Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.

ABSTRACT: Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with electromagnetic and spatial localization technologies. We compared the performance of DL-EMNS and conventional CT-guided methods in percutaneous lung punctures using phantom and animal models. In the phantom study, the DL-EMNS group showed a higher technical success rate (95.6% vs. 77.8%, p = 0.027), smaller error (1.47 ± 1.62 mm vs. 3.98 ± 2.58 mm, p < 0.001), shorter procedure duration (291.56 ± 150.30 vs. 676.44 ± 246.12 s, p < 0.001), and fewer number of CT acquisitions (1.2 ± 0.66 vs. 2.93 ± 0.98, p < 0.001) compared to the traditional CT

SUBMITTER: Peng M 

PROVIDER: S-EPMC11747146 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature