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.