Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Delfino, Jana"'
Autor:
Kim, Andrea, Saharkhiz, Niloufar, Sizikova, Elena, Lago, Miguel, Sahiner, Berkman, Delfino, Jana, Badano, Aldo
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in pa
Externí odkaz:
http://arxiv.org/abs/2408.00191
Autor:
Zamzmi, Ghada, Subbaswamy, Adarsh, Sizikova, Elena, Margerrison, Edward, Delfino, Jana, Badano, Aldo
The growing utilization of synthetic medical data (SMD) in training and testing AI-driven tools in healthcare necessitates a systematic framework for assessing SMD quality. The current lack of a standardized methodology to evaluate SMD, particularly
Externí odkaz:
http://arxiv.org/abs/2406.11143
Autor:
Sizikova, Elena, Badal, Andreu, Delfino, Jana G., Lago, Miguel, Nelson, Brandon, Saharkhiz, Niloufar, Sahiner, Berkman, Zamzmi, Ghada, Badano, Aldo
A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdens
Externí odkaz:
http://arxiv.org/abs/2407.01561
Autor:
Zamzmi, Ghada, Venkatesh, Kesavan, Nelson, Brandon, Prathapan, Smriti, Yi, Paul H., Sahiner, Berkman, Delfino, Jana G.
Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical settings, where data drift may cause unexpected performance that jeopardiz
Externí odkaz:
http://arxiv.org/abs/2402.08088
Autor:
Sizikova, Elena, Saharkhiz, Niloufar, Sharma, Diksha, Lago, Miguel, Sahiner, Berkman, Delfino, Jana G., Badano, Aldo
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available. We propose an evaluat
Externí odkaz:
http://arxiv.org/abs/2310.18494
Autor:
Thompson, Yee Lam Elim, Levine, Gary M, Chen, Weijie, Sahiner, Berkman, Li, Qin, Petrick, Nicholas, Delfino, Jana G, Lago, Miguel A, Cao, Qian, Samuelson, Frank W
In the past decade, Artificial Intelligence (AI) algorithms have made promising impacts to transform healthcare in all aspects. One application is to triage patients' radiological medical images based on the algorithm's binary outputs. Such AI-based
Externí odkaz:
http://arxiv.org/abs/2303.07050
Autor:
Tan, Fei1 (AUTHOR) fei.tan@fda.hhs.gov, Delfino, Jana G.1 (AUTHOR), Zeng, Rongping1 (AUTHOR) rongping.zeng@fda.hhs.gov
Publikováno v:
Bioengineering (Basel). Jun2024, Vol. 11 Issue 6, p614. 18p.
Autor:
Delfino, Jana G.
Cardiac resynchronization therapy (CRT) has recently emerged as an effective treatment option for heart failure patients with dyssynchrony. Patients have traditionally been chosen for CRT based on a prolonged QRS interval. However, this selection met
Externí odkaz:
http://hdl.handle.net/1853/16260
Autor:
Kunst, Mara, Gupta, Rajiv, Coombs, Laura P., Delfino, Jana G., Khan, Amir, Berglar, Inka, Kozak, Benjamin, Small, Juan E., Gillis, Laura, Noonan, Patrick, Fang, Junyong, Pai, Vinay, Tilkin, Mike, Allen, Bibb, Dreyer, Keith, Wald, Christoph
Publikováno v:
In Journal of the American College of Radiology February 2024 21(2):329-340
Autor:
Zhang, Qiang, Fotaki, Anastasia, Ghadimi, Sona, Wang, Yu, Doneva, Mariya, Wetzl, Jens, Delfino, Jana G., O’Regan, Declan P., Prieto, Claudia, Epstein, Frederick H.
Publikováno v:
In Journal of Cardiovascular Magnetic Resonance Winter 2024 26(2)