Article: Exposure dose reconstruction-guided imaging screening of tuberculosis close contacts using IoT-based localisation Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.94 pp.90 - 109 Abstract: In screening close contacts of tuberculosis patients, current approaches classify contacts as 'close' or 'general' based solely on exposure duration, ignoring environmental ventilation, contact distance, and spatial distribution. Many contacts with subclinical lesions are missed while low-risk individuals undergo unnecessary imaging. This paper proposes the Exposure-weighted lesion profiling framework, which uses internet of things positioning data to reconstruct individualised exposure doses and guide computed tomography screening. A prospective cohort of 214 close contacts showed that the area under the curve for predicting subclinical lesions using ventilation-adjusted exposure doses was 0.91, superior to 0.68 of traditional binary classification. Prioritising screening of the top 20% of contacts by this framework increased lesion detection rate from 8.2% to 23.6% and reduced number needed to screen to detect one positive case from 12.6 to 5.9. This framework fills the gap from binary contact classification to continuous dose-response models. Inderscience Publishers - linking academia, business and industry through research

Title: Exposure dose reconstruction-guided imaging screening of tuberculosis close contacts using IoT-based localisation

Authors: Yue Yan

Addresses: School of Medical Technology, Anyang Vocational and Technical College, Anyang, 455000, China

Abstract: In screening close contacts of tuberculosis patients, current approaches classify contacts as 'close' or 'general' based solely on exposure duration, ignoring environmental ventilation, contact distance, and spatial distribution. Many contacts with subclinical lesions are missed while low-risk individuals undergo unnecessary imaging. This paper proposes the Exposure-weighted lesion profiling framework, which uses internet of things positioning data to reconstruct individualised exposure doses and guide computed tomography screening. A prospective cohort of 214 close contacts showed that the area under the curve for predicting subclinical lesions using ventilation-adjusted exposure doses was 0.91, superior to 0.68 of traditional binary classification. Prioritising screening of the top 20% of contacts by this framework increased lesion detection rate from 8.2% to 23.6% and reduced number needed to screen to detect one positive case from 12.6 to 5.9. This framework fills the gap from binary contact classification to continuous dose-response models.

Keywords: contact investigation; exposure dose; subclinical tuberculosis; internet of things geolocation.

DOI: 10.1504/IJICT.2026.156124

International Journal of Information and Communication Technology, 2026 Vol.27 No.94, pp.90 - 109

Received: 31 May 2026
Accepted: 01 Jul 2026
Published online: 04 Sep 2026 *

Title: Exposure dose reconstruction-guided imaging screening of tuberculosis close contacts using IoT-based localisation

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