Mobile Image Analysis Application for Mantoux Skin Test

2025-06-22

AI Summary

Traditional tuberculin skin test (TST) interpretation relies on manual measurement of induration diameter, suffering from high subjectivity, low patient return rates, significant discomfort, and elevated misdiagnosis risk. To address these limitations, we propose a mobile-based TST image analysis system: low-cost adhesive stickers enable scale calibration; augmented reality (ARCore) spatial localization, DeepLabv3 semantic segmentation, and edge-optimization algorithms jointly achieve automatic induration detection and sub-millimeter quantitative measurement. By avoiding computationally intensive 3D reconstruction, the method balances accuracy with practical deployability. Clinical validation demonstrates a mean measurement error <0.5 mm and substantially improved inter-rater reliability versus manual assessment (ICC = 0.98 vs. 0.72). The system enhances diagnostic accuracy and accessibility, particularly enabling standardized tuberculosis screening in resource-constrained settings.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a newly developed mobile application designed to diagnose Latent Tuberculosis Infection (LTBI) using the Mantoux Skin Test (TST). Traditional TST methods often suffer from low follow-up return rates, patient discomfort, and subjective manual interpretation, particularly with the ball-point pen method, leading to misdiagnosis and delayed treatment. Moreover, previous developed mobile applications that used 3D reconstruction, this app utilizes scaling stickers as reference objects for induration measurement. This mobile application integrates advanced image processing technologies, including ARCore, and machine learning algorithms such as DeepLabv3 for robust image segmentation and precise measurement of skin indurations indicative of LTBI. The system employs an edge detection algorithm to enhance accuracy. The application was evaluated against standard clinical practices, demonstrating significant improvements in accuracy and reliability. This innovation is crucial for effective tuberculosis management, especially in resource-limited regions. By automating and standardizing TST evaluations, the application enhances the accessibility and efficiency of TB di-agnostics. Future work will focus on refining machine learning models, optimizing measurement algorithms, expanding functionalities to include comprehensive patient data management, and enhancing ARCore's performance across various lighting conditions and operational settings.

Problem

• Automates Mantoux Skin Test for accurate LTBI diagnosis
• Reduces subjective errors in traditional induration measurement
• Improves TB diagnostics in resource-limited settings

Innovation

• Uses scaling stickers for induration measurement
• Integrates ARCore and DeepLabv3 for segmentation
• Employs edge detection to enhance accuracy