A Multicenter Prospective Observational Study of Computer-aided Risk Perception and Prognosis Prediction in the Whole Process of Laparoscopic Hepatobiliary and Pancreatic Surgery

Artificial intelligence technology is used to realize high-quality 3D scene reconstruction, whole process segmentation, scene activity understanding for common surgery guidance in hepatobiliary surgery, as well as intelligent identification, perception, early warning of key events in the whole process of endoscopic surgery (such as bleeding, blocking, tumor location, anastomosis, etc.), and decision-making assistance

Study Overview

• Status: Enrolling by invitation
• Conditions: Hepatectomy; Cholecystectomy, Laparoscopic; Hepatocellular Carcinomas; Pancreatoduodenectomy

Detailed Description

Endoscopic surgery is the most important and commonly used minimally invasive surgery technology in the field of modern surgery, especially in hepatobiliary surgery, which has become one of the conventional diagnosis and treatment methods of surgery. Compared with traditional open surgery, it has smaller trauma, faster recovery time and lower complication rate. However, the limited visual field of surgical observation caused by the narrow surgical space and the difficulty of immediate identification of key events in the surgical scene greatly increase the difficulty and complexity of endoscopic surgery. Its safety and efficacy largely depend on the precise perception of the complex surgical field and the ability to handle key events during the operation. Therefore, combining modern image processing technology and machine learning algorithm, it is particularly urgent to develop a system that can provide real-time dynamic perception and safety warning of endoscopic surgery. Although domestic and foreign scholars have carried out a lot of research on endoscopic video dynamic perception and safety warning, the current research only focuses on local problems in the surgical process. Traditional image processing technology is often difficult to meet the needs of highly sensitive to real-time dynamic information of surgery, it is difficult to achieve efficient three-dimensional reconstruction of the surgical scene, it can not provide the key anatomical structure information of human organs, and it can not accurately detect the key events in the operation process. For endoscopic surgery, further research is urgently needed to realize the video dynamic perception and safety early warning system of endoscopic surgery, and assist doctors to achieve safe, accurate and efficient endoscopic surgery. In recent years, with the continuous progress of computer graphics and image technology and machine learning methods, the dynamic perception and safety early warning system of endoscopic surgery video will develop towards higher automation and intelligence. Future research may focus on improving the real-time and accuracy of the algorithm, as well as how to better integrate artificial intelligence technology into the clinical operation process, realize the real-time perception and safety warning of endoscopic surgery, and improve the efficiency and safety of surgery through the comprehensive analysis and understanding of endoscopic surgery process. Due to the complexity and variability of the endoscopic surgery environment, it is difficult to identify the key anatomical structures of organs during the operation, and it is very dependent on the subjective empirical judgment of the surgeon. There is a lack of objective instructions. It is particularly important to develop a machine learning method that can detect, perceive and recognize the key anatomical structures in real time during the operation. At the same time, the workflow of endoscopic surgery is fine and complex, so it is very necessary to comprehensively analyze and detect the key events and activity scenes in the video of endoscopic surgery through the AI auxiliary system. In addition, the realization of intraoperative hidden target area augmented reality surgery navigation needs to be carried out on the accurate dynamic organ surface reconstruction and non rigid registration results. However, the complex and narrow field of view endoscopic video further reduces the accuracy of non rigid registration, making augmented reality assisted endoscopic surgery extremely challenging. In conclusion, how to solve the problem of real-time dynamic perception and safety warning of endoscopic video is the key to achieve safe, accurate and efficient endoscopic surgery in clinic. Through the research and application of endoscopic video real-time dynamic perception and safety early warning technology, it can realize real-time dynamic perception, key event early warning, prediction of the location of invisible lesions and other decision-making information in various high-risk and difficult endoscopic surgery processes, and assist doctors to "see", "see clearly", "see accurately" in the operation process, so as to further improve the efficiency and safety of endoscopic surgery. At the same time, based on the above content, the success of the treatment of some key fields in surgery will greatly affect the prognosis of patients and the quality of life of patients. Another purpose of this study is to more comprehensively and objectively understand the incidence, risk factors, prevention and treatment of intraoperative and postoperative complications after evaluation combined with surgical video analysis, so as to provide clinicians with a more scientific treatment plan and guidance.

• Study Type: Observational
• Enrollment (Estimated): 1500

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510515
      • Department of hepatobiliary surgery, Nanfang Hospital, Southern Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The subjects of this study were patients admitted to the hepatobiliary surgery department of each center for surgical treatment with complete surgical video

Description

Inclusion Criteria:

• Voluntarily sign informed consent Patients who underwent hepatectomy and cholecystectomy and were followed up in the research center hospitals from July 2024 to December 2028 Complete case, imaging and operation video data

Exclusion Criteria:

• Patients who had other diseases before surgery, which may affect the results of the study Patients who developed postoperative complications but could not confirm their relevance to surgery According to the judgment of the researcher, it is not suitable to participate in this study

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
experimental group; Group according to the characteristics of different cases (for example), collect the basic information of all included patients and clinical case data, and make descriptive statistical analysis: the basic information of patients, operation methods, operation time, etc

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Main effectiveness indicators	The 1-year progression free survival rate of patients with malignant tumors were evaluated based on RECIST v1.1. The computer then predicts according to the complications, surgical videos, and pre -, intra -, and post-operative examinations, and compares them with the real situation	1 year
Main effectiveness indicators	The 1-year overall survival rate of patients with malignant tumors were evaluated. The computer then predicts according to the complications, surgical videos, and pre -, intra -, and post-operative examinations, and compares them with the real situation	1 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Safety indicators	According to different surgical methods and operations, the postoperative adverse events were counted, including laboratory data, vital signs, image data, etc.	1 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Effectiveness of image recognition	Including the recognition accuracy of artificial intelligence for key operations of endoscopic surgery; High risk process identification accuracy;	1 years
Relevant indicators of intraoperative scene reconstruction by computer	Including intraoperative scene reconstruction image perception similarity; Preoperative and intraoperative organ 2D-3D registration error	1 years

Collaborators and Investigators

• Sponsor: Nanfang Hospital, Southern Medical University
• Collaborators: Guangdong Provincial Hospital of Traditional Chinese Medicine; Chinese University of Hong Kong; The Affiliated Hospital Of Southwest Medical University; Jiangmen Central Hospital; Shenzhen Third People's Hospital; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Affiliated Hospital of Guangdong Medical University; Huzhou Central Hospital，Affiliated Hospital of Huzhou University; Shenzhen Second People's Hospital
• Investigators: Principal Investigator: Kai Wang, Department of hepatobiliary surgery, Nanfang Hospital, Southern Medical University

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2024
• Primary Completion (Estimated): September 1, 2025
• Study Completion (Estimated): January 1, 2027

Study Registration Dates

• First Submitted: October 7, 2024
• First Submitted That Met QC Criteria: October 16, 2024
• First Posted (Actual): October 17, 2024

Study Record Updates

• Last Update Posted (Actual): October 17, 2024
• Last Update Submitted That Met QC Criteria: October 16, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Keywords: artificial intelligence; Laparoscopic technique; risk perception; Hepatobiliary pancreatic
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Digestive System Neoplasms; Liver Diseases; Liver Neoplasms; Carcinoma, Hepatocellular
• Other Study ID Numbers: NFEC-2024-403

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No