Evidence map›Paper›PMID 39833245›Full record

ArticleScientific reports2025

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

Muyun Peng, Xinyi Fan, Qikang Hu, Xilong Mei, Bin Wang, Zeyu Wu, Huali Hu, Lei Tang, Xinhang Hu, Yanyi Yang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Muyun Peng *Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Xinyi Fan *Infervision Medical Technology Co., Ltd., Beijing, China.
Qikang HuDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Xilong MeiDepartment of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.
Bin WangDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Zeyu WuDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Huali HuDepartment of Thoracic Surgery, Hunan Rehabilitation Hospital, Changsha, China.
Lei TangDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Xinhang HuDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China.
Yanyi YangHeath Management Center, The Second Xiangya Hospital, Central South University, Changsha, China.
Chunxia QinInfervision Medical Technology Co., Ltd., Beijing, China.
Huajie ZhangInfervision Medical Technology Co., Ltd., Beijing, China.
Qun LiuInfervision Medical Technology Co., Ltd., Beijing, China.
Xiaofeng ChenHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, China. chenxiaofeng@csu.edu.cn.
Fenglei YuDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China. yufenglei@csu.edu.cn.

Funding

Department of Science and Technology of Hunan Province 2021SK53520Department of Science and Technology of Hunan Province 2023SK2034Health Commission of Hunan Province B2021-04
6 · The paper itself

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.

Indexed as

Deep LearningLung NeoplasmsMultiple Pulmonary NodulesPuncturesSolitary Pulmonary NoduleAnimalsElectromagnetic PhenomenaHumansMalePhantoms, ImagingTomography, X-Ray ComputedDeep learningElectromagnetic navigationPercutaneous puncturePulmonary nodulesSafety

Identifiers

PMID39833245
PMCPMC11747146

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.