Explainable AI digital twin framework for early lung disease detection

Abstract

Introduction 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 the early detection of chronic lung abnormalities in urban young adults aged 20–35 years. Methods Analysis of 4,247 patients from the Delhi metropolitan area revealed a 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. Mathematical modeling incorporates bronchial resistance R b = 2.34 ± 0.45 cmH 2 O/L/s, lung compliance C L = 0.187 ± 0.032 L/cmH 2 O, and deterioration rate λ det = 0.0156 ± 0.0023 per month from longitudinal monitoring. Blockchain integration ensures data security with hash validation efficiency η hash = 0.987 and real-time processing latency τ resp = 127.3 ± 15.7 ms. Environmental factor integration, including the air quality index AQI = 247 ± 67, enables personalized risk stratification accuracy β risk = 0.876 ± 0.045. Results Core performance metrics demonstrate explainability coefficient ξ exp = 0.847 ± 0.023, prediction accuracy α pred = 0.923 ± 0.034, and early detection capability extending t early = 6.7 ± 1.2 months before clinical symptoms. Validation across 1,847 test subjects achieved sensitivity, S early = 0.891, specificity, Sp early = 0.876, and positive predictive value (PPV) = 0.834. Environmental factor integration, including the air quality index AQI = 247 ± 67, enables personalized risk stratification accuracy β risk = 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. Discussion Clinical implementation demonstrates 68.4% reduction in diagnostic delays, 73.6% improvement in intervention timing, and annual healthcare cost savings of Δ C = $2, 847 per patient.