Digital clubbing is an important clinical sign associated with a range of cardiopulmonary diseases; however, its detection and severity assessment in routine practice largely rely on subjective visual inspection.This study proposes an automated, smartphone-based system for real-time detection and severity assessment of digital clubbing using deep learning techniques.The system integrates the YOLOv8 object detection model for initial clubbing classification, the KeypointNet model for anatomical landmark localization, and a novel Clubbing Fingers Severity Analysis (CFSA) algorithm to quantify the Lovibond angle and grade disease severity.Finger images were acquired using a smartphone camera with an OpenCV-based preprocessing strategy to standardize finger-to-camera distance and improve image consistency.Model performance was evaluated using publicly available anonymized datasets.The proposed system achieved an overall accuracy of 94.7% for digital clubbing detection and severity classification.The YOLOv8 model attained a classification accuracy of 92.5%, while the KeypointNet model achieved a landmark localization accuracy of 96.5%.Notably, the recall for severe digital clubbing reached 94.0%, indicating strong sensitivity for identifying high-risk cases.By providing real-time, non-invasive, and reproducible assessments, the proposed system addresses the limitations of conventional visual examination and supports objective severity grading.Although further clinical validation is required, this smartphone-based approach demonstrates strong potential as a preliminary screening support tool for early identification of digital clubbing in clinical and community-based settings, particularly in resource-limited environments.
Wang et al. (Mon,) studied this question.