no code implementations • 27 Sep 2022 • Andoni Elola, Elisabete Aramendi, Jorge Oliveira, Francesco Renna, Miguel T. Coimbra, Matthew A. Reyna, Reza Sameni, Gari D. Clifford, Ali Bahrami Rad
On the test set, the algorithm achieves an unweighted average of sensitivities of 80. 4% and an F1-score of 75. 8%.
1 code implementation • Computing in Cardiology 2022 • Matthew A. Reyna, Yashar Kiarashi, Andoni Elola, Jorge Oliveira, Francesco Renna, Annie Gu, Erick A. Perez Alday, Nadi Sadr, ASHISH SHARMA, Sandra Mattos, Miguel T. Coimbra, Reza Sameni, Ali Bahrami Rad, Gari D. Clifford
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
no code implementations • 2 Aug 2021 • Jorge Oliveira, Francesco Renna, Paulo Dias Costa, Marcelo Nogueira, Cristina Oliveira, Carlos Ferreira, Alipio Jorge, Sandra Mattos, Thamine Hatem, Thiago Tavares, Andoni Elola, Ali Bahrami Rad, Reza Sameni, Gari D Clifford, Miguel T. Coimbra
This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e. g., cardiac murmurs) exists.
1 code implementation • Computing in Cardiology 2020 • Erick A. Perez Alday, Annie Gu, Amit Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Qiao Li, ASHISH SHARMA, Gari D. Clifford, Matthew A. Reyna
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.