Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data

• Published in: Revista Española de medicina nuclear e imagen molecular (English ed.) 2019-09, Vol.38 (5), p.275-279
• Main Authors: Rahmani, Reza, Niazi, Parisa, Naseri, Maryam, Neishabouri, Mohamadreza, Farzanefar, Saeed, Eftekhari, Mohammad, Derakhshan, Farhang, Mollazadeh, Reza, Meysami, Alipasha, Abbasi, Mehrshad
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Coronary artery disease; Enfermedad de las arterias coronarias; Gensini score; Imagen de perfusión miocárdica; Myocardial perfusion imaging; Puntuación de gensini; Radiology; Redes neurales artificiales
• ISSN: 2253-8089; 2253-8089
• DOI: 10.1016/j.remnie.2019.04.005

AbstractObjectiveDiagnostic 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. MethodsOut 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