ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Human-Media Interaction
This article is part of the Research TopicOptimizing Health Outcomes through XAI and Digital Twins in Media InterventionsView all 4 articles
Explainable Artificial Intelligence-Enhanced Digital Twin Framework for Early Detection and Predictive Monitoring of Chronic Lung Abnormalities in Urban Young Adults: A Comprehensive Study of 4,247 Patients
Provisionally accepted
- 1Alliance University, Bangalore, India
- 2Alliance University Alliance College of Engineering and Design, Bengaluru, India
- 3Durban University of Technology, Durban, South Africa
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
ABSTRACT Revision Note (Reviewer 1 & 3): Abstract condensed by 35% to highlight key takeaways while maintaining essential metrics. Added concise explanation of digital twin technology and its correlation with the proposed framework as requested. Digital twin technology creates virtual replicas of physical systems enabling real-time monitoring and predictive analytics through continuous data synchronization. This study presents an explainable artificial intelligence enhanced digital twin framework specifically designed for early detection of chronic lung abnormalities in urban young adults aged 20-35 years. Analysis of 4,247 patients from Delhi metropolitan area revealed 29.3% prevalence of structural lung damage including bronchiectasis, emphysema, and fibrosis. The framework integrates multimodal physiological sensors, environmental pollution monitoring, and lifestyle data through advanced fusion algorithms. Core performance metrics demonstrate explainability coefficient ξexp = 0.847 ± 0.023, prediction accuracy αpred = 0.923 ± 0.034, and early detection capability extending tearly = 6.7±1.2 months before clinical symptoms. Mathematical modeling incorporates bronchial resistance Rb = 2.34 ± 0.45 cmH2O/L/s, lung compliance CL = 0.187 ± 0.032 L/cmH2O, and deterioration rate λdet = 0.0156 ± 0.0023 per month from longitudinal monitoring. Blockchain integration ensures data security with hash validation efficiency ηhash = 0.987 and real-time processing latency τresp = 127.3 ± 15.7 milliseconds. Validation across 1,847 test subjects achieved sensitivity Searly = 0.891, specificity Spearly = 0.876, and positive predictive value PPV = 0.834. Environmental factor integration including air quality index AQI = 247±67 enables personalized risk stratification accuracy βrisk = 0.876 ± 0.045. Statistical analysis confirmed significant improvements in diagnostic timing (p ¡ 0.001), intervention effectiveness (p ¡ 0.001), and patient outcomes compared to conventional approaches. Clinical implementation demonstrates 68.4% reduction in diagnostic delays, 73.6% improvement in intervention timing, and annual healthcare cost savings of ∆C = $2, 847 per patient.
Keywords: BLOCKCHAIN SECURITY, chronic lung disease, Digital twin technology, Early detection systems, Environmental health monitoring, Explainable artificial intelligence, Predictive healthcare analytics, Urban Health
Received: 24 Jun 2025; Accepted: 09 Jan 2026.
Copyright: © 2026 Sungheetha, R and Aroba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Akey Sungheetha
Oluwasegun Julius Aroba
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.