Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images

Abstract

Objectives: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method. Methods: For this retrospective, observational study, patients with pulmonary nodules 5–30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a ‘correct’ cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking. An AI algorithm was applied in which a U-net type neural network model was trained to predict the deformation vector field between two examinations. When the estimated position was within a defined cuboid, the AI algorithm was judged a success. Results: In total, 49 lung nodules in 40 patients, with a total of 368 follow-up CT examinations were examined. The success rate for each time evaluation was 94% (345/368) and for ‘nodule-by-nodule evaluation’ was 78% (38/49). Reasons for a decrease in success rate were related to small nodules and those that decreased in size. Conclusion: Automatic tracking of lung nodules is highly feasible.