Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]

Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion] Publisher Pubmed

Rahmani R¹ ; Niazi P² ; Naseri M² ; Neishabouri M² ; Farzanefar S² ; Eftekhari M³ ; Derakhshan F² ; Mollazadeh R¹ ; Meysami A⁴ ; Abbasi M²

Show Affiliations

1. Cardiology Department, Imam-Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
2. Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
3. Research Institute for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
4. Department of Social Medicine, Tehran University of Medical Sciences, Tehran, Iran

Source: Revista Espanola de Medicina Nuclear e Imagen Molecular Published:2019

Abstract

Objective: Diagnostic accuracy of myocardial perfusion imaging (MPI) is not optimal to predict the result of angiography. The current study aimed at investigating the application of artificial neural network (ANN) to integrate the clinical data with the result and quantification of MPI. Methods: Out of 923 patients with MPI, 93 who underwent angiography were recruited. The clinical data including the cardiac risk factors were collected and the results of MPI and coronary angiography were recorded. The quantification of MPI polar plots (i.e. the counts of 20 segments of each stress and rest polar plots) and the Gensini score of angiographies were calculated. Feed-forward ANN was designed integrating clinical and quantification data to predict the result of angiography (normal vs. abnormal), non-obstructive or obstructive coronary artery disease (CAD), and Gensini score (≥10 and <10). The ANNs were designed to predict the results of angiography using different combinations of data as follows: reports of MPI, the counts of 40 segments of stress and rest polar plots, and the count of these 40 segments in addition to age, gender, and the number of risk factors. The diagnostic performance of MPI with different ANNs was compared. Results: The accuracy of MPI to predict the result of angiography, obstructive CAD, and Gensini score increased from 81.7% to 92.9%, 65.0% to 85.7%, and 50.5% to 92.9%, respectively by ANN using counts and clinical risk factors. Conclusion: The diagnostic accuracy of MPI could be improved by ANN, using clinical and quantification data. © 2019 Sociedad Espanola de Medicina Nuclear e Imagen Molecular

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Style	Citing Format
MLA	Rahmani R, et al.. "Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]." Revista Espanola de Medicina Nuclear e Imagen Molecular, vol. 38, no. 5, 2019, pp. 275-279.
APA	Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, Derakhshan F, Mollazadeh R, Meysami A, Abbasi M (2019). Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]. Revista Espanola de Medicina Nuclear e Imagen Molecular, 38(5), 275-279.
Chicago	Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, Derakhshan F, Mollazadeh R, Meysami A, Abbasi M. "Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]." Revista Espanola de Medicina Nuclear e Imagen Molecular 38, no. 5 (2019): 275-279.
Harvard	Rahmani R et al. (2019) 'Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]', Revista Espanola de Medicina Nuclear e Imagen Molecular, 38(5), pp. 275-279.
Vancouver	Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, et al.. Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]. Revista Espanola de Medicina Nuclear e Imagen Molecular. 2019;38(5):275-279.
BibTex	@article{ author = {Rahmani R and Niazi P and Naseri M and Neishabouri M and Farzanefar S and Eftekhari M and Derakhshan F and Mollazadeh R and Meysami A and Abbasi M}, title = {Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]}, journal = {Revista Espanola de Medicina Nuclear e Imagen Molecular}, volume = {38}, number = {5}, pages = {275-279}, year = {2019} }
RIS	TY - JOUR AU - Rahmani R AU - Niazi P AU - Naseri M AU - Neishabouri M AU - Farzanefar S AU - Eftekhari M AU - Derakhshan F AU - Mollazadeh R AU - Meysami A AU - Abbasi M TI - Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion] JO - Revista Espanola de Medicina Nuclear e Imagen Molecular VL - 38 IS - 5 SP - 275 EP - 279 PY - 2019 ER -

Style

Citing Format

MLA

Rahmani R, et al.. "Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]." Revista Espanola de Medicina Nuclear e Imagen Molecular, vol. 38, no. 5, 2019, pp. 275-279.

APA

Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, Derakhshan F, Mollazadeh R, Meysami A, Abbasi M (2019). Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]. Revista Espanola de Medicina Nuclear e Imagen Molecular, 38(5), 275-279.

Chicago

Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, Derakhshan F, Mollazadeh R, Meysami A, Abbasi M. "Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]." Revista Espanola de Medicina Nuclear e Imagen Molecular 38, no. 5 (2019): 275-279.

Harvard

Rahmani R et al. (2019) 'Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]', Revista Espanola de Medicina Nuclear e Imagen Molecular, 38(5), pp. 275-279.

Vancouver

Rahmani R, Niazi P, Naseri M, Neishabouri M, Farzanefar S, Eftekhari M, et al.. Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]. Revista Espanola de Medicina Nuclear e Imagen Molecular. 2019;38(5):275-279.

BibTex

@article{ author = {Rahmani R and Niazi P and Naseri M and Neishabouri M and Farzanefar S and Eftekhari M and Derakhshan F and Mollazadeh R and Meysami A and Abbasi M}, title = {Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]}, journal = {Revista Espanola de Medicina Nuclear e Imagen Molecular}, volume = {38}, number = {5}, pages = {275-279}, year = {2019} }

RIS

TY - JOUR
AU - Rahmani R
AU - Niazi P
AU - Naseri M
AU - Neishabouri M
AU - Farzanefar S
AU - Eftekhari M
AU - Derakhshan F
AU - Mollazadeh R
AU - Meysami A
AU - Abbasi M
TI - Improved Diagnostic Accuracy for Myocardial Perfusion Imaging Using Artificial Neural Networks on Different Input Variables Including Clinical and Quantification Data; [Precision Diagnostica Mejorada Para La Imagen De Perfusion Miocardica Usando Redes Neuronales Artificiales En Diferentes Variables De Entrada Incluyendo Datos Clinicos Y De Cuantificacion]
JO - Revista Espanola de Medicina Nuclear e Imagen Molecular
VL - 38
IS - 5
SP - 275
EP - 279
PY - 2019
ER -

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