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.
Abstract (translated)
本文介绍了一款新开发的移动应用程序，旨在使用Mantoux皮肤测试（TST）诊断潜伏性结核感染（LTBI）。传统的TST方法经常面临随访率低、患者不适和主观手动解读的问题，尤其是使用圆珠笔测量时，这会导致误诊和治疗延迟。此前开发的一些采用3D重建技术的移动应用中，该应用程序则利用缩放贴纸作为参考物来测量皮肤硬结。 此移动应用集成了先进的图像处理技术，包括ARCore，并运用了DeepLabv3等机器学习算法来进行稳健的图像分割和精确测量LTBI特征性皮肤硬化的尺寸。系统还采用边缘检测算法以提高准确性。该应用程序经过标准临床实践评估，显示出显著提高了准确性和可靠性。 这项创新对于有效管理结核病至关重要，尤其是在资源有限的地区。通过自动化和标准化TST评估流程，该应用增强了TB诊断的可访问性和效率。未来的工作将集中于改进机器学习模型、优化测量算法、扩展功能以包括全面的患者数据管理和增强ARCore在不同光照条件及操作环境下的性能。
URL
https://arxiv.org/abs/2506.17954
