Towards Automated Detection of Rheumatic Heart Disease Using Classical Computer Vision Techniques

• Naila Suele Mascarenhas Silva UEFS
• Carlos Alberto Rodrigues UEFS
• Airandes de Sousa Pinto UEFS
• Bráulio Muzzi Ribeiro de Oliveira IPSEMG
• Renata Fonseca Mendoza UEFS
• Maria do Carmo Pereira Nunes UFMG

Resumo

Acute Rheumatic Fever (ARF) and its primary sequela, Chronic Rheumatic Heart Disease (RHD), remain a significant public health problem, particularly in developing countries. Early diagnosis of RHD, based on echocardiographic criteria such as anterior mitral leaflet (AML) thickening, is crucial for preventing disease progression. However, manual assessment of these parameters is subjective, time-consuming, and limited by the inherently noisy quality of echocardiography images. This study proposes and validates the tip-to-base thickness ratio as a quantitative biomarker, derived from a semi-automated, interpretable pipeline for assessing AML thickening. The methodology encompasses leaflet segmentation through Otsu’s thresholding and morphological operations, followed by the extraction of its medial axis and division into anatomically relevant segments (base, middle, and tip). Validating on a dataset of 80 pediatric echocardiograms (17 ARF, 63 controls), our results indicate that the proposed tip-to-base thickness ratio was significantly higher in patients with ARF (p = 0.03), yielding an Area Under the Curve (AUC) of 0.67 for disease classification. By providing a fully transparent and reproducible measurement, this work demonstrates the feasibility of using classic computational methods to create objective tools for RHD screening, highlighting the value of interpretable classical methods for clinical decision support in RHD screening.

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