Artificial Intelligence (AI)NCT06864702
The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging
A study testing Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal in people with Artificial Intelligence (AI) and Deep Learning, run by Zhujiang Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence (AI), Deep Learning, Laparoscopic Surgery
- What is being tested?
- Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 220 participants
- Where
- 1 siteChina
- Study dates
- 20 Dec 2023to 15 May 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Zhujiang Hospital of Southern Medical University
Guangzhou, Guangdong, 510280
About this study
Official title: The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging: A Retrospective Case Analysis Clinical Study
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.
Read more
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.
Read the detailed description
Hepatocellular Carcinoma(HCC) is a common disease in China. It ranks as the fourth most common 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 the lesions and preserving the normal function of the organs while minimizing trauma to the body. Therefore, key questions that must be carefully designed in preoperative planning include: How to maximize the removal of lesions while preserving normal tissues and organs? How to ensure safe surgical margins? How to control damage during surgery to ensure the rapid recovery of residual tissue function postoperatively? With the development of medical imaging and the advent of the information age, the field of liver surgery is entering the era of digital precision hepatobiliary surgery.
Precise tissue resection via laparoscopy is a prerequisite for successful hepatobiliary surgery. Compared to open surgery, laparoscopic surgery offers advantages such as less trauma, reduced postoperative pain, and faster recovery. However, laparoscopic surgery also has drawbacks, including insufficient tactile feedback, limited surgical space, narrow field of vision, and restricted maneuverability. The visibility and angles of laparoscopic instruments are limited. When encountering adhesions or complex situations, laparoscopic surgery can increase operational difficulty and prolong surgical time. Therefore, complex laparoscopic procedures require extensive experience and have a steep learning curve. 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 addressed. 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. AI systems can identify surgical phases for various important tasks, such as recording and analyzing adverse events, education, statistics, and assessing surgical performance. Currently, these tasks are performed manually by specialized surgeons, which is very time-consuming. AI systems can also perform scene segmentation, including identifying anatomical structures and areas to avoid damage to critical structures, as well as recognizing surgical instruments to prevent accidents. Uncontrolled instrument movements can cause damage to adjacent structures, and identifying surgical actions (e.g., suturing techniques) can help assess skill proficiency. Using such systems during surgery can facilitate real-time monitoring and assist decision-making, potentially enhancing safety and improving patient outcomes. For example, real-time assistance systems can alert surgeons to incorrect anatomical planes, erroneous maneuvers, or impending complications.
However, the application of deep learning models in surgical procedures still has some shortcomings. First, surgical phase recognition can independently estimate the remaining surgical time, effectively arranging logistics in the operating room and providing surgical interns with informative and targeted educational materials. However, current research on surgical phase recognition is still difficult to apply clinically. One reason is that the video duration required for AI models to recognize surgical phases is often too long. Limited training data and the optimization of overall accuracy make it challenging for AI to recognize shorter-duration steps. With improved model design and new annotation standards, AI can achieve high-precision step recognition, providing detailed information for the further development of valuable computer-assisted surgical systems.
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.
- Being tested
- Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal
- Keywords
- artificial inteligence(AI), deep learning, laparoscopic surgery
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominalBehavioural
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).
| Study group | Role | What they receive |
|---|---|---|
| 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) Receives: Behavioral: Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 1
Secondary outcomes (1)
IoU
In the semantic segmentation task, let the manually annotated region be X and the region predicted by the model be Y. The accuracy of the artificial intelligence model is calculated using IoU=(X∩Y)/(X∪Y) and Dice=(2|X∩Y|)/(|X|+|Y|).
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Mar 2025
- Study start20 Dec 2023
- Primary completion20 Mar 2025 (estimated)
- Study completion15 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 220 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhujiang Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06864702
- Sponsor's protocol ID
- 2024-KY-152-02
- Status last verified
- Nov 2024
- First submitted
- 4 Mar 2025
- Last updated
- 7 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Beyersdorffer P, Kunert W, Jansen K, Miller J, Wilhelm P, Burgert O, Kirschniak A, Rolinger J. Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks. Biomed Tech (Berl). 2021 Mar 1;66(4):413-421. doi: 10.1515/bmt-2020-0106. Print 2021 Aug 26.
PubMed 33655738Golany T, Aides A, Freedman D, Rabani N, Liu Y, Rivlin E, Corrado GS, Matias Y, Khoury W, Kashtan H, Reissman P. Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surg Endosc. 2022 Dec;36(12):9215-9223. doi: 10.1007/s00464-022-09405-5. Epub 2022 Aug 8.
