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TRV-2026-0933Certified recordPeer-reviewed

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 artificial 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 NELSO…

Health · The Trace — both readings · certified 2026-08-31 · v1 · article view · machine-readable

Current reading — gain

Automated pulmonary AI matched persisting lung nodules across 3-month LDCT scans with 83.5% success, reaching 91.8% for single-nodule cases and leaving only 1.5% of persisting findings needing manual correction, indicating potential to reduce manual tracking workload.

Current reading — problem

Automated matching failed for 16.5% of persisting findings and performance fell to 72.8% in participants with more than five nodules, with prospective validation in diverse populations still needed.

What this doesn’t fix

Performance drops with high nodule burden and generalizability remains unproven, requiring prospective validation in diverse populations.

Evidence

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Truvace Impact Record TRV-2026-0933, v1: “Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.” Truvace, 2026-08-31. /record/TRV-2026-0933 (accessed at citation time). sha256 a19eea96d6ee870c…

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