Due to the inherently complex anatomical structure and physiological charac-teristics of the biliary system, biliary surgery faces considerable challenges in key stages such as preoperative planning, intraoperative identification, and postoperative management. These challenges contribute to a persistently high incidence of complications, severely affecting patient prognosis and overall survival. In recent years, with the continued advancement of interdisciplinary integration between medicine and engineering, intelligent digital technologies, centered on digital medicine, big data, and medical artificial intelligence, have been increasingly applied in the field of biliary surgery, offering new solutions to these longstanding problems. The authors systematically review recent advances and clinical applications of intelligent digital technologies in biliary surgery, and explore the evolution from clinical needs to technological innovation and practical implementation. The aim is to facilitate the transition of biliary surgery from an experience-based approach to an intelligent and precision-driven paradigm, thereby improving surgical safety and patient outcomes.

Pancreatic surgery remains one of the most challenging fields in general surgery due to its complex anatomy and high risk of complications.In recent years,the integration of digital intelligent technologies-such as three-dimensional(3D)reconstruction,fluorescence navigation,augmented/mixed reality(AR/MR),and artificial intelligence(AI)-has provided new strategies for achieving precision,safety,and intelligence in pancreatic surgery.This review summarizes the innovations and clinical applications of these technologies throughout the full perioperative process,including preoperative planning,intraoperative navigation,and postoperative monitoring.Preoperatively,the deep learning-based nnU-Net framework enables high-precision 3D reconstruction of peripancreatic vessels and pancreaticobiliary ducts for individualized surgical planning.Intraoperatively,multimodal image fusion combining indocyanine green fluorescence imaging with AR/MR navigation allows real-time visualization and spatial localization of key anatomical structures,enhancing surgical accuracy and safety.Postoperatively,AI-driven models integrated with wearable sensors and multimodal data support intelligent risk prediction and early intervention for complications.The systematic application of digital intelligence is reshaping the paradigm of pancreatic surgery,driving the transition from experience-based to data-driven and from surgeon-dependent to intelligent decision-making.With continuous algorithmic refinement and accumulation of clinical evidence,digital empowerment will further advance pancreatic surgery toward personalization,precision,and intelligence.

Pancreatic surgery remains one of the most challenging fields in general surgery due to its complex anatomy and high risk of complications.In recent years,the integration of digital intelligent technologies-such as three-dimensional(3D)reconstruction,fluorescence navigation,augmented/mixed reality(AR/MR),and artificial intelligence(AI)-has provided new strategies for achieving precision,safety,and intelligence in pancreatic surgery.This review summarizes the innovations and clinical applications of these technologies throughout the full perioperative process,including preoperative planning,intraoperative navigation,and postoperative monitoring.Preoperatively,the deep learning-based nnU-Net framework enables high-precision 3D reconstruction of peripancreatic vessels and pancreaticobiliary ducts for individualized surgical planning.Intraoperatively,multimodal image fusion combining indocyanine green fluorescence imaging with AR/MR navigation allows real-time visualization and spatial localization of key anatomical structures,enhancing surgical accuracy and safety.Postoperatively,AI-driven models integrated with wearable sensors and multimodal data support intelligent risk prediction and early intervention for complications.The systematic application of digital intelligence is reshaping the paradigm of pancreatic surgery,driving the transition from experience-based to data-driven and from surgeon-dependent to intelligent decision-making.With continuous algorithmic refinement and accumulation of clinical evidence,digital empowerment will further advance pancreatic surgery toward personalization,precision,and intelligence.

Due to the inherently complex anatomical structure and physiological charac-teristics of the biliary system, biliary surgery faces considerable challenges in key stages such as preoperative planning, intraoperative identification, and postoperative management. These challenges contribute to a persistently high incidence of complications, severely affecting patient prognosis and overall survival. In recent years, with the continued advancement of interdisciplinary integration between medicine and engineering, intelligent digital technologies, centered on digital medicine, big data, and medical artificial intelligence, have been increasingly applied in the field of biliary surgery, offering new solutions to these longstanding problems. The authors systematically review recent advances and clinical applications of intelligent digital technologies in biliary surgery, and explore the evolution from clinical needs to technological innovation and practical implementation. The aim is to facilitate the transition of biliary surgery from an experience-based approach to an intelligent and precision-driven paradigm, thereby improving surgical safety and patient outcomes.

