Overcoming measurement challenges in clinical practice: a deep learning-based approach to monocular surface area measurement.

Background: Precise area measurement of body surfaces is essential in clinical trials and clinical practice, particularly for the clinician, to monitor lesion size, assess skin condition severity and determine precise treatment dosage. The inherent difficulty of this task lies in the analysis of irregular surfaces or lesions with complex outlines. Consequently, existing solutions often lack accuracy or rely on expensive and complex technology, limiting their practicality. Objectives: To introduce a cost-effective and accessible artificial intelligence (AI)-driven methodology that accurately measures body surface areas by effectively accounting for surface relief, outline and orientation. Methods: The proposed approach is based on the analysis of monocular smartphone images. By using a set of specific markers, a state-of-the-art depth estimation model and surface triangulation, the area of delimited body surfaces can be measured with high precision. Results: We tested this area measurement technology in controlled and simulated dermatological scenarios where a precise gold standard can be established. The proposed methodology achieves high precision and robustness across different camera configurations and body surfaces of different anatomical complexity. Notably, the proposed approach significantly outperforms traditional methods with a mean absolute error of 20 mm Conclusions: The results consistently demonstrate high accuracy while being an accessible and time-efficient solution. When integrated into an AI-based medical device, this framework improves its usability and broadens its applications to tasks such as lesion severity assessment and clinical trial standardization, making it a valuable tool for clinicians and patients.