In recent years, lung function assessment has attracted increasing attention in the perioperative management of thoracic surgery. However, traditional pulmonary function testing methods remain limited in clinical practice due to high equipment requirements and complex procedures. With the rapid development of artificial intelligence (AI) technology, lung function assessment based on multimodal chest imaging (such as X-rays, CT, and MRI) has become a new research focus. Through deep learning algorithms, AI models can accurately extract imaging features of patients and have made significant progress in quantitative analysis of pulmonary ventilation, evaluation of diffusion capacity, measurement of lung volumes, and prediction of lung function decline. Previous studies have demonstrated that AI models perform well in predicting key indicators such as forced expiratory volume in one second, diffusing capacity for carbon monoxide, and total lung capacity. Despite these promising prospects, challenges remain in clinical translation, including insufficient data standardization, limited model interpretability, and the lack of prediction models for postoperative complications. In the future, greater emphasis should be placed on multicenter collaboration, the construction of high-quality databases, the promotion of multimodal data integration, and clinical validation to further enhance the application value of AI technology in precision decision-making for thoracic surgery.

In recent years, lung function assessment has attracted increasing attention in the perioperative management of thoracic surgery. However, traditional pulmonary function testing methods remain limited in clinical practice due to high equipment requirements and complex procedures. With the rapid development of artificial intelligence (AI) technology, lung function assessment based on multimodal chest imaging (such as X-rays, CT, and MRI) has become a new research focus. Through deep learning algorithms, AI models can accurately extract imaging features of patients and have made significant progress in quantitative analysis of pulmonary ventilation, evaluation of diffusion capacity, measurement of lung volumes, and prediction of lung function decline. Previous studies have demonstrated that AI models perform well in predicting key indicators such as forced expiratory volume in one second, diffusing capacity for carbon monoxide, and total lung capacity. Despite these promising prospects, challenges remain in clinical translation, including insufficient data standardization, limited model interpretability, and the lack of prediction models for postoperative complications. In the future, greater emphasis should be placed on multicenter collaboration, the construction of high-quality databases, the promotion of multimodal data integration, and clinical validation to further enhance the application value of AI technology in precision decision-making for thoracic surgery.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.