ArticleJTCVS techniques2026
Deformable lung models for anatomical lung resections: The introduction of simulated reality for imaging guidance.
Article in JTCVS techniques, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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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.
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Who cites it
2 citing papers in PubMed.
- Review
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study introduces PulmoSimulatedReality (Pulmo-SR), a novel technique combining artificial intelligence, finite element method, 3-dimensional (3D) visualization, and 4-dimensional (4D) interaction for preoperative imaging and intraoperative surgical guidance in pulmonary resections, such as lobectomy and segmentectomy. The clinical applicability of this 3D modeling approach is evaluated through a preliminary validation protocol. Methods: A deep learning algorithm was employed to generate 3D segmentations of patient anatomy. 3D models were created for 30 patients undergoing pulmonary resection, and 4D models were developed using the Pulmo-SR platform, incorporating finite element methods for dynamic deformation. Clinical validation was conducted by assessing accuracy, precision, and sensitivity using retrospective intraoperative video recordings alongside dynamic 4D models. Latency and 3D model reconstruction time were also measured. Results: Validation of 30 cases yielded high average scores for accuracy, precision, and sensitivity, respectively: artery (0.987 ± 0.047, 0.993 ± 0.037, and 0.994 ± 0.031), vein (0.976 ± 0.099, 0.976 ± 0.099, and 1.00 ± 0.00), and bronchus (1.00 ± 0.00, 1.00 ± 0.00, and 1.00 ± 0.00). Latency was 0.23 ± 0.06 seconds, and 4D model reconstruction was completed in 8.47 seconds. Conclusions: Pulmo-SR integrates artificial intelligence, finite element method, and 3D modeling to provide a 4D deformable reconstruction of patient anatomy, offering realistic simulations for complex lung resections. Clinical validation demonstrated high accuracy, precision, and sensitivity, indicating the potential as a valuable tool in preoperative and intraoperative workflows for anatomical lung resections.
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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.