Bibliographic Details
| Title: |
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 |
| Authors: |
Sahar Mansour, Enas Sweed, Mohammed Mohammed Mohammed Gomaa, Samar Ahmed Hussein, Engy Abdallah, Yassmin Mohamed Nada, Rasha Kamal, Ghada Mohamed, Sherif Nasser Taha, Amr Farouk Ibrahim Moustafa |
| Source: |
The Egyptian Journal of Radiology and Nuclear Medicine, Vol 55, Iss 1, Pp 1-14 (2024) |
| Publisher Information: |
SpringerOpen, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Medical physics. Medical radiology. Nuclear medicine |
| Subject Terms: |
Artificial intelligence, Screening mammogram, Breast cancer screening, Workflow, Double reading, Recall rate, Medical physics. Medical radiology. Nuclear medicine, R895-920 |
| More Details: |
Abstract Background Although artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up. Results Double reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1ยท02 (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2090-4762 |
| Relation: |
https://doaj.org/toc/2090-4762 |
| DOI: |
10.1186/s43055-024-01353-5 |
| Access URL: |
https://doaj.org/article/e5669967238a4de992fee4358b7c765d |
| Accession Number: |
edsdoj.5669967238a4de992fee4358b7c765d |
| Database: |
Directory of Open Access Journals |
