Mobile phone-based evaluation of talent tuberculosis infection

The tuberculin skin test (TST) is the most widely used method for detecting latent tuberculosis (TB) infection (LTBI) in adults and active TB disease in children. This work presents the development of a screening tool to detect LTBI's, which works in conjunction with the TST and serves as an al...

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Bibliographic Details
Main Author: Naraghi, Safa Kagiso
Other Authors: Mutsvangwa, Tinashe E M
Format: Dissertation
Language:English
Published: University of Cape Town 2018
Subjects:
Online Access:http://hdl.handle.net/11427/27831
Description
Summary:The tuberculin skin test (TST) is the most widely used method for detecting latent tuberculosis (TB) infection (LTBI) in adults and active TB disease in children. This work presents the development of a screening tool to detect LTBI's, which works in conjunction with the TST and serves as an alternative for measuring the TST induration. The screening tool makes use of a mobile application developed on the Android platform to capture images of an induration, and photogrammetric reconstruction using Agisoft PhotoScan to reconstruct the induration in 3D, followed by 3D measurement of the induration with the aid of Python functions. The screening accuracy of the developed process was tested using a 3D printed induration and an HTC One smartphone to capture images. In this accuracy test, the developed screening tool was found to measure indurations more accurately than current measurement methods, as indicated by the lower standard deviation produced. An experiment to simulate real-world conditions was conducted by using the developed screening tool on a set of mock skin indurations, created by a make-up artist, and evaluating its performance. It was found that the height of the skin induration and definition of its margins are the most significant factors that influence the accuracy of the screening tool under simulated real-world conditions. Future work should explore possible improvements to the developed image capture protocol and the bimodal segmentation methods employed in this project.