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Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies

Sadeghi, Amir H., Maat, Alexander P.W. M., Taverne, Yannick J.H. J., Cornelissen, Robin, Dingemans, Anne-Marie C., Bogers, Ad J.J. C., Mahtab, Edris A.F.·2021·JTCVS Techniques
DOI 10.1016/j.xjtc.2021.03.016PMC PMC8312141
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  • Together, these studies illustrate a landscape in which AI, AR, and VR increasingly intersect to refine surgical workflows and elevate clinical performance.

    “Together, these studies illustrate a landscape in which AI, AR, and VR increasingly intersect to refine surgical workflows and elevate clinical performance [51].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [51]

    The Conclusions report that an AI-and-VR planning tool was clinically applicable in 10 segmentectomy patients and changed surgical strategy in 40% of cases. The Future Outlook discusses AR only as a potential application, while the Conclusions say larger trials are needed to establish clinical benefits. This supports an intersection of AI and VR in surgical planning, but not a demonstrated improvement in clinical performance across AI, AR, and VR. (LLM, conf=0.87)

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Research works printed but not linked: 18

  1. [1]Yang C.F. D'Amico T.A. Thoracoscopic segmentectomy for lung cancer Ann Thorac Surg 94 2012 668 681 22748648
  2. [2]Ghaly G. Kamel M. Nasar A. Paul S. Lee P.C. Port J.L. Video-assisted thoracoscopic surgery is a safe and effective alternative to thoracotomy for anatomical segmentectomy in patients with clinical stage I non–small cell lung cancer Ann Thorac Surg 101 2016 465 472 26391692
  3. [3]Marchand P. Gilroy J.C. Wilson V.H. An anatomical study of the bronchial vascular system and its variations in disease Thorax 5 1950 207 221 14776713 PMC1018335
  4. [4]Cory R.A. Valentine E.J. Varying patterns of the lobar branches of the pulmonary artery. A study of 524 lungs and lobes seen at operation of 426 patients Thorax 14 1959 267 280 13812149 PMC1018514
  5. [5]Oizumi H. Kanauchi N. Kato H. Endoh M. Suzuki J. Fukaya K. Anatomic thoracoscopic pulmonary segmentectomy under 3-dimensional multidetector computed tomography simulation: a report of 52 consecutive cases J Thorac Cardiovasc Surg 141 2011 678 682 20884021
  6. [6]Sardari Nia P. Olsthoorn J.R. Heuts S. Maessen J.G. Interactive 3D reconstruction of pulmonary anatomy for preoperative planning, virtual simulation, and intraoperative guiding in video-assisted thoracoscopic lung surgery Innovations 14 2019 17 26 30848710
  7. [7]Sadeghi A.H. Taverne Y. Bogers A. Mahtab E.A.F. Immersive virtual reality surgical planning of minimally invasive coronary artery bypass for Kawasaki disease Eur Heart J 41 2020 3279 32637982 PMC7557451
  8. [8]Shirk J.D. Thiel D.D. Wallen E.M. Linehan J.M. White W.M. Badani K.K. Effect of 3-dimensional virtual reality models for surgical planning of robotic-assisted partial nephrectomy on surgical outcomes: a randomized clinical trial JAMA Netw Open 2 2019 e1911598 31532520 PMC6751754
  9. [10]Sadeghi A. Mathari S. Abjigitova D. Maat A.P.W.M. Taverne Y.J.H.J. Bogers A.J.J.C. Current and future applications of virtual, augmented, and mixed reality in cardiothoracic surgery Ann Thorac Surg December 18, 2020 [Epub ahead of print] 10.1016/j.athoracsur.2020.11.030 33347848 · DOI
  10. [11]Lassen-Schmidt B.C. Kuhnigk J.M. Konrad O. van Ginneken B. van Rikxoort E.M. Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data Phys Med Biol 62 2017 6649 6665 28570264
  11. [12]Fischer A.M. Varga-Szemes A. Martin S.S. Sperl J.I. Sahbaee P. Neumann D. Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers J Thorac Imaging 35 Suppl 1 2020 S28 S34 32235188
  12. [13]Sadeghi A.H. Bakhuis W. Van Schaagen F. Oei F.B. Bekkers J.A. Maat A.P. Immersive 3D virtual reality imaging in planning minimally invasive and complex adult cardiac surgery Eur Heart J Digital Health 1 2020 62 70 PMC9708043 36713960
  13. [14]Iizuka S. Kuroda H. Yoshimura K. Dejima H. Seto K. Naomi A. Predictors of indocyanine green visualization during fluorescence imaging for segmental plane formation in thoracoscopic anatomical segmentectomy J Thorac Dis 8 2016 985 991 27162675 PMC4842821
  14. [15]Yang Q. Xie B. Hu M. Sun X. Huang X. Guo M. Thoracoscopic anatomic pulmonary segmentectomy: a 3-dimensional guided imaging system for lung operations Interact Cardiovasc Thorac Surg 23 2016 183 189 27099268
  15. [16]Seguin-Givelet A. Grigoroiu M. Brian E. Gossot D. Planning and marking for thoracoscopic anatomical segmentectomies J Thorac Dis 10 Suppl 10 2019 S1187 S1194 PMC5949389 29785293
  16. [17]Saji H. Inoue T. Kato Y. Shimada Y. Hagiwara M. Kudo Y. Virtual segmentectomy based on high-quality three-dimensional lung modelling from computed tomography Interact Cardiovasc Thorac Surg 17 2013 227 232 23624984 PMC3715161
  17. [18]Le Moal J. Peillon C. Dacher J.N. Baste J.M. Three-dimensional computed tomography reconstruction for operative planning in robotic segmentectomy: a pilot study J Thorac Dis 10 2018 196 201 29600049 PMC5863115
  18. [19]Rouze S. de Latour B. Flecher E. Guihaire J. Castro M. Corre R. Small pulmonary nodule localization with cone beam computed tomography during video-assisted thoracic surgery: a feasibility study Interact Cardiovasc Thorac Surg 22 2016 705 711 26921886

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Authors
Sadeghi, A. H., Maat, A. P. W. M., Taverne, Y. J. H. J., Cornelissen, R., Dingemans, A.-M. C., Bogers, A. J. J. C., & Mahtab, E. A. F.
Venue
JTCVS Techniques
Publisher
Elsevier BV
Published
2021-06
Volume
7
Pages
309-321
ISSN
2666-2507
DOI
10.1016/j.xjtc.2021.03.016
PMC
PMC8312141
Cited by
73
References
0
Language
en
Licence
Funders
Erasmus Medisch Centrum

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