![]() 2019 8:929–937.Source: Smartkarma Blog Smartkarma Blog LinkDoc Technology IPO: Valuation Insights LinkDoc (LDOC US) is a Chinese healthtech company. Three-dimensional printing in the preoperative planning of thoracoscopic pulmonary segmentectomy. 3D printed guides and preoperative planning for uncemented stem anteversion reconstruction during hip arthroplasty: a pilot study. Zhang Y., Rao Z., Zhang J., Li S., Chang S., Sun Y. A novel 3D hepatectomy simulation based on liver circulation: application to liver resection and transplantation. Interactive 3D reconstruction of pulmonary anatomy for preoperative planning, virtual simulation, and intraoperative guiding in video-assisted thoracoscopic lung surgery. Sardari Nia P., Olsthoorn J.R., Heuts S., Maessen J.G. Planning of segmentectomy using three-dimensional computed tomography angiography with a virtual safety margin: technique and initial experience. Iwano S., Yokoi K., Taniguchi T., Kawaguchi K., Fukui T., Naganawa S. L222020) and other sources.Īccuracy Anatomy Artificial intelligence Efficiency Safety Three-dimensional reconstruction model.Ĭopyright © 2022 The Authors. This study was funded by the Beijing Natural Science Foundation (No. Constant optimization and larger population validation are required. ![]() The AI system can accurately predict thoracic anatomical structures with higher efficiency than manual reconstruction software. Compared to Mimics®, the AI system reduced the model reconstruction time by 14.2 min (p < 0.001), and it also outperformed Mimics® in model quality scores (p < 0.001). With the assistance of the AI system, the operation time was shortened by 24.5 min for lobectomy (p < 0.001) and 20 min for segmentectomy (p = 0.007). The AI system reconstructed 13,608 pulmonary segmental branches from retrospective and prospective cohorts, and 1573 branches of interest corresponding to phantoms were detectable during the operation for verification, achieving 100% and 97% accuracy for segmental bronchi, 97.2% and 99.1% for segmental arteries, and 93.2% and 98.8% for segmental veins, respectively. This study was registered at as ChiCTR2100050985. The time consumption for reconstruction and the quality score were compared between the AI system and manual reconstruction software (Mimics®) for efficiency validation. Operation time and blood loss were compared between the retrospective cohort (without AI assistance) and prospective cohort (with AI assistance) for safety evaluation. Accuracy was verified by comparing virtual structures predicted by the AI system with anatomical structures of patients in retrospective (n = 113) and prospective cohorts (n = 139) who underwent lobectomy or segmentectomy at the Peking University Cancer Hospital. This AI system was developed based on a 3D convolutional neural network (CNN) and optimized by gradient descent after training with 500 cases, achieving a Dice coefficient of 89.2%. We aimed to develop a fully automated artificial intelligence-based three-dimensional (3D) reconstruction system (AI system) to assist thoracic surgery and to determine its accuracy, efficiency, and safety for clinical use. Ideal accuracy and high efficiency are prerequisites for its clinical application. Electronic address: phantoms are used in surgical planning and intervention. ![]() Electronic address: 5 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China. 4 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.3 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China.2 Linkdoc AI Research (LAIR), Linkdoc Information Technology (Beijing) Co., Ltd., Beijing, China.1 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
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