This study is a pilot trial designed to evaluate the feasibility and safety of Al-ENDO assisted ESD in patients with gastrointestinal superficial lesions. Patients with gastrointestinal superficial lesions are scheduled for conventional ESD will be screened for eligibility. The study consists of a few stages: 1. In the first phase, the Al-ENDO system will be tested in the background of the ESD procedure prospectively, but without interfering the endoscopists. The Al-ENDO system would be installed in the Endoscopy Centre in Prince of Wales Hospital. Real-time video analysis would be conducted in the background without interference of the endoscopists' performance. A total of 30 clinical ESD procedures would be analysed, with the goal of achieving good accuracy of the system. The data obtained from this group of patients would also serve as control group for comparison with the subsequent procedures with Al-ENDO support. 2. The second phase of the study would comprise of a the prospective pilot study which Al-ENDO system would be connected to the endoscopy tower with the auxiliary output monitor placing side-by-side with the endoscopy main monitor. The GUI would be displayed in the auxiliary monitor. This phase aims to demonstrate device and patients' safety throughout the procedure, and a total of 10 patients would be recruited, with the target of ensuring smooth dissection procedure without system interruption of failure. In this phase of the study, only expert endoscopist would be involved in performing the procedure. 3. The third phase of the study comprises of a continuation of the initial pilot study with additional of 20 more patients, so that total of 30 procedures would be performed to compare the clinical outcomes with the control group collected previously.
Gastrointestinal (GI) cancer, including stomach, colorectal, and esophageal cancer, is collectively the most common cancer worldwide and the leading cause of cancer-related death globally. The disease imposes a significant burden in Hong Kong, with more than 6,000 new cases diagnosed annually according to local cancer statistics. Despite substantial advances in understanding its pathology, molecular mechanisms, and therapeutic strategies, the survival rate of advanced GI cancer remains suboptimal, with a 5-year survival rate below 60% for most malignancies. The introduction of government-led initiatives and international cancer policies has expanded early GI cancer screening programs, enabling more patients to be diagnosed at an earlier stage. Early detection allows for less-invasive and more effective treatment options with improved prognosis and increased curative surgical rates. Complete tumor resection remains the only curative approach for localized GI cancer. Endoscopic submucosal dissection (ESD), a minimally invasive endoscopic technique, has become an established method for early-stage cancers confined to the mucosal layer. ESD enables en-bloc resection of lesions, achieving lower local recurrence rates compared with conventional endoscopic mucosal resection. It has also demonstrated favorable perioperative outcomes, including shorter operative time, lower complication rates, and reduced hospital stay. In countries where screening programs are well implemented, such as Japan, the number of ESD procedures now exceeds radical gastrectomies, making ESD the preferred treatment modality for early gastric cancer. ESD is also used for the treatment of pre-malignant lesions in the GI tract, preventing progression to invasive cancer. An ESD procedure involves four main phases: (1) marking of the targeted lesion, (2) submucosal injection to create a protective cushion, (3) circumferential incision, and (4) submucosal dissection and removal of the lesion. Although ESD enables complete resection of lesions regardless of size and has a recurrence rate below 1%, it remains technically demanding and carries risks such as bleeding and perforation. In regions where ESD adoption is lower, the learning curve may exceed several hundred cases before proficiency is achieved. Increasing international guidance and training efforts have encouraged a global expansion in the use of ESD as its clinical benefits become more widely recognized. Safe and precise dissection of cancerous tissue within the complex gastrointestinal environment is critical. Medical safety events in surgical practice contribute significantly to excess hospital stays, costs, and mortality. ESD is technically challenging, requiring endoscopists to maintain clear visualization of the submucosal layer while preserving a stable endoscopic field. Anatomical complexities such as colon flexures, thin colonic walls, mucosal folds, and peristalsis further increase the difficulty of colorectal ESD and raise the risk of incomplete dissection and perforation. Given these challenges, there is a clear need for advanced technologies that enhance surgical precision and safety through intelligent intraoperative guidance. To improve surgical quality and patient safety, it is essential to develop systems that learn from expert experience and surgical outcomes, providing real-time insights that help prevent operative errors. Identification of the correct dissection plane, submucosal vessels, and cutting landmarks contributes to safe and complete tumor removal. An AI-ENDO integrated platform has been developed by the investigators to assist endoscopists in enhancing the safety and efficiency of ESD procedures. The system incorporates multiple intelligent functions: 1. Real-time recognition of ESD surgical phases and performance assessment The AI-ENDO platform analyzes surgical workflow to identify the ongoing procedural phase at each moment. The model was trained on high-quality expert ESD videos collected from Prince of Wales Hospital and externally validated using data from several international centers. The system demonstrated high accuracy, precision, and recall, maintaining consistent performance across datasets. The integrated platform operates with real-time computational efficiency. 2. Real-time identification of safe and dangerous zones during ESD The system performs automated segmentation of intraoperative visual fields, identifying anatomic structures such as mucosa, submucosa, muscle layers, and blood vessels. This enables real-time differentiation between safe dissection zones and areas at higher risk for complications. Validation studies using endoscopic surgical videos demonstrated high segmentation accuracy and low latency, with experiments confirming feasibility during live animal trials. AI assistance was associated with shorter procedural time and reduced bleeding events compared with standard procedures of similar complexity. 3. Intelligent dissection trajectory guidance using reinforcement learning Reinforcement learning and imitation learning methods were incorporated to predict optimal dissection trajectories based on expert surgical demonstrations. The system analyzes surgical context in real time to provide decision support and trajectory prediction. Testing on extensive video datasets demonstrated improved accuracy and generalization compared with existing trajectory prediction frameworks. Through the integration of these functions, the AI-ENDO multifunctional platform aims to support intraoperative decision-making, reduce adverse events, and shorten the learning curve for ESD procedures. The purpose of this study is to evaluate the clinical utility and efficacy of the AI-ENDO system in endoscopic submucosal dissection for early gastrointestinal neoplasia. Technical success, clinical success, and post-ESD adverse events will be systematically assessed.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
60
During procedure of AI-ENDO assisted ESD, the AI-ENDO system will be connected to the video output system of the main endoscopy processor, so that real-time video data could be fed to the AI-ENDO system for AI analysis. In ESD procedures with AI-ENDO assistance, an auxiliary monitor would be placed side-by-side to the main endoscopy output monitor. This enables endoscopist to simultaneously take reference to the AI interpreted videos.
