn from history that we do not learn from history.’

At Cairo University, our concept is ‘DIFFERENT’.
There is no hypothesis for showoffs or data sharing for propaganda.

We LEARN from history.
We document, analyze, and upgrade.

Understand the importance of research and prove the results.
Incorporate our data into our colleagues’ fund of knowledge.
Provide enough details so that others can judge the reliability of our study and reproduce their own research.

In 2021, that was the voice user MMG AI for Kasr AlAiny School of Medicine, Cairo University, Egypt.

In 3 years, there was a list of published research work and experiences of breast cancer AI practice:
–Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?
-Discrimination between phyllodes tumor and fibroadenoma: Does artificial intelligence-aided mammograms have an impact?
-Strengths and challenges of the artificial intelligence in the assessment of dense breasts.

-Does artificial intelligence aid in the detection of different types of breast cancer.
-Mammographically detected asymmetries in the era of artificial intelligence.
-Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter?
-The synergy between AI and radiologist in advancing digital mammography: comparative study between stand-alone radiologist and concurrent use of artificial intelligence in BIRADS 4 and 5 female patients.

-What’s beyond breast asymmetry? Comparative study between artificial intelligence and contrast-enhanced spectral mammography in the assessment of various types of breast asymmetries.
-The integration of artificial intelligence with contrast-enhanced mammogram in the work up of suspicious breast lesions: what do you expect?

-Artificial intelligence as a negative predictive tool for breast cancer postoperative recurrence.
-Artificial intelligence as an initial reader for double reading in breast cancer screening: a prospective initial study of 32,822 mammograms of the Egyptian population.
-Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications.”

Dr. Sahar Mansour is a Professor of Radiology in the Women’s Imaging Unit at Kasr El Ainy Hospital, Cairo University. She serves as Chief of the Scientific Committee for the Egyptian Society of Breast Imaging and is the Research Program Coordinator for the Radiology Department at Baheya Foundation. Dr. Mansour also consults in breast imaging and intervention at Baheya Charity Women’s Cancer Hospital and is the Cairo Regional Coordinator for Egypt’s National Breast Cancer Screening Program with the Ministry of Health and Population.