An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT.
J Nucl Cardiol
; 17(3): 405-13, 2010 Jun.
Article
en En
| MEDLINE
| ID: mdl-20204564
BACKGROUND: The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS). METHODS AND RESULTS: Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent. CONCLUSIONS: The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de la Arteria Coronaria
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Tomografía Computarizada de Emisión de Fotón Único
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Redes Neurales de la Computación
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Imagen de Perfusión Miocárdica
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
Límite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Nucl Cardiol
Asunto de la revista:
CARDIOLOGIA
Año:
2010
Tipo del documento:
Article
País de afiliación:
Turquía
Pais de publicación:
Estados Unidos