Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and COVİD-19

Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated artificial intelligence-based hybrid anomaly detection technique composed of various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated artificial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated artificial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classification technique based on artificial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated artificial intelligence-based hybrid anomaly detection technique, we have demonstrated a significant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated artificial intelligence-based hybrid anomaly detection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, specificity of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the artificial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it outperformed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of coronary artery diseases by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated artificial intelligence-based hybrid anomaly detection technique may serve as an efficient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly beneficial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated artificial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase-polymerase chain reaction (RT-PCR) curves, thorax computed tomography images, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomography images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneumonia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present significant challenges for differential diagnosis. We developed a new automated artificial intelligence-based hybrid clinical decision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and artificial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated artificial intelligence-based hybrid clinical decision support technique benefits from the diversity in different data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classification performance results of the proposed convolutional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, specificity of 92.5%, precision of 80.4%, and F1-score of 86%. Therefore, it outperformed thorax computed tomography in terms of the specificity of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological findings. Therefore, it has great potential to be successfully used in the differential diagnosis of pulmonary diseases containing ground-glass opacities. The binary classification performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, specificity of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has comparable sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classification performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, specificity of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis. Furthermore, the multi-class classification performance results of the proposed automated artificial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sensitivity of 66.3%, specificity of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the differential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classification performance results of the automated artificial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensitivity of 90%, specificity of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and specificity compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated artificial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demonstrate its ability to provide highly reliable diagnosis of COVID-19 by jointly analyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may significantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.