Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Peter Brotchie"'
Autor:
Helen M. L. Frazer, Carlos A. Peña-Solorzano, Chun Fung Kwok, Michael S. Elliott, Yuanhong Chen, Chong Wang, The BRAIx Team, Jocelyn F. Lippey, John L. Hopper, Peter Brotchie, Gustavo Carneiro, Davis J. McCarthy
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such
Externí odkaz:
https://doaj.org/article/2c201c5f842d4cf6b4d53d6962a08a8c
Autor:
Steven Korevaar, Ruwan Tennakoon, Mark Page, Peter Brotchie, John Thangarajah, Cosmin Florescu, Tom Sutherland, Ning Mao Kam, Alireza Bab-Hadiashar
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques
Externí odkaz:
https://doaj.org/article/694c313d6c154e93b4d8962f798f5592
Autor:
Hassan K. Ahmad, Michael R. Milne, Quinlan D. Buchlak, Nalan Ektas, Georgina Sanderson, Hadi Chamtie, Sajith Karunasena, Jason Chiang, Xavier Holt, Cyril H. M. Tang, Jarrel C. Y. Seah, Georgina Bottrell, Nazanin Esmaili, Peter Brotchie, Catherine Jones
Publikováno v:
Diagnostics, Vol 13, Iss 4, p 743 (2023)
Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is nec
Externí odkaz:
https://doaj.org/article/571a11ab740e4109a505dc682f9f2a14
Autor:
Helen M. L. Frazer, Jennifer S. N. Tang, Michael S. Elliott, Katrina M. Kunicki, Brendan Hill, Ravishankar Karthik, Chun Fung Kwok, Carlos A. Peña-Solorzano, Yuanhong Chen, Chong Wang, Osamah Al-Qershi, Samantha K. Fox, Shuai Li, Enes Makalic, Tuong L. Nguyen, Daniel F. Schmidt, Prabhathi Basnayake Ralalage, Jocelyn F. Lippey, Peter Brotchie, John L. Hopper, Gustavo Carneiro, Davis J. McCarthy
Publikováno v:
Radiology: Artificial Intelligence. 5
Publikováno v:
Stroke. 52:3308-3317
Background and Purpose: Distal medium vessel occlusions (DMVOs) are increasingly considered for endovascular thrombectomy but are difficult to detect on computed tomography angiography (CTA). We aimed to determine whether time-to-maximum of tissue re
Autor:
Catherine M Jones, Quinlan D. Buchlak, Nazanin Esmaili, Ben Hachey, John F Lambert, Luke Oakden-Rayner, Hassan Ahmad, Anuar Aimoldin, Stephen J F Hogg, Cyril H M Tang, Xavier G Holt, Peter Brotchie, Hung N. Pham, Christine Bennett, Jarrel Seah, Jeffrey B Wardman, Benjamin P Johnston
Publikováno v:
The Lancet Digital Health. 3:e496-e506
Summary Background Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray inter
Publikováno v:
Journal of Medical Imaging and Radiation Oncology
Introduction This study aims to evaluate deep learning (DL)‐based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image. Methods We evaluated several DL‐based AI techniques that employ di
Autor:
Quinlan Buchlak, Cyril Tang, Jarrel Seah, Andrew Johnson, Xavier Holt, Georgina Bottrell, Jeffrey Wardman, Gihan Samarasinghe, Leonardo Pinheiro, Hongze Xia, Hassan Ahmad, Hung Pham, Jason Chiang, Nalan Ektas, Michael Milne, Christopher Chiu, Ben Hachey, Melissa Ryan, Benjamin Johnston, Nazanin Esmaili, Christine Bennett, Tony Goldschlager, Jonathan Hall, Duc Tan Vo, Lauren Oakden-Rayner, Jean-Christophe Leveque, Farrokh Farrokhi, Catherine Jones, Simon Edelstein, Peter Brotchie
Background: Non-contrast computed tomography of the brain (NCCTB) is commonly used in clinical practice to detect intracranial pathology but is subject to interpretation errors. Machine learning is capable of augmenting clinical decision making and t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19d69a3bd62b6144fc946936edd40ba5
https://doi.org/10.21203/rs.3.rs-1588540/v1
https://doi.org/10.21203/rs.3.rs-1588540/v1
Autor:
Quinlan D. Buchlak, Michael R. Milne, Jarrel Seah, Andrew Johnson, Gihan Samarasinghe, Ben Hachey, Nazanin Esmaili, Aengus Tran, Jean-Christophe Leveque, Farrokh Farrokhi, Tony Goldschlager, Simon Edelstein, Peter Brotchie
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ee7c5918b2446819ecf352f97e6d64b
https://hdl.handle.net/10453/170982
https://hdl.handle.net/10453/170982
Autor:
Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Michael Robert Milne, Xavier Holt, Hassan Ahmad, John Lambert, Nazanin Esmaili, Luke Oakden-Rayner, Peter Brotchie, Catherine M Jones
Publikováno v:
BMJ Open
ObjectivesTo evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal