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The Role of AI in Characterizing the DCM Phenotype
Clint Asher1,2, Esther Puyol-Antón1, Maleeha Rizvi1,2
1Department of Cardiovascular Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Insights
Artificial intelligence, particularly deep learning, enhances cardiac MRI analysis for Dilated Cardiomyopathy (DCM). This approach offers improved phenotyping, risk stratification, and personalized treatment strategies beyond conventional methods for better patient outcomes.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Dilated Cardiomyopathy (DCM) diagnosis relies on left ventricular dilatation and dysfunction, but conventional assessments like ejection fraction inadequately predict outcomes in this heterogeneous disease.
- Despite advances in heart failure therapy, many DCM patients face adverse events, highlighting the need for refined phenotyping and risk stratification.
- Personalized medicine requires advanced tools to integrate diverse data for a comprehensive understanding of DCM.
Purpose of the Study:
- To review the role of artificial intelligence (AI), specifically deep learning, in enhancing cardiac magnetic resonance (CMR) imaging for Dilated Cardiomyopathy (DCM) phenotyping and risk stratification.
- To explore how AI-powered CMR analysis can provide more precise functional and tissue characterization beyond current clinical standards.
- To discuss the integration of AI with clinical, genetic, and biochemical data for improved DCM classification and outcome prediction.
Main Methods:
- Review of current literature on AI applications in cardiac MRI for DCM.
- Focus on deep learning techniques for image analysis and feature extraction.
- Integration of CMR data with clinical, genetic, and biochemical information.
Main Results:
- AI and deep learning offer advanced quantitative measures of cardiac function and tissue characterization via CMR, surpassing standard assessments.
- Deep learning platforms can overcome limitations in current clinical workflows, enabling better differentiation of at-risk DCM subgroups.
- AI facilitates the integration of multi-modal data (imaging, genetics, clinical variables) for enhanced DCM classification and risk prediction.
Conclusions:
- AI, particularly deep learning applied to CMR, presents a significant advancement in characterizing the Dilated Cardiomyopathy phenotype.
- These technologies promise more accurate risk stratification and personalized treatment strategies for DCM patients.
- The integration of AI with comprehensive data sources is crucial for improving diagnosis and clinical outcomes in DCM.
Abstract:
Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.
