Association of Shanghai air pollution with postoperative infection in adolescent orthopedic patients: a study using a deep learning-based evolutionary model - Wang, Yufeng; Yuan, Yang; Liu, Yang; Cai, Haoqi (Frontiers in Artificial Intelligence, 2025)
BackgroundSurgical site infections (SSI) represent severe complications in adolescent orthopedic surgery. Shanghai’s complex air pollution profile creates a critical context to investigate multi-pollutant impacts on SSI risk in this vulnerable population.MethodsWe analyzed 32,261 adolescent SSI cases from Shanghai (2019–2024) alongside high-resolution pollution/meteorological data. An evolutionary deep learning model (CNN-BiGRU-Attention optimized by Improved StarFish Algorithm) and generalized additive models (GAMs) assessed lagged effects, age/gender stratification, and concentration-response relationships.ResultsNO2 and SO2 showed significantly positive associations with SSI risk at lag0 (concurrent day); O3 exhibited protective effects (strongest at lag05: −2.396% [95% CI: −3.349% to −1.443%] per 10 μg/m3 increase); Age stratification: 7–14 ages groups demonstrated heightened sensitivity to NO2/SO2. O3 effects varied across age groups; Gender differences: O3’s negative association was stronger in males; Dose–response: NO2/SO2 showed monotonic increases with no safety thresholds; O3 displayed a straight line curve.ConclusionMulti-pollutant exposure modulates SSI risk in adolescents, with NO2/SO2 as risk factors and O3 showing context-dependent protection. Deep learning identified SO2/NO2/O3 as dominant predictors, supporting perioperative air-quality interventions.