Augmented Reality and 3-Dimensional Printing Technologies for Guiding Complex Thoracoscopic Surgery
Abstract
Background Virtual 3-dimentional models of the lungs have been used for guiding thoracoscopic procedures including segmentectomy and subsegmentectomy. However, the virtual models displayed on the screen were not tangible. A printed model with assistance of augmented reality (AR) might add value to guide surgical performance. We wanted to investigate whether the combined technology with patient-specific printed models and the AR display could offer better surgical outcomes than the on-screen models in complex thoracoscopic surgery. Methods A total of 142 patients with early lung cancers undertaking thoracoscopic segmentectomy or subsegmentectomy. The 3-dimentional lung models were either displayed on screen (n = 87) or printed out and displayed using AR (n = 55) in the operating room. A propensity score-matching analysis was used to compare the surgical outcomes between the 2 groups. Results The surgical outcomes data before and after propensity score matching showed 3-dimensional printing with AR had a shorter operating time (P = .001 and .001, respectively), less intraoperative blood loss (P = .024 and .006, respectively), and shorter length of hospital stay (P = .001 and .001, respectively) than the on-screen group. The complications and operating success rate (P = .846 and >.999, respectively) and (P = .567 and >.999, respectively) were not significantly different. Surgeons gave a higher score in the tangible group than in the on-screen group (P = .001 vs .001, respectivel…
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Li et al. introduce a strategy that merges 3D printing with augmented-reality visualization; when patient-specific 3D-printed lung models are paired with AR during surgery, spatial orientation improves significantly, contributing to reduced operative duration, decreased blood loss, and shorter hospitalization.
“Li et al. introduce an innovative strategy that merges three-dimensional (3D) printing with augmented-reality (AR) visualization.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [49]
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Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Li, C., Zheng, B., Yu, Q., Yang, B., Liang, C., & Liu, Y.
- Venue
- The Annals of Thoracic Surgery
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 112
- Issue
- 5
- Pages
- 1624-1631
- ISSN
- 0003-4975
- Cited by
- 35
- References
- 0
- Language
- en
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