The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging: A Retrospective Case Analysis Clinical Study
Trial ID: NCT06864702
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-20
Completion date: 2025-05-15
Sponsor: Zhujiang Hospital
Conditions: Artificial Intelligence (AI); Deep Learning; Laparoscopic Surgery
Interventions: Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal
Locations: Zhujiang Hospital of Southern Medical University
Countries: China
Enrollment: 220
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Hepatocellular Carcinoma(HCC) is a common disease in China, ranking as the fourth most prevalent malignant tumor and the third leading cause of cancer-related deaths in the country. Along with other liver, biliary, pancreatic, and splenic diseases, it poses a serious threat to the lives and health of the Chinese population. Precise organ resection techniques, centered around accurate preoperative imaging and functional assessment as well as meticulous surgical operations, have become the mainstream in hepatobiliary surgery in the 21st century. These techniques require precise dissection of intrahepatic blood vessels, the biliary system, and the pancreatic-splenic duct system to achieve an optimal balance between eradicating lesions and preserving the normal function of the organs while minimizing trauma to the body.

Precise tissue resection via laparoscopy is a prerequisite for successful hepatobiliary surgery. Addressing how to assist surgeons in performing surgeries more safely and effectively, as well as how to enhance learning outcomes during training, are pressing issues that need to be resolved. Efficient learning and analysis of surgical videos may help improve surgeons' intraoperative performance.

In recent years, advancements in artificial intelligence (AI) have led to a surge in the application of computer vision (CV) in medical image analysis, including surgical videos. Laparoscopic surgery generates a large amount of surgical video data, providing a new opportunity for the enhancement of laparoscopic surgical CV technology. AI-based CV technology can utilize these surgical video data to develop real-time automated decision support tools and surgical training systems, offering new directions for addressing the shortcomings of laparoscopic surgery.

However, the application of deep learning models in surgical procedures still has some shortcomings. Based on this, the present study aims to conduct a retrospective analysis of cases involving laparoscopic hepatobiliary and pancreatic surgeries performed at Zhujiang Hospital, Southern Medical University, between 2017 and 2024. The goal is to investigate the recognition and validation of deep learning models for classifying surgical phase images in medical imaging, as well as for semantic segmentation of anatomical structures, surgical instruments, and surgical gestures, including abdominal CT and MRI.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent laparoscopic hepatobiliary and pancreatic surgery at Zhujiang Hospital, Southern Medical University, from January 1, 2017, to October 31, 2023.
* Liver function classified as Child-Pugh grade A or B.
* Age 18 to 85 years.
* Complete clinical medical records.

Exclusion Criteria:

* Presence of underlying diseases that cannot tolerate surgery (such as severe heart, lung, brain, or kidney dysfunction).
* Preoperative imaging examinations and intraoperative findings of cancer thrombus in the main and branch of the portal vein, common hepatic duct and its branches, hepatic vein main and branch, and inferior vena cava.
* Intraoperative findings of extrahepatic invasion and metastasis.
* Planned pregnancy, unplanned pregnancy, and pregnant individuals.
* Preoperative liver function classified as Child-Pugh grade C.
* Previous history of treatments such as radiofrequency or microwave ablation, radiotherapy, liver transplantation, etc.

Outcomes:
F1 score