Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis.

The Radiologist as a Gatekeeper in Chest Pain.

Chest pain is a symptom that can be found in life-threatening conditions such as acute coronary syndrome (ACS). Those patients requiring invasive coronary angiography treatment or surgery should be identified. Often the clinical setting and laboratory tests are not sufficient to rule out a coronary or aortic syndrome. Cardiac radiological imaging has evolved in recent years both in magnetic resonance (MR) and in computed tomography (CT). CT, in particular, due to its temporal and spatial resolution, the quickness of the examination, and the availability of scanners, is suitable for the evaluation of these patients. In particular, the latest-generation CT scanners allow the exclusion of diagnoses such as coronary artery disease and aortic pathology, thereby reducing the patient's stay in hospital and safely selecting patients by distinguishing those who do not need further treatment from those who will need more- or less-invasive therapies. CT additionally reduces costs by improving long-term patient outcome. The limitations related to patient characteristics and those related to radiation exposure are weakening with the improvement of CT technology.

Machine learning-based coronary artery disease diagnosis: A comprehensive review.

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.

Diagnostic role of coronary calcium scoring in the rapid access chest pain clinic: prospective evaluation of NICE guidance.

BACKGROUND: Coronary artery calcium (CAC) imaging by unenhanced computed X-ray tomography (CT) is recommended as an initial diagnostic test for patients with stable chest pain symptoms but a low likelihood (10-29%) of underlying obstructive coronary artery disease (CAD) after clinical assessment. The recommendation has not previously been tested prospectively in a rapid access chest pain clinic (RACPC). METHODS: We recruited 300 consecutive patients presenting with stable chest pain to the RACPC of three hospitals. All patients underwent CAC imaging, followed by invasive coronary angiography (ICA) in patients with CAC ≥ 1000 Agatston units (Au) and CT coronary angiography (CTCA) in those with CAC <1000. Patients with 50-70% stenosis on CTCA underwent myocardial perfusion scintigraphy (MPS) while those with ≥ 70% stenosis underwent ICA. Obstructive CAD was defined as ≥ 70% stenosis on ICA or the presence of inducible ischaemia on MPS. Patients were followed up clinically for a mean of 17 (SD 6) months. RESULTS: The mean patient age was 60.6 (SD 9.6) years and 48% were males. Obstructive CAD was found in 56 (19%) patients, of whom 42 (14%) underwent revascularization. CAC was zero in 131 (44%) patients, of whom two (1.5%) had obstructive CAD and one (0.8%) underwent revascularization. The sensitivity, specificity, negative predictive value, and positive predictive value of CAC ≥ 1 for detection of obstructive CAD were 96, 53, 32, and 98%, respectively. None of the 57 patients with low pre-test probability of CAD and zero CAC had obstructive CAD or suffered a cardiovascular event during the follow-up. CONCLUSION: Patients with stable chest pain symptoms but a low likelihood of CAD can safely be diagnosed as not having obstructive CAD in the absence of detectable coronary calcification by unenhanced CT. Patients with CAC >400 Au have a high prevalence of obstructive CAD and further investigation with ICA or functional imaging may be warranted rather than CTCA. These findings support NICE guidance for the investigation of stable chest pain. ClinicalTrials gov identifier: NCT01464203.

Multimodality image fusion for diagnosing coronary artery disease.

Coronary artery disease (CAD) is one of the leading causes of death in the US and a substantial health-care burden in all industrialized societies. In recent years we have witnessed a constant strive towards the development and the clinical application of novel or improved detection methods as well as therapies. Particularly, noninvasive imaging is a decisive component in the cardiovascular field. Image fusion is the ability of combining into a single integrated display the anatomical as well as the physiological data retrieved by separated modalities. Clinical evidence suggests that it represents a promising strategy in CAD assessment and risk stratification by significantly improving the diagnostic power of each modality independently considered and of the traditional side-by-side interpretation. Numerous techniques and approaches taken from the image registration field have been implemented and validated in the context of CAD assessment and management. Although its diagnostic power is widely accepted, additional technical developments are still needed to become a routinely used clinical tool.