Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque

Scicolone, Roberta; Vacca, Sebastiano; Pisu, Francesco; Benson, John C.; Nardi, Valentina; Lanzino, Giuseppe; Suri, Jasjit S.; Saba, Luca

doi:10.1016/j.ejrad.2024.111497

European Journal of Radiology

2024

DOI: 10.1016/j.ejrad.2024.111497

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Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque

Roberta Scicolone,

Sebastiano Vacca,

Francesco Pisu

et al.

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Cited by 3 publications

References 68 publications

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Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Shi,

Feng,

Zhi

et al. 2024

MedComm

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Cognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far‐reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative‐induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular‐induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high‐dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning‐based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use.

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Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Shi,

Feng,

Zhi

et al. 2024

MedComm

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Coronary Plaque Characterization and Cardiovascular Risk Using Radiomics and Artificial Intelligence

Ionita,

Canty

2024

Trends in Cardiovascular Medicine

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An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review

Saba,

Maindarkar,

Khanna

et al. 2024

Rev. Cardiovasc. Med.

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Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients. This study aimed to address this gap by proposing three hypotheses: (i) a strong relationship exists between OSA and ASCVD/stroke, (ii) deep learning (DL) can stratify ASCVD/stroke risk in OSA patients using surrogate carotid imaging, and (iii) including OSA risk as a covariate with cardiovascular risk factors can improve CVD risk stratification. Methods: The study employed the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) search strategy, yielding 191 studies that link OSA with coronary, carotid, and aortic atherosclerotic vascular diseases. This research investigated the link between OSA and CVD, explored DL solutions for OSA detection, and examined the role of DL in utilizing carotid surrogate biomarkers by saving costs. Lastly, we benchmark our strategy against previous studies. Results: (i) This study found that CVD and OSA are indirectly or directly related. (ii) DL models demonstrated significant potential in improving OSA detection and proved effective in CVD risk stratification using carotid ultrasound as a biomarker. (iii) Additionally, DL was shown to be useful for CVD risk stratification in OSA patients; (iv) There are important AI attributes such as AI-bias, AI-explainability, AI-pruning, and AI-cloud, which play an important role in CVD risk for OSA patients. Conclusions: DL provides a powerful tool for CVD risk stratification in OSA patients. These results can promote several recommendations for developing unique, bias-free, and explainable AI algorithms for predicting ASCVD and stroke risks in patients with OSA.

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Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque

Cited by 3 publications

References 68 publications

Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Coronary Plaque Characterization and Cardiovascular Risk Using Radiomics and Artificial Intelligence

An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review

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