Artificial intelligence applications in free flap microvascular reconstruction: preoperative planning, intraoperative assessment, and postoperative monitoring

Abstract

Reliable planning, execution, and postoperative monitoring in microvascular free flap reconstruction are essential to optimize clinical outcomes. Artificial intelligence has demonstrated value in several applications to clinical medicine and surgery, including image analysis and simulation, outcomes modeling, and evaluation of large datasets. Within microvascular reconstruction, artificial intelligence has been increasingly applied to preoperative planning, intraoperative decision making, and postoperative monitoring. The present paper aims to review salient applications to each. The authors conclude by suggesting areas suitable for future analysis.

Keywords

Artificial intelligence / free flap / microvascular reconstruction / anastomosis

References

[1] Hirigoyen MB,Weinberg H.Free flap monitoring: a review of current practice.Microsurgery1995;16:723-6

[2] Salgado CJ,Mardini S.Flap monitoring and patient management.Plast Reconstr Surg2009;124:e295-302

[3] Hidalgo DA,Cordeiro PG.A review of 716 consecutive free flaps for oncologic surgical defects: refinement in donor-site selection and technique.Plast Reconstr Surg1998;102:722-32

[4] Haddock NT,Teotia SS.Categorizing patient selection, outcomes, and indications in a decade of 405 profunda artery perforator flaps.Plast Reconstr Surg2024;154:632e-40

[5] Ramesh AN,Monson JR.Artificial intelligence in medicine.Ann R Coll Surg Engl2004;86:334-8 PMCID:PMC1964229

[6] Novakovsky G,Libbrecht MW,Mostafavi S.Obtaining genetics insights from deep learning via explainable artificial intelligence.Nat Rev Genet2023;24:125-37

[7] Johnson KW,Glicksberg BS.Artificial intelligence in cardiology.J Am Coll Cardiol2018;71:2668-79

[8] Topalovic M, Das N, Burgel PR, et al; Pulmonary Function Study Investigators; Pulmonary Function Study Investigators. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019;53:1801660.

[9] Kurmis AP.Artificial intelligence in orthopedic surgery: evolution, current state and future directions.Arthroplasty2022;4:9 PMCID:PMC8889658

[10] Lareyre F,Carrier M,Raffort J.Artificial intelligence for education of vascular surgeons.Eur J Vasc Endovasc Surg2020;59:870-1

[11] Kwon DY,Oleru O.Implications for the use of artificial intelligence in plastic surgery research and practice.Plast Reconstr Surg2024;153:862e-3 PMCID:PMC10961169

[12] Blum JD,Villavisanis DF.Machine learning in metopic craniosynostosis: does phenotypic severity predict long-term esthetic outcome?.J Craniofac Surg2023;34:58-64 PMCID:PMC9825625

[13] Villavisanis DF,Zapatero ZD.Craniofacial soft-tissue anthropomorphic database with magnetic resonance imaging and unbiased diffeomorphic registration.Plast Reconstr Surg2024;153:667-77

[14] Villavisanis DF,Zhao C.Predicting changes in cephalic index following spring-mediated cranioplasty for nonsyndromic sagittal craniosynostosis: a stepwise and machine learning algorithm approach.J Craniofac Surg2022;33:2333-8

[15] Ono S,Ohi H.Imaging studies for preoperative planning of perforator flaps: an overview.Clin Plast Surg2017;44:21-30

[16] Nahabedian MY,Galdino G.Breast reconstruction with the free TRAM or DIEP flap: patient selection, choice of flap, and outcome.Plast Reconstr Surg2002;110:466-75

[17] Cevik J,Hunter-Smith DJ.A history of innovation: tracing the evolution of imaging modalities for the preoperative planning of microsurgical breast reconstruction.J Clin Med2023;12:5246 PMCID:PMC10455834

[18] Smit JM,Werker PM.An overview of methods for vascular mapping in the planning of free flaps.J Plast Reconstr Aesthet Surg2010;63:e674-82

[19] Thimmappa ND.MRA for preoperative planning and postoperative management of perforator flap surgeries: a review.J Magn Reson Imaging2024;59:797-811

[20] Lim B,Seth I.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 Open2024;40:273-85 PMCID:PMC11067004

[21] Bassani S,Molteni G.Harnessing the power of artificial intelligence: revolutionizing free flaps monitoring in head and neck tumor treatment.Crit Rev Oncog2023;28:25-30

[22] Asaad M,Hassan AM.The use of machine learning for predicting complications of free-flap head and neck reconstruction.Ann Surg Oncol2023;30:2343-52

[23] Formeister EJ,Knott PD.Machine learning for predicting complications in head and neck microvascular free tissue transfer.Laryngoscope2020;130:E843-9

[24] Moosa S.The role of artificial intelligence in predicting flap outcomes in plastic surgery: protocol of a systematic review.URNCST J2022;6:1-8

[25] Li K,Nicoli F.Application of indocyanine green in flap surgery: a systematic review.J Reconstr Microsurg2018;34:77-86

[26] Singaravelu A,Potter S.Artificial intelligence for optimum tissue excision with indocyanine green fluorescence angiography for flap reconstructions: proof of concept.JPRAS Open2024;41:389-93 PMCID:PMC11381603

[27] Atkinson CJ,Xie Y.Artificial intelligence language model performance for rapid intraoperative queries in plastic surgery: ChatGPT and the deep inferior epigastric perforator flap.J Clin Med2024;13:900 PMCID:PMC10856538

[28] Battaglia S,Cercenelli L.Combination of CAD/CAM and augmented reality in free fibula bone harvest.Plast Reconstr Surg Glob Open2019;7:e2510 PMCID:PMC6908345

[29] Cercenelli L,Badiali G.Augmented reality to assist skin paddle harvesting in osteomyocutaneous fibular flap reconstructive surgery: a pilot evaluation on a 3D-printed leg phantom.Front Oncol2021;11:804748 PMCID:PMC8770836

[30] Tang Y,Li X,Kuang W.Augmented reality-assisted systematic mapping of anterolateral thigh perforators.BMC Musculoskelet Disord2022;23:1047 PMCID:PMC9716696

[31] Falola RA,Rodriguez-Unda NA.Augmented reality microsurgery: proof of concept for a novel approach to microsurgical field visualization in plastic surgery.Plast Reconstr Surg2024;153:650e-5

[32] Selber JC,Liu J.The survival curve: factors impacting the outcome of free flap take-backs.Plast Reconstr Surg2012;130:105-13

[33] Kim J,Kim DE.Development of an automated free flap monitoring system based on artificial intelligence.JAMA Netw Open2024;7:e2424299 PMCID:PMC11282448

[34] O’Neill AC,Roy M,Hofer SOP.Development and evaluation of a machine learning prediction model for flap failure in microvascular breast reconstruction.Ann Surg Oncol2020;27:3466-75

[35] Kuo P,Chien P.Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.Oncotarget2018;9:13768-82 PMCID:PMC5862614