PubMed 35941306Kim JH, Kim H. Modified liver hanging maneuver in laparoscopic major hepatectomy: the learning curve and evolution of indications. Surg Endosc. 2020 Jun;34(6):2742-2748. doi: 10.1007/s00464-019-07248-1. Epub 2019 Nov 11.
PubMed 31712899Gaitanidis A, Simopoulos C, Pitiakoudis M. What to consider when designing a laparoscopic colorectal training curriculum: a review of the literature. Tech Coloproctol. 2018 Mar;22(3):151-160. doi: 10.1007/s10151-018-1760-y. Epub 2018 Mar 6.
PubMed 29512045Vaz RM, Bordenali G, Bibancos M. Testicular Cancer-Surgical Treatment. Front Endocrinol (Lausanne). 2019 May 15;10:308. doi: 10.3389/fendo.2019.00308. eCollection 2019.
PubMed 31156556Moris 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.
PubMed 28746157Yoon YI, Kim KH, Kang SH, Kim WJ, Shin MH, Lee SK, Jung DH, Park GC, Ahn CS, Moon DB, Ha TY, Song GW, Hwang S, Lee SG. Pure Laparoscopic Versus Open Right Hepatectomy for Hepatocellular Carcinoma in Patients With Cirrhosis: A Propensity Score Matched Analysis. Ann Surg. 2017 May;265(5):856-863. doi: 10.1097/SLA.0000000000002072.
PubMed 27849661Han HS, Shehta A, Ahn S, Yoon YS, Cho JY, Choi Y. Laparoscopic versus open liver resection for hepatocellular carcinoma: Case-matched study with propensity score matching. J Hepatol. 2015 Sep;63(3):643-50. doi: 10.1016/j.jhep.2015.04.005. Epub 2015 Apr 12.
PubMed 25872167Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe M, Sato D, Sugomori Y, Hara S, Ito M. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020 Nov;34(11):4924-4931. doi: 10.1007/s00464-019-07281-0. Epub 2019 Dec 3.
PubMed 31797047Ziogas IA, Tsoulfas G. Advances and challenges in laparoscopic surgery in the management of hepatocellular carcinoma. World J Gastrointest Surg. 2017 Dec 27;9(12):233-245. doi: 10.4240/wjgs.v9.i12.233.
PubMed 29359029Rivas H, Diaz-Calderon D. Present and future advanced laparoscopic surgery. Asian J Endosc Surg. 2013 May;6(2):59-67. doi: 10.1111/ases.12028.
PubMed 23601993
Study at a glance
From the registry record, last updated by the sponsor on 7 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging: A Retrospective Case Analysis Clinical Study
- ClinicalTrials.gov ID
- NCT06864702
- Acronym / sponsor ID
- 2024-KY-152-02
- Keywords
- artificial inteligence(AI), deep learning, laparoscopic surgery
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 220 participants (estimated)
- Lead sponsor
- Zhujiang HospitalUniversity, hospital or other
- Study start
- 20 Dec 2023 (actual)
- Primary completion
- 20 Mar 2025 (estimated)
- Study completion
- 15 May 2025 (estimated)
- First posted
- 7 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Hepatocellular Carcinoma(HCC) is a common disease in China. It ranks as the fourth most common 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 the lesions and preserving the normal function of the organs while minimizing trauma to the body. Therefore, key questions that must be carefully designed in preoperative planning include: How to maximize the removal of lesions while preserving normal tissues and organs? How to ensure safe surgical margins? How to control damage during surgery to ensure the rapid recovery of residual tissue function postoperatively? With the development of medical imaging and the advent of the information age, the field of liver surgery is entering the era of digital precision hepatobiliary surgery.
Precise tissue resection via laparoscopy is a prerequisite for successful hepatobiliary surgery. Compared to open surgery, laparoscopic surgery offers advantages such as less trauma, reduced postoperative pain, and faster recovery. However, laparoscopic surgery also has drawbacks, including insufficient tactile feedback, limited surgical space, narrow field of vision, and restricted maneuverability. The visibility and angles of laparoscopic instruments are limited. When encountering adhesions or complex situations, laparoscopic surgery can increase operational difficulty and prolong surgical time. Therefore, complex laparoscopic procedures require extensive experience and have a steep learning curve. 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 addressed. 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. AI systems can identify surgical phases for various important tasks, such as recording and analyzing adverse events, education, statistics, and assessing surgical performance. Currently, these tasks are performed manually by specialized surgeons, which is very time-consuming. AI systems can also perform scene segmentation, including identifying anatomical structures and areas to avoid damage to critical structures, as well as recognizing surgical instruments to prevent accidents. Uncontrolled instrument movements can cause damage to adjacent structures, and identifying surgical actions (e.g., suturing techniques) can help assess skill proficiency. Using such systems during surgery can facilitate real-time monitoring and assist decision-making, potentially enhancing safety and improving patient outcomes. For example, real-time assistance systems can alert surgeons to incorrect anatomical planes, erroneous maneuvers, or impending complications.
