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Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve

Laparoscopic Nissen fundoplication (LNF) requires extensive training for surgical proficiency, with significant early learning curve complications. A ...

Zhejiang Chinese Medical University

Advanced bionic organ models with vivid biological structures and wetness and softness are essential for medical-surgical training. Still, there are many challenges in the preparation process, such as matching mechanical properties, good feedback on surgical instruments, reproducibility of specific surgical scenarios, and distinguishability between structural levels. In this paper, we achieved tissuemimicking dual-network (DN) hydrogels with customizable stiffness by adjusting the composition of the hydrogel matrix and the immersion time of the ionic solution to match different biological soft tissues precisely. Combined with advanced threedimensional (3D) printing fabrication techniques, various performance-tunable bionic hydrogel organ models with structural complexity and fidelity, including kidney, liver, pancreas, and vascular tissues, were perfectly fabricated. The simulation and applicability of the model were also simulated for the forced change of the suture needle in the puncture and suture of a single tissue and between different tissues, the cutting of substantive organs by ultrasonic scalpel, the coagulation and hemostasis of blood vessels, the visualization of the internal structure under ultrasound, and the microwave ablation of liver tumors. By constructing advanced biomimetic organ models based on hydrogel with specific and tunable properties, the development of surgical training, medical device testing, and medical education reform will be significantly promoted.

Multifunctional high-simulation 3D-printed hydrogel model manufacturing engineering for surgical training.

Gastroesophageal reflux disease (GERD) profoundly compromises the quality of life, with prolonged untreated cases posing a heightened risk of severe complications such as esophageal injury and esophageal carcinoma. The imperative for early diagnosis is paramount in averting progressive pathological developments. This study introduces a wrapper-based feature selection model based on the enhanced Runge Kutta algorithm (SCCRUN) and fuzzy k-nearest neighbors (FKNN) for GERD prediction, named bSCCRUN-FKNN-FS. Runge Kutta algorithm (RUN) is a metaheuristic algorithm designed based on the Runge-Kutta method. However, RUN's effectiveness in local search capabilities is insufficient, and it exhibits insufficient convergence accuracy. To enhance the convergence accuracy of RUN, spiraling communication and collaboration (SCC) is introduced. By facilitating information exchange among population individuals, SCC expands the solution search space, thereby improving convergence accuracy. The optimization capabilities of SCCRUN are experimentally validated through comparisons with classical and state-of-the-art algorithms on the IEEE CEC 2017 benchmark. Subsequently, based on SCCRUN, the bSCCRUN-FKNN-FS model is proposed. During the period from 2019 to 2023, a dataset comprising 179 cases of GERD, including 110 GERD patients and 69 healthy individuals, was collected from Zhejiang Provincial People's Hospital. This dataset was utilized to compare our proposed model against similar algorithms in order to evaluate its performance. Concurrently, it was determined that features such as the internal diameter of the esophageal hiatus during distention, esophagogastric junction diameter during distention, and external diameter of the esophageal hiatus during non-distention play crucial roles in influencing GERD prediction. Experimental findings demonstrate the outstanding performance of the proposed model, with a predictive accuracy reaching as high as 93.824 %. These results underscore the significant advantage of the proposed model in both identifying and predicting GERD patients.

Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease.

Magnetic sphincter augmentation (MSA) is a novel surgical technique investigated at many clinical institutes worldwide. In 2012, it received approval for treating Gastroesophageal reflux disease (GERD) because of its superior benefits compared to drug therapies. This study aimed to explore the safety and efficacy of MSA compared with Laparoscopic Nissen fundoplication (LNF) for GERD treatment.

A comparative study of magnetic sphincter augmentation and Nissen fundoplication in the management of GERD.

The evaluation of thyroid lesions through Fine-Needle Aspiration Biopsy (FNAB) is a common procedure that requires advanced hand manipulation skills. Conventional training models for this procedure lack essential features such as tactile sensation and the ability to repeat punctures similar to those of real organs. To improve the quality of training, we have developed a hydrogel thyroid model that possesses features such as high-water retention and self-healing properties. This model consists of polyvinyl alcohol (PVA), polyacrylic acid (PAA), and trehalose that enhance water retention. By utilizing indirect printing technology, this hydrogel-based thyroid model closely resembles those of porcine thyroid tissue in terms of compression modulus and friction coefficient, exhibiting exceptional conformability, flexibility, and a water retention rate of 94.7 % at 6 h. It also displays a thrust force range of 0-0.98 N during simulated puncture, closely approximating real FNAB operations. This model shows evidence that it effectively simulates thyroid tissue and can be utilized for repetitive FNAB training to enhance the proficiency of medical personnel. Our study focuses on introducing new possibilities for developing advanced materials training models to be utilized in the medical field.

A high-water retention, self-healing hydrogel thyroid model for surgical training.

A comparative study of magnetic sphincter augmentation and Nissen fundoplication in the management of GERD. Author's reply.

Three-dimensional (3D) printed surgical models provide an excellent surgical training option to closely mimic real operations to teach medical students who currently rely largely on visual learning aided with simple suturing pads. There is an unmet need to create simple to complex surgical training programs suitable for medical students. A prospective cohort study was conducted on a group of 16 6th year students. They were randomly divided into two groups for suture training on a basic training pad or on unique 3D-printed intestinal anastomosis models. After 4 weeks of open and laparoscopic surgical training, exams were performed on the standardized 3D-printed model at the end of each stage to assess surgical performance including surgical time and scores. Both groups had similar skills before the start of each stage. In stage 1, both groups showed comparable learning performance, but the 3D model group performed better in Exam 1. In stage 2, the 3D model group took more time but showed significant improvements and outperformed the pad group in Exam 2 in both performance scores and time. Post-training questionnaires indicated increased interest in surgery and technical training among students using 3D models. Realistic 3D-printed models benefit surgical training, expected to become integral in teaching operative skills and techniques to medical students.

A randomized cohort study on the use of 3D printed models to enhance surgical training in suturing techniques.

Gastroesophageal reflux disease (GERD) is a prevalent condition often associated with hiatal hernia (HH). While conventional laparoscopic anti-reflux surgery (CLAS) remains the gold standard for refractory GERD, robotic-assisted anti-reflux surgery (RAAS) has emerged as a promising alternative, offering enhanced precision and ergonomics. This study compares perioperative outcomes and mid-term symptom resolution between RAAS and CLAS. A retrospective cohort analysis included 91 patients undergoing primary anti-reflux surgery (RAAS n = 31, CLAS n = 60) between April 2022 and April 2024. Patients had confirmed GERD unresponsive to proton pump inhibitors (PPI), excluding giant HH or prior surgery. Standardized preoperative evaluation and surgical protocols were followed. Perioperative metrics (operative time, complications) and symptomatic outcomes at 12-month follow-up (GERDQ scores, symptom resolution, reoperations, PPI use, endoscopic/ultrasound findings) were compared. Demographic and baseline characteristics were comparable between groups. RAAS had significantly longer total operative time (158.8 vs. 146.4 min, p = 0.02) and docking/setup time (21.2 vs. 11.5 min, p = 0.02), primarily due to the initial dissection phase. Intraoperative complication rates (3.2% vs. 5.0%, p = 0.65), postoperative stay (3.32 vs. 3.75 days, p = 0.40), and 30-day mortality (0% both) were similar. At median follow-up, rates of complete symptom resolution (80.6% vs. 75.0%, p = 0.55), residual symptoms (e.g., dysphagia: 6.5% vs. 15.0%, p = 0.40), postoperative PPI use (16.1% vs. 8.3%, p = 0.43), objective reflux on imaging (7.7% both), and reoperation rates (3.2% vs. 5.0%, p = 0.70) showed no significant differences. RAAS achieves perioperative safety and medium-term symptomatic outcomes comparable to CLAS. While technically feasible, RAAS requires longer operative times initially, with a notable learning curve for docking. No significant clinical superiority of RAAS was demonstrated in this cohort. Further randomized trials evaluating long-term durability and cost-effectiveness are warranted.

Robot-assisted versus conventional laparoscopic antireflux surgery: a retrospective cohort analysis of perioperative and medium-term outcomes.

Correction: Robot-assisted versus conventional laparoscopic antireflux surgery: a retrospective cohort analysis of perioperative and medium-term outcomes.

Article	Year
Multifunctional high-simulation 3D-printed hydrogel model manufacturing engineering for surgical training. International journal of bioprinting\| PubMed ID: 37457926	2023
Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease. Computers in biology and medicine\| PubMed ID: 38657464	2024
A comparative study of magnetic sphincter augmentation and Nissen fundoplication in the management of GERD. Hernia : the journal of hernias and abdominal wall surgery\| PubMed ID: 39400916	2024
A high-water retention, self-healing hydrogel thyroid model for surgical training. Materials today. Bio\| PubMed ID: 39624054	2024
A comparative study of magnetic sphincter augmentation and Nissen fundoplication in the management of GERD. Author's reply. Hernia : the journal of hernias and abdominal wall surgery\| PubMed ID: 39666230	2024
A randomized cohort study on the use of 3D printed models to enhance surgical training in suturing techniques. Scientific reports\| PubMed ID: 39753693	2025
Robot-assisted versus conventional laparoscopic antireflux surgery: a retrospective cohort analysis of perioperative and medium-term outcomes. Journal of robotic surgery\| PubMed ID: 40613965	2025
Correction: Robot-assisted versus conventional laparoscopic antireflux surgery: a retrospective cohort analysis of perioperative and medium-term outcomes. Journal of robotic surgery\| PubMed ID: 40772990	2025

Zhu Zhihao

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