Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

Aquino, Gilberto; Chamberlin, Jordan; Mercer, Megan; Kocher, Madison; Kabakus, Ismail; Akkaya, Selçuk; Fiegel, Matthew; Brady, Sean; Leaphart, Nathan; Dippre, Andrew; Giovagnoli, Vincent; Yacoub, Basel; Jacob, Athira; Gülsün, Mehmet A.; Sahbaee, Pooyan; Sharma, Puneet; Waltz, Jeffrey; Schoepf, U. Joseph; Baruah, Dhiraj; Emrich, Tilman; Zimmerman, Stefan L.; Field, Michael E.; Agha, Ali; Burt, Jeremy R.

doi:10.1016/j.jcct.2021.12.005

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…They analyzed how the performance of AI models varies in different subgroups and identified key regions in ECG that help predict AF. Aquino et al (2022) developed a deep learning model that quantifies the volume of the left atrium using routine non‐contrast chest CT scans. The measured volumes demonstrated a significant correlation with the occurrence of new‐onset AF, hospitalization due to heart failure, and adverse cardiac events.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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“…Our study corroborates findings from the Heinz Nixdorf Recall Study and others, and further brings to light the value of non-coronary findings in coronary calcium scans for a comprehensive CVD risk assessment beyond coronary heart disease 12,13,14,15 . Although manual and automated LA volumetry in chest CT scans are relatively novel 37 ,38 , the pathophysiology of enlarged LA and its relationship with AF is well understood 39,40 .…”

Section: Discussionmentioning

confidence: 99%

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

Naghavi,

Yankelevitz,

Reeves

et al. 2024

Preprint

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Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC) of 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We then used the 5-year outcomes data and compared the C-statistic of the AI-CAC LA volume with known predictors of atrial fibrillation (AF), the CHARGE-AF Risk Score and NT-proBNP (BNP) for prediction of incident AF. The AI-CAC automated cardiac chambers volumetry took on average 21 seconds per CAC scan. Results: At 1,2,3,4, and 5 years follow up 36, 77, 123, 182, and 236 cases of AF were identified respectively. The C-statistic AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02). For year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF at year 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.

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“…The application of ML approaches in cardio-oncology is summarized in Table 1 (Ref. [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]).…”

Section: ML Approaches In Cardio-oncologymentioning

confidence: 99%

“…To date, the application of DL in the field of cardio-oncology has focused mainly on LDCT [ 29 , 30 ]. LDCT is effective in lung cancer screening in clinical trials [ 41 , 42 ], and screening for CVD comorbidities in high-risk populations undergoing LDCT is vital for reducing overall mortality.…”

Section: ML Approaches In Cardio-oncologymentioning

confidence: 99%

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

Zheng,

Chen,

Huang

et al. 2023

Rev. Cardiovasc. Med.

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A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.

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Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

Cited by 9 publications

References 17 publications

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

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