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123 results on '"Guindy M"'

2. E-107 Multi-center study of a deep learning model for intracranial aneurysm detection in computed tomography angiography

Author

Wu, D, primary, Orru’, E, additional, Ferraciolli, S, additional, Ashok, A, additional, Sorby-Adams, A, additional, Guindy, M, additional, Montes, D, additional, Medina, A, additional, Bianco, G, additional, Kaggie, J, additional, Rabina, D, additional, Ashush, O, additional, Han, X, additional, Baror, Y, additional, Patel, M, additional, Dayan, I, additional, Romero, J, additional, González, R, additional, Wald, C, additional, Kitamura, F, additional, Kuriki, P, additional, Matys, T, additional, and Li, Q, additional

Published
2022
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4. 135P Real-time genotyping-based breast cancer risk assessment in MyPeBS, an international randomized trial in the general population comparing risk-stratified to standard breast cancer screening (BCS)

Author

Delaloge, S., primary, Giorgi Rossi, P., additional, Balleyguier, C., additional, Guindy, M., additional, Gilbert, F.J., additional, Burrion, J-B., additional, Roman, M., additional, de Montgolfier, S., additional, Giordano, L., additional, Drubay, D., additional, Evans, D.G., additional, Keatley, D., additional, Gauthier, E., additional, du Bois d'Aische, A., additional, Baron, C., additional, Boland, A., additional, Blanché, H., additional, Couch, D., additional, Deleuze, J-F., additional, and Michiels, S., additional

Published
2022
Full Text
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6. Erratum: On the role of artificial intelligence in medical imaging of COVID-19 (Patterns (2021) 2(6), (S2666389921000957), (10.1016/j.patter.2021.100269))

Author

Beymer, D, Rajan, D, Coy, A, Mukherjee, VV, Manica, M, Prasanna, P, Ballah, D, Guindy, M, Shaham, D, Shah, PL, Karteris, E, Robertus, JL, Gabrani, M, and Rosen-Zvi, M

Abstract

The corrected full text is available on Brunel University Reasearch Archive at: https://bura.brunel.ac.uk/handle/2438/26331. Copyright © 2021 The Author(s). (Patterns 2, 100269-1–100269-18; June 11, 2021). Due to an error in production, the references for this article were incorrectly numbered upon publication of the typeset paper, May 26, 2021. Since this is a meta-analysis, incorrect numbering of references may have caused misinterpretation of the conclusions. The corrected version of the article is available online as of June 5, 2021, and we sincerely apologize to our readers for any confusion that may have resulted from this error.

Published
2021

24. Muscular reactions to femoral angiography with low osmolality contrast media

Author

Alan J. Wilson, Evill Ca, Sage Mr, and Guindy M

Subjects
Male, medicine.medical_specialty, business.industry, Electromyography, media_common.quotation_subject, Iohexol, Muscles, Osmolar Concentration, Contrast Media, Iothalamate Meglumine, General Medicine, Femoral Artery, Radiography, medicine, Ioxaglic Acid, Contrast (vision), Animals, Radiology, Nuclear Medicine and imaging, Female, Radiology, Rabbits, business, Femoral angiography, media_common
Published
1991

31. Precious metals '89, proceedings of an international symposium sponsored by the Copper, Nickel, Cobalt and Precious Metals Committee of The Minerals, Metals & Materials Society and the International Precious Metals Institute held at the TMS Annual Meeting, Las Vegas, Nevada, 27 February-2 March 1989.

Author

Jha M.C., Minerals Metals and Materials Society; International Precious Metals Institute., eds., El Guindy M., Hill S.D., Jha M.C., Minerals Metals and Materials Society; International Precious Metals Institute., eds., El Guindy M., and Hill S.D.

Abstract

36 papers are presented: some papers present an update on commercial practice by such major companies as Homestake, Newmont, Mitsubishi, and AMAX, while others present a potential view of the future, particularly in the areas of alternate lixiviants and processing of refractory ores and secondary materials. The papers are presented in 6 sections: Primary gold operations (5 papers, 1 abstract); Heap leaching/CIP technology (6 papers); Processing refractory ores (5 papers, 2 abstracts); Alternate lixiviants (6 papers, 1 abstract); Silver (7 papers); and Platinum group metals (3 papers)., 36 papers are presented: some papers present an update on commercial practice by such major companies as Homestake, Newmont, Mitsubishi, and AMAX, while others present a potential view of the future, particularly in the areas of alternate lixiviants and processing of refractory ores and secondary materials. The papers are presented in 6 sections: Primary gold operations (5 papers, 1 abstract); Heap leaching/CIP technology (6 papers); Processing refractory ores (5 papers, 2 abstracts); Alternate lixiviants (6 papers, 1 abstract); Silver (7 papers); and Platinum group metals (3 papers).

Published
1988

32. The Case of Missed Cancers: Applying AI as a Radiologist's Safety Net

Author

Chorev M. , Shoshan Y., Spiro A., Naor S., Hazan A., Barros V., Weinstein I., Herzel E., Shalev V., Guindy M., and Rosen-Zvi, M.

Subjects
education, Deep learning, Computer-aided diagnosis, Breast imaging, 3. Good health
Abstract

We investigate the potential contribution of an AI system as a safety net application for radiologists in breast cancer screening. As a safety net, the AI alerts on cases suspected to be malignant which the radiologist did not recommend for a recall. We analyzed held-out data of 2,638 exams enriched with 90 missed cancers. In screening mammography settings, we show that a system alerting on 11 out of every 1,000 cases, could detect up to 10.7% of the radiologists’ missed cancers. Thus, significantly increasing radiologist’s sensitivity to 80.3%, while only slightly decreasing their specificity to 95.3%. Importantly, the safety net demonstrated a significant contribution to their performance even when radiologists utilized both mammography and ultrasound images. In those settings, it would have alerted 8.5 times per 1,000 cases, and detected 11.7% of the radiologists’ missed cancers. In an analysis of the missed cancers by an expert, we found that most of the cancers detected by the AI were visible post-hoc. Finally, we performed a reader study with five radiologists over 120 exams, 10 of which were originally missed cancers. The AI safety net was able to assist 3 out of the 5 radiologists in detecting missed cancers without raising any false alerts.

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