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| 003 | EG-GiCUC | ||
| 005 | 20250223032847.0 | ||
| 008 | 211114s2020 ua dh f m 000 0 eng d | ||
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_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.11.31.M.Sc.2020.He.E | ||
| 100 | 0 | _aHedaya Wasfy Ali Mohamed | |
| 245 | 1 | 0 |
_aEfficacy of mammographic artificial intelligence in detecting different histopathological subtypes of breast cancer / _cHedaya Wasfy Ali Mohamed ; Supervised Mariam Raafat Louis Bouls , Basma Mohamed Alkalaawy , Passant Essam Eldin Ahmed Shibel |
| 246 | 1 | 5 | _aفاعلية الذكاء الاصطناعى بالتصوير الشعاعى للثدى فى الكشف عن الأنواع النسيجية المختلفة لسرطان الثدى |
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_aCairo : _bHedaya Wasfy Ali Mohamed , _c2020 |
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_a116 P. : _bcharts , facsimiles ; _c25cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Medicine - Department of Radio-Diagnosis | ||
| 520 | _aBackground:Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.AI-based algorithms can also increase the efficiency of interpretation workflows by reducing workload and interpretation time Methods: The study was prospectively carried on 123 female patients with 134 pathologically proven malignant breast lesion (between December 2020 to June 2021); the mean age was 53.6 ± SD 12.0 years old.Females coming to breast imaging unit either for screening or with breast complaint, basic sono-mammography was done. Artificial intelligence images were automatically generated by AI software (Lunit INSIGHT for mammography) from mammographic images. Biopsieswere done for suspicious breast lesions.Artificial intelligence results as well as mammography results were correlated to the pathology as the gold reference standard. Results: Artificial intelligence has higher sensitivity than mammography in detecting malignant breast lesions; sensitivity of the two methods (AI and mammography) was 96.6% vs 87.3% and false negative rate 3.4% vs 12.7% respectively. Also AI was more sensitive to detect cancers with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% as well as asymmetry and distortion 100% vs 84.6%.AI has better performance in detecting different histopathological subtype of breast malignancy as DCIS, IDC and ILC than mammography with sensitivity (100%, 96.7%, 96.6%) vs (88.9%, 89%, 82.2%) respectively.While in other rare types of breast malignancy both AI and mammography showed the same sensitivity 80% | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aArtificial intelligence | |
| 653 | 4 | _aBreast cancer | |
| 653 | 4 | _aMammography | |
| 700 | 0 |
_aBasma Mohamed Alkalaawy , _eSupervisor |
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| 700 | 0 |
_aMariam Raafat Louis Bouls , _eSupervisor |
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| 700 | 0 |
_aPassant Essam Eldin Ahmed Shibel , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
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_aNazla _eRevisor |
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_aShimaa _eCataloger |
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_2ddc _cTH |
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_c83126 _d83126 |
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