Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis

Covas, Pedro; Guzman, Eison De; Barrows, Ian R.; Bradley, Andrew J.; Choi, Brian G.; Krepp, Joseph; Lewis, Jannet F.; Katz, Richard J.; Tracy, Cynthia M.; Zeman, Robert K.; Earls, James P.; Choi, Andrew

doi:10.3389/fcvm.2022.839400

Cited by 8 publications

(7 citation statements)

References 69 publications

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“…AI includes machine learning (ML), significant data management and information mining, image processing and pattern recognition. ML is the approach and core of medical AI, including supervised learning, unsupervised learning and reinforcement learning ( 40 , 41 ). Supervised learning refers to the application of known cohorts as known information of learning, so as to build a classification and prediction model for unknown cohorts ( 42 ).…”

Section: Ai In the Radiomics Of Ocmentioning

confidence: 99%

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Wang,

Lin,

Zhuang

et al. 2024

Oncol Rep

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Artificial intelligence (AI) has emerged as a crucial technique for extracting high-throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI-based radiomics has proven to be a non-invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

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Section: Ai In the Radiomics Of Ocmentioning

confidence: 99%

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Wang,

Lin,

Zhuang

et al. 2024

Oncol Rep

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“…CNN and improved models are widely employed in medical image processing ( 9 ). In the field of vascular diseases, deep learning-based predictive models have yielded significant achievements in various areas, including coronary artery disease ( 10 , 11 ), stroke ( 12 , 13 ), and thrombotic disorders ( 14 , 15 ). This study provides a summary of the current applications of deep learning technology in the diagnosis and treatment of AA disease.…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

Huang,

Lu,

Xiao

et al. 2024

Front. Cardiovasc. Med.

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ObjectiveThis study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction.MethodsA comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes.ResultsDeep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy.ConclusionDeep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management.

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“…Several reviews have been published to address this issue in ASCVD. In fact, the majority of existing reviews focus on coronary artery (CAD) and cardiac disease, in general (10,11), or, more specifically, on imaging applications in CAD (12)(13)(14). For this reason, a separate category of reviews concentrates solely on non-cardiac ASCVD (15,16).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in atherosclerotic disease: Applications and trends

Kampaktsis

Emfietzoglou²,

Shehhi³

et al. 2023

Front. Cardiovasc. Med.

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Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

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Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis

Cited by 8 publications

References 69 publications

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

Artificial intelligence in atherosclerotic disease: Applications and trends

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