Abstract
Mantaux test is used for analyzing tuberculosis. Various factors like drug history, immunological status, age, coexisting illness, administration flaws influence its outcome, so also its interpretation. There is a lot of inconsistency involved in the existing approaches in measuring the wheal size in skin tests. This work presents a computer aided clinical assessment system that analyzes the outcomes of the mantaux test. The assessment system uses medical photography to acquire forearm images from patients and image processing techniques to obtain geometric features from the image. These features are used to quantify the size of the wheals of the skin test. The size of the wheal is quantified using area, perimeter, length and breadth of the bounding box. The system is integrated with a smartphone application built using React Native which is connected to the backend servers which help in running the image processing algorithms and delivering the results. The NodeJS server and the Flask server connected to AWS S3 Bucket store all the images. The servers are deployed on an Oracle Cloud instance which ensures all-time online availability. The application and the computing approaches presented in this work minimize the errors and inconsistencies involved in the reading and interpretation of skin test outcomes; moreover, using computer aided diagnosis would enhance automation of healthcare informatics at clinics and hospitals.
This work was funded by SCIENCE & ENGINEERING RESEARCH BOARD, Department of Science & Technology, Government of India; Startup Research Grant, File No.: SERB/SRG/2019/001801, November 2019.
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Acknowledgment
The authors thank the senior allergist, Dr. George Moses, for providing conceptual guidance and valuable suggestions to improve this work. The authors also thank the clinicians at the Good Samaritan Lab Services and Allergy Testing Centre, Kilpauk, Chennai, India, for supporting in data collection. The authors would like to thank Dr.Praylin and the clinicians at the Joyce Clinical Lab Services, Marthandam, Kanyakumari District for supporting us in data pre-processing, and application development stages.
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Kavya, R. et al. (2022). CASTA: Clinical Assessment System for Tuberculosis Analysis. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_21
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