Article ; Online: Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images.
The Journal of international medical research
2024 Volume 52, Issue 2, Page(s) 3000605241230033
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 ... ...
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. |
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MeSH term(s) | Humans ; Artificial Intelligence ; Retrospective Studies ; Lung Neoplasms ; Solitary Pulmonary Nodule ; Algorithms ; Tomography, X-Ray Computed/methods |
Language | English |
Publishing date | 2024-02-06 |
Publishing country | England |
Document type | Observational Study ; Journal Article |
ZDB-ID | 184023-x |
ISSN | 1473-2300 ; 0300-0605 ; 0142-2596 |
ISSN (online) | 1473-2300 |
ISSN | 0300-0605 ; 0142-2596 |
DOI | 10.1177/03000605241230033 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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