ObjectiveTo analyze the difference of location identification of pulmonary nodules in two dimensional (2D) and three dimensional (3D) images, and to discuss the identification methods and clinical significance of pulmonary nodules location in 3D space.MethodsThe clinical data of 105 patients undergoing sublobectomy in the Department of Thoracic Surgery, the First Affiliated Hospital with Nanjing Medical University from December 2018 to December 2019 were analyzed retrospectively. There were 28 males and 77 females, with an average age of 57.21±13.19 years. The nodule location was determined by traditional 2D method and 3D depth ratio method respectively, and the differences were compared.ResultsA total of 30 nodules had different position identification between the two methods, among which 25 nodules in the inner or middle zone of 2D image were located in the peripheral region of 3D image. The overall differences between the two methods were statistically significant (P<0.05). The diagnostic consistency rates of two methods were 66.67% in the right upper lung, 83.33% in the right middle lung, 73.68% in the right lower lung, 75.76% in the left upper lung, and 64.71% in the left lower lung. In each lung lobe, the difference between the two methods in the right upper lung (P=0.014) and the left upper lung (P=0.019) was statistically significant, while in the right middle lung (P=1.000), right lower lung (P=0.460) and left lower lung (P=0.162) were not statistically significant.ConclusionThe 3D position definition of lung nodules based on depth ratio is more accurate than the traditional 2D definition, which is helpful for preoperative planning of sublobectomy.
ObjectiveTo analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three dimensional (3D) reconstruction for thoracoscopic anatomical pulmonary segmentectomy. MethodsA retrospective analysis was conducted on patients who underwent thoracoscopic pulmonary segmentectomy at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI 3D reconstruction were included in the AI group, those who underwent Mimics 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. ResultsA total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistical differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, or postoperative complications among the three groups (P>0.05). There were statistical differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistical differences in operation time, extubation time, bleeding volume, or postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistical difference in drainage volume between the AI group and the control group (P>0.05), while there were statistical differences in operation time, drainage tube retention time, bleeding volume, and postoperative hospital stay (P<0.05). ConclusionFor patients requiring thoracoscopic anatomical pulmonary segmentectomy, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.