Artificial intelligence software in pulmonary nodule assessment

J R Coll Physicians Edinb. 2022 Sep;52(3):228-231. doi: 10.1177/14782715221123856. Epub 2022 Sep 6.


Background: This study tests the impact of the addition of autonomous computed tomography (CT) interpreting software to radiologist assessment of pulmonary nodules.

Methods: Computed tomography scans for nodule assessment were identified retrospectively. Lung cancer risk factors, initial radiologist (RAD) report, Philips Lung Nodule software report (computer-aided nodule (CAD)) and radiologist report following the review of CT images and CAD (RAD + CAD) were collected. Follow-up recommendations based on current guidelines were derived from each report.

Results: In all, 100 patients were studied. Median maximal diameter of the largest nodule reported by RAD and RAD + CAD were similar at 10.0 and 9.0 mm, respectively (p = 0.06) but were reported as larger by CAD at 11.8 mm (p < 0.001). Follow-up recommendations derived from RAD + CAD were less intensive in 23 (23%) and more intensive in 34 (34%) than that of RAD.

Discussion: This study suggests that autonomous software use can alter radiologist assessment of pulmonary nodules such that suggested follow-up is altered.

Keywords: artificial intelligence; computer-assisted; lung cancer; radiographic image interpretation; solitary pulmonary nodule.

MeSH terms

  • Artificial Intelligence
  • Humans
  • Lung Neoplasms* / diagnostic imaging
  • Multiple Pulmonary Nodules* / diagnostic imaging
  • Retrospective Studies
  • Sensitivity and Specificity
  • Software