Table 3. Artificial Intelligence studies related to preoperative evaluation in thoracic surgery.

Author	Objective	AI algorithm	Application	Main results
Esteva H, et al.	Assessment of surgical risk in patients undergoing pulmonary resection	Neural network	Prediction of postoperative outcomes in lung resections	NN can integrate results from multiple data predicting the individual outcome for patients, rather than assigning them to less-precise risk group categories
Santos-Garcia G, et al.	To propose an ensemble model of ANNs to predict cardio-respiratory morbidity after pulmonary resection for NSCLC	Artificial neural network	Prediction of cardio-respiratory morbidity after pulmonary resection for NSCLC	In this series an ANN ensemble offered a high performance to predict postoperative cardio-respiratory morbidity
Bolourani S, et al.	To identify risk factors for respiratory failure after pulmonary lobectomy	Random forest	Predicting of respiratory failure after pulmonary lobectomy	Two ML-based prediction models were generated and optimized. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making
Salati M, et al.	To verify if the application of an AI analysis could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection	Extreme gradient boosting	Prediction of cardiopulmonary complications after lung resection	XGBOOST algorithm generated a model able to predict complications with an area under the curve of 0.75
Chang YJ, et al.	To construct a prediction model with seven supervised ML algorithms to predict whether patients could be weaned immediately after lung resection surgery	Multiple ML algorithms	Prediction of staged weaning from ventilator after lung resection surgery	The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics

ML, machine learning; NN, neural networks; ANNs, artificial neural networks; NSCLC, non-small cell lung cancer; AI, artificial intelligence.