The ESD procedure would be performed in the same manner as in usual clinical practice. In brief, after identification of the target lesion, submucosal injection will be performed to elevate the submucosal layer from the muscularis propria. Mucosal incision followed by submucosal dissection will be performed using dedicated ESD knives. Countertraction method could be utilized based on the endoscopists' personal preference. After successful resection and ensuring adequate haemostasis, the specimen would be retrieved for pathological evaluation.
Department of Surgery, Faculty of Medicine, the Chinese University of Hong Kong
Hong Kong, Hong Kong
RECRUITINGTechnical success rate
Defined as the rate of procedure with successful en-bloc, without major intra-operative adverse event. Unit of measure: % of procedures
Time frame: 1 day
Clinical success rate
Defined as the rate of procedure with pathological R0 resection, without major intra-operative and post-operative adverse event. Unit of measure: % of procedures
Time frame: 30 days
Total procedure time
Time taken from injection to completion of resection. Unit of measure: minutes
Time frame: 1 day
Resected lesion size
Size of the resected lesion. Unit of measure: millimeters (mm)
Time frame: 1 day
Lesion location
Location of the lesion in the GI tract. Unit of measure: categorical (location)
Time frame: 1 day
Lesion histological type
The pathological type of the resected lesion. Unit of measure: categorical (histology)
Time frame: 30 days
NASA Task Load Index
Endoscopist's NASA Task Load Index (NASA-TLX) score for the procedure. Score range 0-100, where higher scores means a higher, more demanding perceived workload. Unit of measure: score (0-100)
Time frame: 1 day
Overall rate of intraprocedural adverse events
Rate of all intra-procedural adverse event. Unit of measure: % of procedures
Time frame: 1 day
Rate of intraprocedural adverse events - partial thickness muscle injury
Rate of partial thickness muscle injury without full thickness perforation occurring intra-procedure. Unit of measure: % of procedures
Time frame: 1 day
Rate of intraprocedural adverse events - full thickness perforation
Rate of full thickness perforation occurring intra-procedure. Unit of measure: % of procedures
Time frame: 1 day
Rate of intraprocedural adverse events - haemorrhage
Rate of major bleeding during procedure causing hemodynamic instability, or require blood transfusion, or causing haemoglobin drop \> 2 g/dL. Unit of measure: % of procedures
Time frame: 1 day
Rate of intraprocedural adverse events - Peritonitis
Rate of inflammation of the peritoneum occurring intra-procedure. Unit of measure: % of procedures
Time frame: 1 day
Rate of intraprocedural adverse events - others
Rate of any other intra-procedure adverse event. Unit of measure: % of procedures
Time frame: 1 day
Overall rate of post-procedural adverse events
Rate of all post-procedural adverse event. Unit of measure: % of patients
Time frame: 30 days
Rate of postprocedural adverse events - Electrocoagulation syndrome
Rate of lectrocoagulation syndrome, which is defined as presence of abdominal pain, and fever / leucocytosis after the procedure. Unit of measure: % of patients
Time frame: 30 days
Rate of post-procedure adverse events - delayed haemorrhage
Rate of significant bleeding with haemoglobin drop \> 2 g/dL, or causing hemodynamic instability, or require blood transfusion, or requiring re-intervention by endoscopy / surgery. Unit of measure: % of patients
Time frame: 30 days
Rate of post-procedure adverse events - delayed perforation
Rate of delayed perforation occurring after completion of procedure. Unit of measure: % of patients
Time frame: 30 days
Rate of post-procedure adverse events - others
Rate of any other adverse events after the procedure. Unit of measure: % of patients
Time frame: 30 days
AI-system of latency
Time lag in image interpretation by the AI system at ≥25 frames per second. Unit of measure: milliseconds (ms)
Time frame: 1 day
AI system accuracy (pixel accuracy)
Mean pixel-wise accuracy of AI interpretation, expressed as percentage of correctly classified pixels. Unit of measure: % accuracy
Time frame: 1 day
AI system accuracy (Dice similarity coefficient)
Mean spatial overlap between AI-predicted and ground-truth regions (Dice coefficient). Unit of measure: Dice score (0-1)
Time frame: 1 day
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