Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Theodore W. Cary"'
Autor:
Laith R. Sultan, Allison Haertter, Maryam Al-Hasani, George Demiris, Theodore W. Cary, Yale Tung-Chen, Chandra M. Sehgal
Publikováno v:
AI, Vol 4, Iss 4, Pp 875-887 (2023)
With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine a
Externí odkaz:
https://doaj.org/article/bcdfa97e1b6f442895f90aba7e4bdef0
Autor:
Laith R. Sultan, Theodore W. Cary, Maryam Al-Hasani, Mrigendra B. Karmacharya, Santosh S. Venkatesh, Charles-Antoine Assenmacher, Enrico Radaelli, Chandra M. Sehgal
Publikováno v:
AI, Vol 3, Iss 3, Pp 739-750 (2022)
Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observat
Externí odkaz:
https://doaj.org/article/91af7bdbbbdc497586c2bb233122321d
Autor:
Maryam Al-Hasani, Laith R. Sultan, Hersh Sagreiya, Theodore W. Cary, Mrigendra B. Karmacharya, Chandra M. Sehgal
Publikováno v:
Diagnostics, Vol 12, Iss 11, p 2737 (2022)
Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. D
Externí odkaz:
https://doaj.org/article/821b3031111e4139af0b50e504595c2e
Publikováno v:
Journal of the American College of Emergency Physicians Open, Vol 2, Iss 2, Pp n/a-n/a (2021)
Abstract Background and objective Lung ultrasound is an inherently user‐dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer‐based pleural line (p‐line) ultrasound features
Externí odkaz:
https://doaj.org/article/16e2c3c10bb642c4a5fc324c3638768a
Autor:
Afaf F. Moustafa, Theodore W. Cary, Laith R. Sultan, Susan M. Schultz, Emily F. Conant, Santosh S. Venkatesh, Chandra M. Sehgal
Publikováno v:
Diagnostics, Vol 10, Iss 9, p 631 (2020)
Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted
Externí odkaz:
https://doaj.org/article/904482777c8f4d03a5c7d9ecd689c8a9
Publikováno v:
IEEE Int Ultrason Symp
Despite major advances in breast cancer imaging there is compelling need to reduce unnecessary biopsies by improving characterization of breast lesions. This study demonstrates the use of machine learning to enhance breast cancer diagnosis with multi
Publikováno v:
Journal of the American College of Emergency Physicians Open
Journal of the American College of Emergency Physicians Open, Vol 2, Iss 2, Pp n/a-n/a (2021)
Journal of the American College of Emergency Physicians Open, Vol 2, Iss 2, Pp n/a-n/a (2021)
Background and objective Lung ultrasound is an inherently user‐dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer‐based pleural line (p‐line) ultrasound features in compar
Publikováno v:
Breast Cancer Research and Treatment. 173:365-373
Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with
Publikováno v:
Ultrasound
Objective Impairment of flow-mediated dilation of the brachial artery is a marker of endothelial dysfunction and often predisposes atherosclerosis and cardiovascular events. In this study, we propose a user-guided automated approach for monitoring ar
Autor:
Hui Xiong, Laith R. Sultan, Chandra M. Sehgal, Ghizlane Bouzghar, Susan M. Schultz, Theodore W. Cary
Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31b947b9491cb176d1fe7c679ac7fe71
https://europepmc.org/articles/PMC5438055/
https://europepmc.org/articles/PMC5438055/