R Rahmani1, P Niazi2, M Naseri2, M Neishabouri2, S Farzanefar2, M Eftekhari3, F Derakhshan2, R Mollazadeh1, A Meysami4, M Abbasi5. 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. 5. Department of Nuclear Medicine, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: meabbasi@tums.ac.ir.
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.
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.
Authors: Minna Husso; Isaac O Afara; Mikko J Nissi; Antti Kuivanen; Paavo Halonen; Miikka Tarkia; Jarmo Teuho; Virva Saunavaara; Pauli Vainio; Petri Sipola; Hannu Manninen; Seppo Ylä-Herttuala; Juhani Knuuti; Juha Töyräs Journal: Ann Biomed Eng Date: 2020-08-20 Impact factor: 3.934