Defining two dimensional transthoracic echocardiographic aorta dimension reference ranges in healthy subjects: a machine learning based model Article Swipe
Andreina Carbone
,
Francesco Ferrara
,
Mario Zanfardino
,
Monica Franzese
,
Sara Rega
,
Antonio D’Agostino
,
Erica Maffei
,
Carlo Cavaliere
,
Filippo Cademartiri
,
Eduardo Bossone
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/eurjpc/zwaf236.221
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/eurjpc/zwaf236.221
Background Proximal aortic dimensions are influenced by a complex interaction of age, sex, and anthropometric factors. Thus defining their full range in healthy subjects remains a challenge, even in the era of artificial intelligence (AI). Purpose This study sought to design machine learning (ML)-based models integrated with AIDA (Artificial Intelligence for Diameters of Aorta) web application in order to estimate two-dimensional transthoracic echocardiographic (TTE) proximal aorta diameters reference range in healthy subjects. Methods The study population consisted of 788 caucasian healthy subjects (57% women; mean age 43±15, range 16-92 years-old). They all underwent standardized TTE as for current EACVI/ASE guidelines. In particular, TTE proximal ascending aortic diameters were measured both in diastolic leading edge-to-leading edge (DLE) and in systolic inner edge-to-inner edge (SIE) at four levels (aortic annulus [AA], sinus of Valsalva [SV], sinotubular junction [STJ] and ascending aorta [AscAo]). Unsupervised Random Forest (RF)-based clustering was performed separately for 3 age groups (defined by a quartiles analysis): i) group 1: ≤32 (n=206), ii) group 2: 33-53 (n=373); iii) group 3: >53 years old (n=209) on both measurement techniques using aorta diameters normalized by BSA and height and according to sex. The feature importance (FI) weight from the RF algorithm was analyzed to determine the most informative characteristics for clusters identified by RF. A score based on FI weights was used to assign new subjects to the identified cluster. This score represented the basis of the AIDA functioning web-app. Results Study population demographic, clinical and aorta diameters data are presented in Table 1. The analysis of aortic diameters revealed a significant increase (p <0.05) of the aortic size at AA, SV, STJ and AscAo across the age groups,for both measurement techniques (DLE and SIE). This trend was maintained if aortic diameters were normalized to BSA or height. AA showed a non-significant increase between the groups 1 and 2 when BSA normalized (DLE: p = 0.36; SIE: p = 0.28).From RF-based clustering, 2 clusters (for both DLE and SIE) for each age group were identified representing individuals with specific anthropometric characteristics (age, weight, height, BSA and BMI), showing significant difference (p<0.05) among clusters in all aortic diameters (AA, SV, STJ, AscAo). Moreover, a method based on feature importance weight was developed to assign new subjects to one of identified clusters (the method can be used through AIDA web-app), with a mean accuracy > 0.8. Conclusion This study highlights the utility of unsupervised clustering techniques to define aorta dimension physiologic variations (Figure 1). The ML-based AIDA web application provides a user-friendly and efficient tool for clinicians to establish individualized aortic diameters reference ranges, enhancing both risk assessment and clinical decision-making processes.Table 1.Study population characteristicsFigure 1.ML models for aortic evaluation
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/eurjpc/zwaf236.221
- https://academic.oup.com/eurjpc/article-pdf/32/Supplement_1/zwaf236.221/63229024/zwaf236.221.pdf
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410499303
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410499303Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/eurjpc/zwaf236.221Digital Object Identifier
- Title
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Defining two dimensional transthoracic echocardiographic aorta dimension reference ranges in healthy subjects: a machine learning based modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-01Full publication date if available
- Authors
-
Andreina Carbone, Francesco Ferrara, Mario Zanfardino, Monica Franzese, Sara Rega, Antonio D’Agostino, Erica Maffei, Carlo Cavaliere, Filippo Cademartiri, Eduardo BossoneList of authors in order
- Landing page
-
https://doi.org/10.1093/eurjpc/zwaf236.221Publisher landing page
- PDF URL
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https://academic.oup.com/eurjpc/article-pdf/32/Supplement_1/zwaf236.221/63229024/zwaf236.221.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/eurjpc/article-pdf/32/Supplement_1/zwaf236.221/63229024/zwaf236.221.pdfDirect OA link when available
- Concepts
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Medicine, Dimension (graph theory), Cardiology, Aorta, Internal medicine, Radiology, Pure mathematics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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