Page 50 - Read Online

Page 138 Villavisanis et al. Art Int Surg. 2025;5:133-38 https://dx.doi.org/10.20517/ais.2024.89

nonsyndromic sagittal craniosynostosis: a stepwise and machine learning algorithm approach. J Craniofac Surg. 2022;33:2333-8. DOI
PubMed
15. Ono S, Hayashi H, Ohi H, Ogawa R. Imaging studies for preoperative planning of perforator flaps: an overview. Clin Plast Surg.
2017;44:21-30. DOI PubMed
16. Nahabedian MY, Momen B, Galdino G, Manson PN. Breast reconstruction with the free TRAM or DIEP flap: patient selection, choice
of flap, and outcome. Plast Reconstr Surg. 2002;110:466-75. DOI PubMed
17. Cevik J, Seth I, Hunter-Smith DJ, Rozen WM. A history of innovation: tracing the evolution of imaging modalities for the preoperative
planning of microsurgical breast reconstruction. J Clin Med. 2023;12:5246. DOI PubMed PMC
18. Smit JM, Klein S, Werker PM. An overview of methods for vascular mapping in the planning of free flaps. J Plast Reconstr Aesthet
Surg. 2010;63:e674-82. DOI PubMed
19. Thimmappa ND. MRA for preoperative planning and postoperative management of perforator flap surgeries: a review. J Magn Reson
Imaging. 2024;59:797-811. DOI PubMed
20. Lim B, Cevik J, Seth I, et al. Evaluating artificial intelligence’s role in teaching the reporting and interpretation of computed
tomographic angiography for preoperative planning of the deep inferior epigastric artery perforator flap. JPRAS Open. 2024;40:273-
85. DOI PubMed PMC
21. Bassani S, Eccher A, Molteni G. Harnessing the power of artificial intelligence: revolutionizing free flaps monitoring in head and neck
tumor treatment. Crit Rev Oncog. 2023;28:25-30. DOI PubMed
22. Asaad M, Lu SC, Hassan AM, et al. The use of machine learning for predicting complications of free-flap head and neck
reconstruction. Ann Surg Oncol. 2023;30:2343-52. DOI PubMed
23. Formeister EJ, Baum R, Knott PD, et al. Machine learning for predicting complications in head and neck microvascular free tissue
transfer. Laryngoscope. 2020;130:E843-9. DOI PubMed
24. Moosa S, Dydynsky R. The role of artificial intelligence in predicting flap outcomes in plastic surgery: protocol of a systematic
review. URNCST J. 2022;6:1-8. DOI
25. Li K, Zhang Z, Nicoli F, et al. Application of indocyanine green in flap surgery: a systematic review. J Reconstr Microsurg.
2018;34:77-86. DOI PubMed
26. Singaravelu A, Dalli J, Potter S, Cahill RA. Artificial intelligence for optimum tissue excision with indocyanine green fluorescence
angiography for flap reconstructions: proof of concept. JPRAS Open. 2024;41:389-93. DOI PubMed PMC
27. Atkinson CJ, Seth I, Xie Y, et al. Artificial intelligence language model performance for rapid intraoperative queries in plastic surgery:
ChatGPT and the deep inferior epigastric perforator flap. J Clin Med. 2024;13:900. DOI PubMed PMC
28. Battaglia S, Badiali G, Cercenelli L, et al. Combination of CAD/CAM and augmented reality in free fibula bone harvest. Plast
Reconstr Surg Glob Open. 2019;7:e2510. DOI PubMed PMC
29. Cercenelli L, Babini F, Badiali G, et al. Augmented reality to assist skin paddle harvesting in osteomyocutaneous fibular flap
reconstructive surgery: a pilot evaluation on a 3D-printed leg phantom. Front Oncol. 2021;11:804748. DOI PubMed PMC
30. Tang Y, Guo Q, Li X, Huang Y, Kuang W, Luo L. Augmented reality-assisted systematic mapping of anterolateral thigh perforators.
BMC Musculoskelet Disord. 2022;23:1047. DOI PubMed PMC
31. Falola RA, Lombana NF, Rodriguez-Unda NA, et al. Augmented reality microsurgery: proof of concept for a novel approach to
microsurgical field visualization in plastic surgery. Plast Reconstr Surg. 2024;153:650e-5. DOI PubMed
32. Selber JC, Angel Soto-Miranda M, Liu J, Robb G. The survival curve: factors impacting the outcome of free flap take-backs. Plast
Reconstr Surg. 2012;130:105-13. DOI PubMed
33. Kim J, Lee SM, Kim DE, et al. Development of an automated free flap monitoring system based on artificial intelligence. JAMA Netw
Open. 2024;7:e2424299. DOI PubMed PMC
34. O’Neill AC, Yang D, Roy M, Sebastiampillai S, Hofer SOP, Xu W. Development and evaluation of a machine learning prediction
model for flap failure in microvascular breast reconstruction. Ann Surg Oncol. 2020;27:3466-75. DOI PubMed
35. Kuo P, Wu S, Chien P, et al. Artificial neural network approach to predict surgical site infection after free-flap reconstruction in
patients receiving surgery for head and neck cancer. Oncotarget. 2018;9:13768-82. DOI PubMed PMC