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.

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 observe the clinical effect of percutaneous transhepatic one-step biliary fistulation (PTOBF) and percutaneous transhepatic cholangioscopy (PTCS) in the treatment of hepatolithiasis with hepatobiliary surgery history,and to explore the clinical application value of PTOBF.Methods This is retrospective analysis of 68 patients with hepatolithiasis who were admitted to hepatobiliary surgery in the First Affiliated Hospital of Guangzhou Medical University from November 2009 to October 2017.Among these cases,35 patients in the observation group (group PTOBF) were treated with PTOBF,and 33 patients in the control group (group PTCS) received PTCS treatment.The final clearance rate,the postoperative complications rate,the hospitalization time,the operation times within the course of treatment,the recurrence rate and the residual stenosis rate of the two groups were compared.Results Compared with group PTCS,the clearance rate was significantly higher in group PTOBF(82.9％ vs 54.6％,P＜0.05),while the postoperative complications rate between the two groups are similar (14.3 ％ vs 30.3 ％,P＞ 0.05);Besides,the hospitalization time(12.3±5.3 d vs 17.4±7.0 d,P＜0.05),the operation times within the course of treatment (2.2±1.3 vs 2.8±1.0,P＜0.05) and the recurrence rate(17.4％ vs 39.4％,P＜0.05) of group PTOBF were obviously lower.Conclusions PTOBF is a safe and feasible treatment for hepatolithiasis with hepatobiliary surgery history.Compared with PTCS,it has the advantages of short hospitalization time,fewer operations and better recovery.

Objective To explore the effect of LPLD (laparoscopic peritoneal lavage and drainage ) on SAP (severe acute pancreatitis), and to compare its effect with that of non- LPLD (conservative medical management). Methods We collected data from 87 consecutive patients with SAP, from January 2009 to May 2014, including LPLD group (n = 46) and non-LPLD group (n = 41). LPLD was performed in the 1st and 2nd week after the disease onset in LPDP group and other treatment in LPDP group was the same as that in non-LPLD group. Data were comparatively analyzed in two groups about the length of hospital stay, ICU stay, cure rate, incidence of complications and in-hospital mortality. Results In LPLD group, hospital stay, and ICU stay were shorter while cure rate was higher than those in non-LPLD group , and the difference was statistically significant (P < 0.05). In terms of the incidence of complications in two groups, only the incidence of sepsis indicated statistical significance (P < 0.05) and in-hospital mortality did not differ significantly between two groups (P > 0.05). Conclusion Compared with non-LPLD, LPLD is effective in short outcome, which is a promising treatment for SAP.

Objective To evaluate the clinical value of percutaneous transhepaticcholangioscopic lithotomy (PTCSL)combined with rigid cholangioscopy in treatment of recurrent hepatolithiasis. Methods Retrospective analysisof therapeutic result of 54 patientswith postoperative recurrent hepatolithiasisduring January 2012 to January 2015. Twenty eight cases were recruited as the observation group (PTCSL group). Twenty six cases were recruited as the control group (Laparotomy group). Following parameters were observed, operation time, intraoperative blood loss, clearance of stones and postoperative hospital stay. Results The operation time, intraoperative blood loss , clearanceof stones , and the postoperative hospital stay of the PTCSL group werebetter than that of the laparotomy group (P<0.05). The number of patients with postoperative pain of the PTCSL group was significantly lower than in the laparotomygroup (P<0.05). There were no significant differences in other complication rates. There was no difference in terms of stone recurrence , incidence of cholangitis and intrahepatic biliary strictures recurrence ratebetween two groups in follow-up period. Conclusions PTCSL combined with rigid choledochoscopywas a safe and effectivemethod with minimal invasion formanagement of the postoperative recurrent hepatolithiasis. It could got a better resultsin the short-term outcomes.