Evidence map›Paper›PMID 42714956›Full record

Observational studyJMIR formative research2026

Real-Time AI-Augmented Fluoroscopic Navigation for Intraoperative Pulmonary Nodule Localization: Prospective Observational Pilot Study.

Hsiang Teng, Hsu-Kai Huang, Cheng-Jung Lin, Ying-Shian Chen, Yueh-Hsun Tsai, Tsai-Wang Huang, Kuan-Hsun Lin

Registry-linked trialAbstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07682012 (Clinical Feasibility, Localization Accuracy, and Safety of the LungVision System for Intraoperative Localization of Small Pulmonary Nodules During Thoracoscopic Surgery), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT07682012 nacompletednot on this map

Clinical Feasibility, Localization Accuracy, and Safety of the LungVision System for Intraoperative Localization of Small Pulmonary Nodules During Thoracoscopic Surgery: A Prospective Single-Center Pilot Study

TypeinterventionalSponsorTri-Service General Hospital (TSGH)Ran2024 to 2024Enrolled14ConditionsLung Neoplasms, Pulmonary Nodule, Solitary, Carcinoma, Non-Small-Cell LungArmsLungVision System, Preoperative Dual Localization
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Hsiang TengDivision of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0009-0007-7066-5779
Hsu-Kai HuangDivision of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-4358-4822
Cheng-Jung LinDepartment of Thoracic Surgery, School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0009-0007-3874-7782
Ying-Shian ChenDepartment of Thoracic Surgery, School of Medicine, College of Medicine, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-0442-1086
Yueh-Hsun TsaiDivision of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0003-2918-4408
Tsai-Wang HuangDivision of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0001-8741-9223
Kuan-Hsun LinDivision of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-3371-5489

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of AI into intraoperative surgical imaging represents an emerging frontier in digital health. Despite advances in preoperative computed tomography (CT)-based surgical planning, real-time translation of imaging data into actionable intraoperative guidance remains limited by CT-to-body divergence-a fundamental information gap between preoperative digital models and the dynamic surgical field. This divergence, driven by lung deflation under anesthesia and positional changes, represents a critical digital-to-physical registration challenge that current preoperative imaging workflows fail to address in real time.

objectiveThis study aimed to evaluate the clinical feasibility, localization success, and safety of the LungVision system-an AI-augmented fluoroscopic navigation platform-for real-time intraoperative localization of small pulmonary nodules during thoracoscopic surgery.

methodsA prospective single-center study enrolled 14 patients with pulmonary nodules requiring localization prior to thoracoscopic resection between January 2024 and December 2024. The platform comprises a passive radiopaque positioning board, an AI-powered computing unit for real-time image processing, and a tablet-based interface for procedural planning and augmented visualization. All patients received dual localization with either preoperative CT-guided dye injection or Archimedes virtual bronchoscopic navigation followed by intraoperative localization with the LungVision system and video-assisted thoracoscopic surgery. Demographic data, lesion characteristics, procedural performance, and procedure-related complications were recorded.

resultsThe mean patient age was 57.2 (SD 9.2) years, and 92.9% (13/14) were nonsmokers. Most nodules were peripherally located (12/14, 85.7%), with a mean diameter of 9.3 (SD 5.3) mm and a mean CT attenuation of -320.1 (SD 334.9) Hounsfield units. LungVision successfully localized all target lesions intraoperatively, with a mean navigation time of 38.6 (SD 19.5) minutes. Complete resection was achieved in all cases, and 71.4% (10/14) of nodules were pathologically malignant. No intraoperative or localization-related complications were observed. The system was integrated into the existing operating room without additional infrastructure modifications.

conclusionsIn this prospective study, the LungVision system achieved successful intraoperative localization of small, hypodense pulmonary nodules using a bronchoscopic approach integrated with conventional C-arm fluoroscopy. These findings support the feasibility of the technique and provide preliminary evidence on its use in thoracoscopic resection workflows. Larger studies are needed to further evaluate its clinical performance and implementation in diverse practice settings.

trial registrationClinicalTrials.gov NCT07682012; https://clinicaltrials.gov/study/NCT07682012.

Indexed as

Artificial IntelligenceLung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleSurgery, Computer-AssistedAgedFemaleFluoroscopyHumansMaleMiddle AgedPilot ProjectsProspective StudiesThoracic Surgery, Video-AssistedTomography, X-Ray ComputedAIartificial intelligenceaugmented fluoroscopycomputer-assisted surgerydigital surgical planningintraoperative navigationpulmonary nodule localization

Identifiers

PMID42714956
PMCPMC13601874

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Registered trials

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.