Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an arti cial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSON 2.0/EUPS protocol) for cases requiring 3-month follow-up. To assess true algorithmic robustness, all AI-detected baseline candidate nodules (≥ 100 mm³) proceeded to fully automated longitudinal matching without any manual selection. The pulmonary AI identi ed 181 participants with 378 baseline nodules ≥ 100 mm³. In total, 39 nodules had naturally resolved at follow-up. The pulmonary AI achieved an 83.5% (283/339; 95% CI: 79.2–87.1%) matching success rate for 339 persisting nodules. Matching performance was 91.8% (89/97) for participants with a single baseline candidate nodule (59.7% of the cohort) and 72.8% (75/103) for participants with more than ve nodules (6.6%). Expert review of the 56/339 (16.5%) unmatched ndings showed that almost all were non-nodular structures (91.1%, 51/56), predominantly pleural plaques (46.4%, 26/ 56). Consequently, only ve unmatched discrete solid nodules (1.5%; 95% CI: 0.6–3.5% of 339 persisting ndings) required manual intervention. In conclusion, the pulmonary AI demonstrates robust longitudinal matching performance, with substantial potential for follow-up manual tracking workload reduction.