Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.
CirilloMDMirdellRSjöbergFPhamTD. Time-independent prediction of burn depth using deep convolutional neural networks. J Burn Care Res. 2019;40(6):857-863.
2.
AngulliaFFrightWRRichardsRSchievanoSLinneyADDunawayDJ. A novel RBF-based predictive tool for facial distraction surgery in growing children with syndromic craniosynostosis. Int J Comput Assist Radiol Surg. 2020;15(2):351-367.
3.
FormeisterEJBaumRKnottPD, et al.Machine learning for predicting complications in head and neck microvascular free tissue transfer. Laryngoscope. 2020;130(12):E843-e849.
4.
KuoPJWuSCChienPC, 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(17):13768-13782.
5.
HassanALuSAsaadM, et al.Novel machine learning approach for prediction of hernia recurrence, surgical complications, and 30-day readmission following abdominal wall reconstruction. J Am Coll Surg. 2022;234(5):918-927. Published ahead of print. ISSN 1879-1190/22. DOI: 10.1097/XCS.0000000000000141
6.
HassanATamirisaNSinghPOffodileACButlerCE. A novel support vector machine to predict sentinel lymph node status in elderly patients with breast cancer. J Clin Oncol. 2022. In press.
7.
HassanAMBiaggiAPAsaadM, et al.Development and assessment of machine learning models for individualized risk assessment of mastectomy skin flap necrosis. Ann Surg. 2022. Publish Ahead of Print. doi:10.1097/SLA.0000000000005386
8.
HassanAMB-OAAsaadMMorrisNLiuJSelberJCButlerCE. Artificial intelligence modeling to predict periprosthetic infection and explantation following implant-based reconstruction. Plast Reconstr Surg. 2022. In press.
9.
BertsimasDDunnJVelmahosGCKaafaraniHMA. Surgical risk is not linear: Derivation and validation of a novel, user-friendly, and machine-learning-based predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg. 2018;268(4):574-583.
10.
BihoracAOzrazgat-BaslantiTEbadiA, et al.MySurgeryRisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652-662.
11.
BilimoriaKYLiuYParuchJL, et al.Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833-842.
12.
MantelakisAAssaelYSorooshianPKhajuriaA. Machine learning demonstrates high accuracy for disease diagnosis and prognosis in plastic surgery. Plast Reconstr Surg Glob Open. 2021;9(6):e3638.
13.
NgiamKYKhorIW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262-e273.
14.
LiuYZhouJWuJ, et al.Development and validation of machine learning models to predict epidermal growth factor receptor mutation in non-small cell lung cancer: A multi-center retrospective radiomics study. Cancer Control. 2022;29:10732748221092926.
15.
RyuYHKimSYKimTU, et al.Prediction of poststroke depression based on the outcomes of machine learning algorithms. J Clin Med. 2022;11(8):2264.
16.
ZhangMYinFChenB, et al.Mortality risk after liver transplantation in hepatocellular carcinoma recipients: A nonlinear predictive model. Surgery. 2012;151(6):889-897.
17.
WalczakSVelanovichV. Predicting elective surgical patient outcome destination based on the preoperative modified frailty index and laboratory values. J Surg Res. 2022;275:341-351.
18.
NilssonJOhlssonMThulinLHöglundPNashefSABrandtJ. Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks. J Thorac Cardiovasc Surg. 2006;132(1):12-19.
19.
GerratanaLPiergaJYReubenJM, et al.Modeling the prognostic impact of circulating tumor cells enumeration in metastatic breast cancer for clinical trial design simulation. Oncologist. 2022: oyac045Published ahead of print.
20.
LiCZhangSZhangH, et al.Using the K-nearest neighbor algorithm for the classification of lymph node metastasis in gastric cancer. Comput Math Methods Med. 2012;2012:876545.
ChenYCaoWGaoXOngHJiT. Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model. BMC Med Inf Decis Making. 2015;15:44.
23.
HadayaJVermaASanaihaYRamezaniRQadirNBenharashP. Machine learning-based modeling of acute respiratory failure following emergency general surgery operations. PLoS One. 2022;17(4):e0267733.
24.
JiangHLiuLWangY, et al.Machine learning for the prediction of complications in patients after mitral valve surgery. Front Cardiovasc Med. 2021;8:771246.
25.
WangXQiuJJTanCL, et al.Development and validation of a novel radiomics-based nomogram with machine learning to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Front Oncol. 2022;12:843376.
26.
MoghisiREl MorrCPaceKTHajihaMHuangJ. A machine learning approach to predict the outcome of urinary calculi treatment using shock wave lithotripsy: Model development and validation study. Interact J Med Res. 2022;11(1):e33357.
27.
CeveniniGBarbiniEMassaiMRBarbiniP. A naïve Bayes classifier for planning transfusion requirements in heart surgery. J Eval Clin Pract. 2013;19(1):25-29.
28.
EstevaARobicquetARamsundarB, et al.A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29.
29.
LeCunYBengioYHintonG. Deep learning. Nature. 2015;521(7553):436-444.
30.
HanXFLagaHBennamounM. Image-Based 3D object reconstruction: State-of-the-art and trends in the deep learning era. IEEE Trans Pattern Anal Mach Intell. 2021;43(5):1578-1604.
31.
KhalidSGoldenbergMGrantcharovTTaatiBRudziczF. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open. 2020;3(3):e201664.
32.
RumelhartDEHintonGEWilliamsRJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533-536.
33.
MiottoRWangFWangSJiangXDudleyJT. Deep learning for healthcare: review, opportunities and challenges. Briefings Bioinf. 2018;19(6):1236-1246.
34.
BroschTTamR. Manifold learning of brain MRIs by deep learning. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):633-640.
35.
GulshanVPengLCoramM, et al.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410.
36.
EstevaAKuprelBNovoaRA, et al.Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.
37.
CookNR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem. 2008;54(1):17-23.
38.
MandrekarJN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316.
39.
OrlenkoAMooreJH. A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions. BioData Min. 2021;14(1):9.
40.
ApleyDWZhuJ. Visualizing the effects of predictor variables in black box supervised learning models. J Roy Stat Soc B. 2020;82(4):1059-1086.
