Objective:To investigate the application value of augmented reality navigation combined with indocyanine green(ICG) fluorescence imaging technology in laparoscopic anatomical segment 8 liver resection.Methods:Clinical and pathological data from 8 patients with hepatocellular carcinoma located in segment 8 of the liver admitted to the First Department of Hepatobiliary Surgery,Zhujiang Hospital,Southern Medical University from October 2021 to October 2022 were collected restrospectively. Among them,there were 5 males and 3 females,aged between 40 and 72 years. During the operation,the self-developed laparoscopic augmented reality surgical navigation system was used to integrate the three-dimensional liver model with the laparoscopic scene,and ICG fluorescence imaging technology was used to guide the anatomical liver resection of segment 8. The predicted liver resection volume and actual liver resection volume,related surgical indicators and postoperative complications were analyzed.Results:Among the 8 patients, 4 underwent laparoscopic anatomical segment 8 liver resection,1 underwent laparoscopic anatomical ventral subsegment of segment 8 liver resection,2 underwent laparoscopic anatomical ventral subsegment combined with medial subsegment of segment 8 liver resection, and 1 underwent laparoscopic anatomical dorsal subsegment of segment 8 liver resection. All operations were completed under the guidance of augmented reality navigation combined with ICG fluorescence imaging,without conversion to open surgery. The operation time was (276.3±54.8)minutes(range:200 to 360 minutes). Intraoperative blood loss was (75.0±35.4)ml(range:50 to 150 ml). No blood transfusion was performed during the operation. The length of postoperative hospital stay was (7.6±0.8)days(range:7 to 9 days). There were no deaths or postoperative complications such as bleeding or biliary fistula during the perioperative period.Conclusion:Augmented reality navigation combined with ICG fluorescence imaging technology can guide the implementation of laparoscopic anatomical segment 8 liver resection.

Objective:To investigate the application value of augmented reality navigation combined with indocyanine green(ICG) fluorescence imaging technology in laparoscopic anatomical segment 8 liver resection.Methods:Clinical and pathological data from 8 patients with hepatocellular carcinoma located in segment 8 of the liver admitted to the First Department of Hepatobiliary Surgery,Zhujiang Hospital,Southern Medical University from October 2021 to October 2022 were collected restrospectively. Among them,there were 5 males and 3 females,aged between 40 and 72 years. During the operation,the self-developed laparoscopic augmented reality surgical navigation system was used to integrate the three-dimensional liver model with the laparoscopic scene,and ICG fluorescence imaging technology was used to guide the anatomical liver resection of segment 8. The predicted liver resection volume and actual liver resection volume,related surgical indicators and postoperative complications were analyzed.Results:Among the 8 patients, 4 underwent laparoscopic anatomical segment 8 liver resection,1 underwent laparoscopic anatomical ventral subsegment of segment 8 liver resection,2 underwent laparoscopic anatomical ventral subsegment combined with medial subsegment of segment 8 liver resection, and 1 underwent laparoscopic anatomical dorsal subsegment of segment 8 liver resection. All operations were completed under the guidance of augmented reality navigation combined with ICG fluorescence imaging,without conversion to open surgery. The operation time was (276.3±54.8)minutes(range:200 to 360 minutes). Intraoperative blood loss was (75.0±35.4)ml(range:50 to 150 ml). No blood transfusion was performed during the operation. The length of postoperative hospital stay was (7.6±0.8)days(range:7 to 9 days). There were no deaths or postoperative complications such as bleeding or biliary fistula during the perioperative period.Conclusion:Augmented reality navigation combined with ICG fluorescence imaging technology can guide the implementation of laparoscopic anatomical segment 8 liver resection.

Objective:To investigate the application value of major anatomical structure recognition model of minimally invasive liver resection based on deep learning.Methods:The retrospective and descriptive study was conducted. The 31 surgical videos of laparoscopic left lateral sectionectomy performed in Zhujiang Hospital of Southern Medical University from January 2019 to April 2023 were collected. Video clips containing the surgical procedure of left lateral lobe liver pedicle and left hepatic vein were screened by 2 liver surgeons. After quality control, screening and frame extraction, the major anatomical structures on the images of these clips were annotated. After pre-processing, these images were transported to the DeepLab v3+neural network framework for model training. Observation indicators: (1) video annotation and classification; (2) results of arti-ficial intelligence anatomical recognition model testing. Measurement data with normal distribution were represented as Mean± SD, and count data were described as absolute numbers. Results:(1) Video annotation and classification. A total of 4 130 frames of images were annotated in the 31 surgical videos, including 2 083 frames of annotated images for the left lateral lobe liver pedicle, 1 578 frames of annotated images for the left hepatic vein and 469 frames of annotated images for both the left lateral lobe liver pedicle and left hepatic vein. (2) Results of artificial intelligence anatomical recognition model testing. In four application scenarios (clean scene, bloodstain scene, partially obstruction by instrument scene, and small exposed area scene), the model was able to successfully recognize the left lateral lobe liver pedicle and left hepatic vein, with a recognition speed for anatomical markers >13 frames/s. When performing anatomical recognition on images with only the left lateral lobe liver pedicle, the Dice coefficient, intersection over union, accuracy, sensitivity and specificity of the model were 0.710±0.110, 0.560±0.120, 0.980±0.010, 0.640±0.030, and 0.980±0.010, respectively. The above indicators of the model were 0.670±0.180, 0.530±0.200, 0.980±0.010, 0.600±0.040, and 0.990±0.010 when performing anatomical recognition on images with only the left hepatic vein, and 0.580±0.180, 0.430±0.190, 0.980±0.010, 0.580±0.020, and 0.990±0.010 when per-forming anatomical recognition on images with both the left lateral lobe liver pedicle and left hepatic vein.Conclusion:The major anatomical structure recognition model of minimally invasive liver resection based on deep learning can be applied in identifying liver pedicle and hepatic vein.