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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

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.

Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact humanity. 3D printing of patient-specific organ models is expected to replace animal carcasses, providing scenarios that simulate the surgical environment for preoperative training and educating patients to propose effective solutions. Due to the complexity of 3D printing manufacturing, it is still used on a small scale in clinical practice, and there are problems such as the low resolution of obtaining MRI/CT images, long consumption time, and insufficient realism. AI has been effectively used in 3D printing as a powerful problem-solving tool. This paper introduces 3D printed organ models, focusing on the idea of AI application in 3D printed manufacturing of organ models. Finally, the potential application of AI to 3D-printed organ models is discussed. Based on the synergy between AI and 3D printing that will benefit organ model manufacturing and facilitate clinical preoperative training in the medical field, the use of AI in 3D-printed organ model making is expected to become a reality.

Application of artificial intelligence in 3D printing physical organ models.

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.

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.

Article	Year
Multifunctional high-simulation 3D-printed hydrogel model manufacturing engineering for surgical training. International journal of bioprinting\| PubMed ID: 37457926	2023
Application of artificial intelligence in 3D printing physical organ models. Materials today. Bio\| PubMed ID: 37746667	2023
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 high-water retention, self-healing hydrogel thyroid model for surgical training. Materials today. Bio\| PubMed ID: 39624054	2024

Shijie Yu

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