Mobile Image Analysis Application for Mantoux Skin Test

TL;DR

This paper introduces a mobile app that automates and improves the accuracy of Mantoux Skin Test interpretation for tuberculosis diagnosis using advanced image processing and machine learning, especially beneficial in resource-limited settings.

Contribution

The app uniquely combines ARCore, DeepLabv3, and edge detection to standardize and automate TST measurement, addressing limitations of manual and previous digital methods.

Findings

Significantly improved measurement accuracy over traditional methods

Enhanced reliability in induration detection using machine learning

Potential to increase TB diagnosis accessibility in resource-limited regions

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.