Presentation + Paper
15 February 2021 Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
Ida Arvidsson, Niels Christian Overgaard, Anette Davidsson, Jeronimo Frias Rose, Kalle Aström, Miguel Ochoa Figueroa, Anders Heyden
Author Affiliations +
Abstract
Myocardial perfusion scintigraphy, which is a non-invasive imaging technique, is one of the most common cardiological examinations performed today, and is used for diagnosis of coronary artery disease. Currently the analysis is performed visually by physicians, but this is both a very time consuming and a subjective approach. These are two of the motivations for why an automatic tool to support the decisions would be useful. We have developed a deep neural network which predicts the occurrence of obstructive coronary artery disease in each of the three major arteries as well as left bundle branch block. Since multiple, or none, of these could have a defect, this is treated as a multi-label classification problem. Due to the highly imbalanced labels, the training loss is weighted accordingly. The prediction is based on two polar maps, captured during stress in upright and supine position, together with additional information such as BMI and angina symptoms. The polar maps are constructed from myocardial perfusion scintigraphy examinations conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). The study includes data from 759 patients. Using 5-fold cross-validation we achieve an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level for the three major arteries, 0.94 on per-patient level and 0.82 for left bundle branch block.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ida Arvidsson, Niels Christian Overgaard, Anette Davidsson, Jeronimo Frias Rose, Kalle Aström, Miguel Ochoa Figueroa, and Anders Heyden "Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970N (15 February 2021); https://doi.org/10.1117/12.2580890
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KEYWORDS
Arteries

Scintigraphy

Neural networks

Brain-machine interfaces

Cameras

Receivers

Visual analytics

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