The application of robots in thoracic surgery is mainly based on the da Vinci general surgery robot. With the popularization of artificial intelligence (AI) application scenarios, the combination of AI and robots is more closely, and there is a strong clinical demand and huge application space for the development of specialized disease-specific robotic systems for thoracic surgery. This article aims to systematically describe the history of the rise of specialized surgical robots and the status of the localization of surgical robots in China, propose the concept of applying AI to the research and development of integrated specialized disease-specific robots in thoracic surgery, and clarify the ethics and prospects that intelligent specialized disease-specific surgical robots will face.
ObjectiveBy integrating biological assays with imaging evaluations, a clinical prediction model is developed based on a cohort of ten thousand individuals to enhance the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of 1,017 patients with pulmonary nodules who underwent chest CT and testing for seven types of lung cancer-related serum autoantibodies (7-AABs) at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to April 2024, all of whom had definitive pathological diagnosis results. Statistical analysis was performed using R and MSTATA software, with the development of univariate and multivariate logistic regression models, as well as a nomogram model. The performance of the models was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1,017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58.12±12.41) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (57.99±12.56) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest AUC value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis included age, imaging nodule diameter, nodule characteristics, and the combination of CT and 7-AABs testing as independent predictive factors to construct a nomogram prediction model. The AUC values for this model were 0.831 and 0.861 in the training and validation sets, respectively, demonstrating excellent performance in decision curve analysis (DCA). ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.