However, the application of deep learning models in surgical procedures still has some shortcomings. First, surgical phase recognition can independently estimate the remaining surgical time, effectively arranging logistics in the operating room and providing surgical interns with informative and targeted educational materials. However, current research on surgical phase recognition is still difficult to apply clinically. One reason is that the video duration required for AI models to recognize surgical phases is often too long. Limited training data and the optimization of overall accuracy make it challenging for AI to recognize shorter-duration steps. With improved model design and new annotation standards, AI can achieve high-precision step recognition, providing detailed information for the further development of valuable computer-assisted surgical systems.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Experimental group | Experimental | Behavioral: Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal220 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) |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominalThe 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). | Behavioural | Experimental group |
Primary outcome
F1 score
Time frame: Day 1
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.
1 secondary outcome
IoU
Time frame: Day 1
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Mar 2025.
- Background: PubMed 33655738Beyersdorffer P, Kunert W, Jansen K, Miller J, Wilhelm P, Burgert O, Kirschniak A, Rolinger J. Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks. Biomed Tech (Berl). 2021 Mar 1;66(4):413-421. doi: 10.1515/bmt-2020-0106. Print 2021 Aug 26.
- Background: PubMed 35941306Golany T, Aides A, Freedman D, Rabani N, Liu Y, Rivlin E, Corrado GS, Matias Y, Khoury W, Kashtan H, Reissman P. Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surg Endosc. 2022 Dec;36(12):9215-9223. doi: 10.1007/s00464-022-09405-5. Epub 2022 Aug 8.
- Background: PubMed 31712899Kim JH, Kim H. Modified liver hanging maneuver in laparoscopic major hepatectomy: the learning curve and evolution of indications. Surg Endosc. 2020 Jun;34(6):2742-2748. doi: 10.1007/s00464-019-07248-1. Epub 2019 Nov 11.
- Background: PubMed 29512045Gaitanidis A, Simopoulos C, Pitiakoudis M. What to consider when designing a laparoscopic colorectal training curriculum: a review of the literature. Tech Coloproctol. 2018 Mar;22(3):151-160. doi: 10.1007/s10151-018-1760-y. Epub 2018 Mar 6.
- Background: PubMed 31156556Vaz RM, Bordenali G, Bibancos M. Testicular Cancer-Surgical Treatment. Front Endocrinol (Lausanne). 2019 May 15;10:308. doi: 10.3389/fendo.2019.00308. eCollection 2019.
- Background: PubMed 28746157Moris 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.
- Background: PubMed 27849661Yoon YI, Kim KH, Kang SH, Kim WJ, Shin MH, Lee SK, Jung DH, Park GC, Ahn CS, Moon DB, Ha TY, Song GW, Hwang S, Lee SG. Pure Laparoscopic Versus Open Right Hepatectomy for Hepatocellular Carcinoma in Patients With Cirrhosis: A Propensity Score Matched Analysis. Ann Surg. 2017 May;265(5):856-863. doi: 10.1097/SLA.0000000000002072.
- Background: PubMed 25872167Han HS, Shehta A, Ahn S, Yoon YS, Cho JY, Choi Y. Laparoscopic versus open liver resection for hepatocellular carcinoma: Case-matched study with propensity score matching. J Hepatol. 2015 Sep;63(3):643-50. doi: 10.1016/j.jhep.2015.04.005. Epub 2015 Apr 12.
- Background: PubMed 31797047Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe M, Sato D, Sugomori Y, Hara S, Ito M. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020 Nov;34(11):4924-4931. doi: 10.1007/s00464-019-07281-0. Epub 2019 Dec 3.
- Background: PubMed 29359029Ziogas IA, Tsoulfas G. Advances and challenges in laparoscopic surgery in the management of hepatocellular carcinoma. World J Gastrointest Surg. 2017 Dec 27;9(12):233-245. doi: 10.4240/wjgs.v9.i12.233.
- Background: PubMed 23601993Rivas H, Diaz-Calderon D. Present and future advanced laparoscopic surgery. Asian J Endosc Surg. 2013 May;6(2):59-67. doi: 10.1111/ases.12028.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal in people with Artificial Intelligence (AI) and Deep Learning, run by Zhujiang Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 7 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
F1 score (time frame: Day 1). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06864702, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Mar 2025.