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...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85209-6 |
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| author | Muyun Peng Xinyi Fan Qikang Hu Xilong Mei Bin Wang Zeyu Wu Huali Hu Lei Tang Xinhang Hu Yanyi Yang Chunxia Qin Huajie Zhang Qun Liu Xiaofeng Chen Fenglei Yu |
| author_facet | Muyun Peng Xinyi Fan Qikang Hu Xilong Mei Bin Wang Zeyu Wu Huali Hu Lei Tang Xinhang Hu Yanyi Yang Chunxia Qin Huajie Zhang Qun Liu Xiaofeng Chen Fenglei Yu |
| author_sort | Muyun Peng |
| collection | DOAJ |
| description | 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-guided group. In the animal study, DL-EMNS significantly improved technical success rate (100% vs. 84.0%, p = 0.015), reduced operation time (121.36 ± 38.87 s vs. 321.60 ± 129.12 s, p < 0.001), number of CT acquisitions (1.09 ± 0.29 vs. 2.96 ± 0.73, p < 0.001) and complication rate (0% vs. 20%, p = 0.002). In conclusion, with the assistance of DL-EMNS, the operators got better performance in the percutaneous puncture of small pulmonary nodules. |
| format | Article |
| id | doaj-art-168f914465554b9faa5a8fa6f3964b7c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-168f914465554b9faa5a8fa6f3964b7c2025-01-26T12:33:03ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-85209-6Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodulesMuyun Peng0Xinyi Fan1Qikang Hu2Xilong Mei3Bin Wang4Zeyu Wu5Huali Hu6Lei Tang7Xinhang Hu8Yanyi Yang9Chunxia Qin10Huajie Zhang11Qun Liu12Xiaofeng Chen13Fenglei Yu14Department of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityInfervision Medical Technology Co., Ltd.Department of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityDepartment of Radiology, The Second Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, Hunan Rehabilitation HospitalDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityHeath Management Center, The Second Xiangya Hospital, Central South UniversityInfervision Medical Technology Co., Ltd.Infervision Medical Technology Co., Ltd.Infervision Medical Technology Co., Ltd.Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South UniversityDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South UniversityAbstract 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-guided group. In the animal study, DL-EMNS significantly improved technical success rate (100% vs. 84.0%, p = 0.015), reduced operation time (121.36 ± 38.87 s vs. 321.60 ± 129.12 s, p < 0.001), number of CT acquisitions (1.09 ± 0.29 vs. 2.96 ± 0.73, p < 0.001) and complication rate (0% vs. 20%, p = 0.002). In conclusion, with the assistance of DL-EMNS, the operators got better performance in the percutaneous puncture of small pulmonary nodules.https://doi.org/10.1038/s41598-025-85209-6Pulmonary nodulesDeep learningElectromagnetic navigationPercutaneous punctureSafety |
| spellingShingle | Muyun Peng Xinyi Fan Qikang Hu Xilong Mei Bin Wang Zeyu Wu Huali Hu Lei Tang Xinhang Hu Yanyi Yang Chunxia Qin Huajie Zhang Qun Liu Xiaofeng Chen Fenglei Yu Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules Scientific Reports Pulmonary nodules Deep learning Electromagnetic navigation Percutaneous puncture Safety |
| title | Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| title_full | Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| title_fullStr | Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| title_full_unstemmed | Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| title_short | Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| title_sort | deep learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules |
| topic | Pulmonary nodules Deep learning Electromagnetic navigation Percutaneous puncture Safety |
| url | https://doi.org/10.1038/s41598-025-85209-6 |
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