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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 Provincial People's Hospital

Rapid development of three-dimensional (3D) printing technique has enabled the production of many new materials for medical applications but the dry laboratory surgical training model made of soft and flexible materials is still insufficient. We established a new 3D-printed Nissen fundoplication training model of which materials simulate the real mechanical properties. In this study, 16 participants were divided into two groups: Experimental group and control group. The validity of model was tested using Likert scale by the experts and the experimental group. To evaluate the efficacy, performances of the experimental group were scored at the first, fourth, and eighth training by OSATS system and the duration of procedure was compared through the use of recorded video. Meanwhile, an  model was used to compare the performance of the experiment group and control group after the training in the same way. Our results showed that the 3D-printed model can support the future surgical applications, help improve surgical skills, and shorten procedure time after training.

Validity of a Soft and Flexible 3D-Printed Nissen Fundoplication Model in Surgical Training.

Laparoscopic choledochojejunostomy (LCJ) is an essential basic skill for biliary surgeons. Therefore, we established a convenient and effective LCJ 3D printing model to evaluate whether the model could simulate the actual operation situation and determine its effectiveness and validity in surgical training.

Development and evaluation of a portable and soft 3D-printed cast for laparoscopic choledochojejunostomy model in surgical training.

To explore the sources of information on antireflux surgery for patients undergoing this surgery in China.

Sources of information on surgical options for patients undergoing antireflux surgery: a single-center questionnaire-based survey.

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.

Intestinal anastomosis is a clinical procedure widely used to reconstruct the digestive tract, but authentic laparoscopic intracorporeal intestinal anastomosis (LIIA) models are lacking. However, three-dimensional (3D) printing can enable authentic and reusable models. In this paper, a novel cost-effective 3D-printing training model of LIIA is designed and the authenticity and validity of the model are tested.

Assessment of laparoscopic intracorporeal intestinal anastomosis training using simulation-based 3D printed models: exploring surgical performance and learning curves.

A cost-effective milestone training model based on 3D printed for laparoscopic Roux-en-Y gastric bypass.

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.

The increasing complexity and difficulty of surgical procedures have led to a rise in medical errors within clinical settings in recent years. Gastrointestinal diseases, in particular, present significant medical challenges and impose substantial economic burdens, underscoring the urgent need for experiential, high-fidelity gastrointestinal surgical training tools. This study leverages patient-specific computed tomography (CT) and magnetic resonance imaging (MRI) data, combined with 3D printed manufacturing, to develop hydrogel organ models with tunable performance and tissue-mimicking softness. These properties are achieved by regulating the freeze-thaw cycles, cross-linking agents, and the concentration of incorporated antibacterial nanoparticles in DN hydrogels. Through the application of indirect 3D printing and the "sacrificial material method", we successfully fabricate organ tissues such as the stomach, intestines, and blood vessels with high precision. In ex vivo surgical training demonstrations, these tissue-like soft hydrogels provide an effective platform for preoperative simulation and surgical training in digestive surgery, accommodating various surgical procedures and accurately simulating intraoperative bleeding. The development of advanced bionic organ models with specific and tunable characteristics based on DN hydrogels is poised to significantly advance surgical training, medical device testing, and the reform of medical education.

Versatile and Tunable Performance of PVA/PAM Tridimensional Hydrogel Models for Tissues and Organs: Augmenting Realism in Advanced Surgical Training.

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.

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.

Article	Year
Validity of a Soft and Flexible 3D-Printed Nissen Fundoplication Model in Surgical Training. International journal of bioprinting\| PubMed ID: 35669328	2022
Development and evaluation of a portable and soft 3D-printed cast for laparoscopic choledochojejunostomy model in surgical training. BMC medical education\| PubMed ID: 36721193	2023
Sources of information on surgical options for patients undergoing antireflux surgery: a single-center questionnaire-based survey. BMC gastroenterology\| PubMed ID: 36918773	2023
Multifunctional high-simulation 3D-printed hydrogel model manufacturing engineering for surgical training. International journal of bioprinting\| PubMed ID: 37457926	2023
Assessment of laparoscopic intracorporeal intestinal anastomosis training using simulation-based 3D printed models: exploring surgical performance and learning curves. International journal of surgery (London, England)\| PubMed ID: 37498142	2023
A cost-effective milestone training model based on 3D printed for laparoscopic Roux-en-Y gastric bypass. American journal of surgery\| PubMed ID: 37838504	2024
Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease. Computers in biology and medicine\| PubMed ID: 38657464	2024
Versatile and Tunable Performance of PVA/PAM Tridimensional Hydrogel Models for Tissues and Organs: Augmenting Realism in Advanced Surgical Training. ACS applied bio materials\| PubMed ID: 39194173	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 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

Jinlei Mao

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