The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging

NCT06864702 | Enrolling By Invitation

• 1 other identifier: 2024-KY-152-02
• Study Type: interventional
• Target: 220
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Artificial Intelligence (AI); Deep Learning; Laparoscopic Surgery

Keywords

artificial inteligence(AI); deep learning; laparoscopic surgery

Outcome Measures

Primary Outcomes (1)

• F1 score

  In the accuracy of medical image segmentation, TP represents a case predicted as positive, and the true label is positive; FN represents a case predicted as negative, but the true label is positive; FP represents a case predicted as positive, but the true label is negative; TN represents a case predicted as negative, and the true label is also negative. The F1 score is calculated as F1 = 2 × \[Precision × Recall / (Precision + Recall)\], and it takes into account both the precision and recall of the classification model, defined as the harmonic mean of the model's precision and recall.

  Day 1

Secondary Outcomes (1)

• IoU

  Day 1

Other Outcomes (3)

• Precision

  Day 1

• Recall

  Day 1

• Accuracy

  Day 1

Study Arms (1)

Experimental group

EXPERIMENTAL

220 participants were allocated to this group. The intervention is whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal. The construction of a deep learning model for semantic segmentation of images during laparoscopic hepatobiliary and pancreatic surgery in this arm, focusing on classifying surgical stages, anatomical structures, surgical instruments, and surgical gestures, along with the validation of model performance (Intersection over Union, Dice score, accuracy, precision, recall, and F1 score)

Behavioral: Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal

Interventions

Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal BEHAVIORAL

The construction of a deep learning model for semantic segmentation of images during laparoscopic hepatobiliary and pancreatic surgery, focusing on classifying surgical stages, anatomical structures, surgical instruments, and surgical gestures, along with the validation of model performance (Intersection over Union, Dice score, accuracy, precision, recall, and F1 score).

Experimental group

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Zhujiang Hospital: lead

Study Sites (1)

Zhujiang Hospital of Southern Medical University

Guangzhou, Guangdong, 510280, China

Location

Related Publications (11)

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  PMID: 33655738 BACKGROUND

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  PMID: 35941306 BACKGROUND

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  PMID: 29512045 BACKGROUND

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  PMID: 31156556 BACKGROUND

• Moris D, Vernadakis S. Laparoscopic Hepatectomy for Hepatocellular Carcinoma: The Opportunities, the Challenges, and the Limitations. Ann Surg. 2018 Jul;268(1):e16. doi: 10.1097/SLA.0000000000002458. No abstract available.

  PMID: 28746157 BACKGROUND

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  PMID: 31797047 BACKGROUND

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  PMID: 23601993 BACKGROUND

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 4, 2025
• First Posted: March 7, 2025
• Study Start: December 20, 2023
• Primary Completion: March 20, 2025
• Study Completion: May 15, 2025
• Last Updated: March 7, 2025

Record last verified: 2024-11

Locations

CN (1)