Why the study?
The study aimed to develop and validate an artificial intelligence-aid method in myocardial perfusion imaging to differentiate ischemia in coronary artery disease.
Does an AI-aid method improve the diagnostic accuracy of myocardial perfusion imaging in patients with suspected coronary artery disease compared to physician interpreters?
Does an AI-aid method improve the diagnostic accuracy of myocardial perfusion imaging in patients with suspected coronary artery disease compared to physician interpreters?
An AI-based YOLO neural network demonstrated excellent predictive accuracy for detecting ischemia on myocardial perfusion imaging, outperforming human interpreters and significantly reducing interpretation time.
BACKGROUND: This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS: We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS: Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION: The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.
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Zhang et al. (2023) studied this question.
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