Artificial intelligence and machine learning in cardiovascular computed tomography

Seetharam, Karthik; Bhat, Premila; Orris, Maxine; Prabhu, Hejmadi; Shah, Jilan; Asti, Deepak; Chawla, Preety; Mir, Tanveer

doi:10.4330/wjc.v13.i10.546

Cited by 3 publications

(6 citation statements)

References 60 publications

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“…Transthoracic echocardiography is routinely preferred in the quantification and assessment of epicardial adipose tissue, but cardiac CT is another useful means of evaluation, presently gaining in popularity. The latter is not a dynamic, human-dependent procedure; thus, it can be assisted by AI algorithms in achieving automated measurements of epicardial adipose tissue so as to increase accuracy and reliability of outcomes whilst reducing the time of clinical examination [13].…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Harnessing Artificial Intelligence for Automated Diagnosis

Zachariadis,

Leligou

2024

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The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented.

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Section: Cardiovascular Systemmentioning

confidence: 99%

Harnessing Artificial Intelligence for Automated Diagnosis

Zachariadis,

Leligou

2024

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“…AI can be integrated with virtual planning to create digital twins, and various interventional treatment procedures can be tested on the twin before the actual intervention [1]. Instead of disrupting workflow, ML can be a trustworthy companion which can streamline several processes [23]. Furthermore, it can allow more time for physicians to interact with their patients for meaningful interactions.…”

Section: Evolving Beliefs and Perceptions On MLmentioning

confidence: 99%

“…The use of CT is ubiquitous in current cardiology practice for diagnosing coronary artery disease (CAD) and procedure planning for structural heart interventions [ 22 , 23 ]. It plays a seminal role in the evaluation of patients with chest pain.…”

Section: Application Of ML In Cardiovascular Imagingmentioning

confidence: 99%

“…Life never moves from point A to B but moves dynamically or haphazardly. Greater integration of ML could be instrumental to clinical trials [ 23 ]. Many randomized clinical trials fail to reach completion because of poorly defined objectives, inadequate planning, poor execution, and inadequately powered endpoints [ 23 ].…”

Section: Evolving Beliefs and Perceptions On MLmentioning

confidence: 99%

“…Greater integration of ML could be instrumental to clinical trials [ 23 ]. Many randomized clinical trials fail to reach completion because of poorly defined objectives, inadequate planning, poor execution, and inadequately powered endpoints [ 23 ]. Preliminary analysis by these algorithms can offer valuable information to clinical investigators, and clinical trials can be managed more effectively [ 17 ].…”

Section: Evolving Beliefs and Perceptions On MLmentioning

confidence: 99%

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Applications of Machine Learning in Cardiology

et al. 2022

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In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

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Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging

Alahdab,

El Shawi,

Ahmed

et al. 2023

PLoS ONE

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Background Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. Methods Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. Results A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction’s sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). Conclusion ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.

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Artificial intelligence and machine learning in cardiovascular computed tomography

Cited by 3 publications

References 60 publications

Harnessing Artificial Intelligence for Automated Diagnosis

Harnessing Artificial Intelligence for Automated Diagnosis

Applications of Machine Learning in Cardiology

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging

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