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1. Patient Decision Making for Traditional Vs. 3D Printing-Based Meniscus Transplantation

   Zhang, Z.; Wang, S. (May 2017, Industrial and systems engineering)

   Every year about 1.5 million people have surgery to treat meniscal tears across the US and Europe. Traditionally, meniscus transplantation is the primary treatment in the long term. 3D printing is a substitute to the traditional transplantation method. With its previous contribution to tooth crowns, hearing aids and other life science industries, 3D printing has shown to be successful. In this article, we would like to investigate the feasibility of adopting 3D printing on meniscus in terms of supply chain cost and patient cost. We use data collected from online resources, literature citations and making assumptions where necessary. The analysis is carried in two directions: first, cost models for traditional transplantation and 3D printing-based transplantation in patients’ perspective are developed. Second, a hypothesized pathway model is created to analyze post-transplantation cost and risk for patients. Simulation based on the pathway model will be done to estimate parameters of the model. Meanwhile, we use a Markov model to study the potential post-transplantation risks which may induce additional cost to patients. Our results will help hospitals in making decisions on the introduction of 3D printing systems. 

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2. Modeling-Based Assessment of 3D Printing-Enabled Meniscus Transplantation

   https://doi.org/10.3390/healthcare7020069  

   Zhang, Zimeng; Wu, Qian; Zeng, Li; Wang, Shiren (June 2019, Healthcare)

   3D printing technology is able to produce personalized artificial substitutes for patients with damaged menisci. However, there is a lack of thorough understanding of 3D printing-enabled (3DP-enabled) meniscus transplantation and its long-term advantages over traditional transplantation. To help health care stakeholders and patients assess the value of 3DP-enabled meniscus transplantation, this study compares the long-term cost and risk of this new paradigm with traditional transplantation by simulation. Pathway models are developed to simulate patients’ treatment process during a 20-year period, and a Markov process is used to model the state transitions of patients after transplantation. A sensitivity analysis is also conducted to show the effect of quality of 3D-printed meniscus on model outputs. The simulation results suggest that the performance of 3DP-enabled meniscus transplantation depends on quality of 3D-printed meniscus. The conclusion of this study is that 3DP-enabled meniscus transplantation has many advantages over traditional meniscus transplantation, including a minimal waiting time, perfect size and shape match, and potentially lower cost and risk in the long term. 

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3. Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys

   https://doi.org/10.3982/ECTA20203  

   Agarwal, Nikhil; Hodgson, Charles; Somaini, Paulo (January 2025, Econometrica)

   While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice‐based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life‐years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post‐transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest. 

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4. Suitability of 3D-Printed devices for low-temperature geochemical T experiments

   https://doi.org/doi.org/10.1016/j.apgeochem.2018.08.012  

   Kletetschka, K; Rimstidt, J Donald; Long, Timothy E; Michel, F Marc (August 2018, Applied geochemistry)

   Desktop 3D printing stereolithography (SLA) is a fabrication technique based on photopolymerization that can be used to efficiently create novel reaction devices for laboratory geochemistry with complex features (e.g. internal channels, small volumes) that are beyond the capabilities of traditional machining methods. However, the stability of 3D printed parts for low-temperature aqueous geochemical conditions has not been carefully evaluated. Furthermore, it is unclear what criteria should be used when attempting to optimize the mechanical and chemical properties during post-processing steps. Addressing these challenges is important for determining the suitability of 3D printed devices for laboratory investigations such as mineral precipitation/dissolution ex- periments. Here, we use thermogravimetric analysis (TGA) profiles, dynamic mechanical analysis (DMA), and chemical extraction of leachables to show how ultraviolet (UV) post-curing can optimize properties of a com- mercial photo-reactive resin (Formlabs Standard Clear). The mechanical and chemical stability of the post-cured material was enhanced and a working temperature of up to 80 °C was determined. We further provide data showing the stability and compatibility of the material in aqueous conditions of pH 0, 5.7 and 12. As SLA 3D printing is still an emerging and rapidly developing technology, the method presented here will provide a fra- mework for assessing how new printer types and materials (i.e. resins) impact the suitability of SLA printed devices for future experimental studies. 

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5. Integrating Surgeon Expertise and AI: The Transplant Surgeon Fuzzy Associative Memory Model for Enhanced Kidney Transplant Decision-Making

   https://doi.org/10.1016/j.ajt.2025.07.824  

   Dzieran, R; Dagli, C; Randall, H; Marley, R; Canfield, C; Lentine, K; Schnitzler, M (August 2025, American Journal of Transplantation)

   (TSFAM) model, an adaptive human-AI teaming framework designed to enhance hard-to-place kidney acceptance decision-making by integrating transplant surgeons’ individualized expertise with advanced AI analytics (Figure 1). Methods: TSFAM is an innovative solution for complex issues in kidney transplant decision-making support. It employs fuzzy associative memory to capture and codify unique decision-making rules of transplant surgeons. Using the Deceased Donor Organ Assessment (DDOA) and Final Acceptance AI models designed to evaluate hard-to-place kidneys, TSFAM integrates fuzzy logic with deep learning techniques to manage inherent uncertainties in donor organ assessments. Surgeon-specifi c ontologies and membership functions are extracted through interviews. Similar to how a pain scale is used for understanding patients, an ontology ambiguity scale is used to develop surgeon rules (Figure 2). Fuzzy logic captures ambiguity and enables the model to adapt to evolving clinical, environmental, and policy conditions. The structured incorporation of human expertise ensures decision support remains closely aligned with local clinical practices and global best evidence. Results: This novel framework incorporates human expertise into AI decisionmaking tools to support donor organ acceptance in transplantation. Integrating surgeon-defi ned criteria into a robust decision-support tool enhances accuracy and transparency of organ allocation decision-making support. TSFAM bridges the gap between data-driven models and nuanced judgment required in complex clinical scenarios, fostering trust and promoting responsible AI adoption. Conclusions: TSFAM fuses deep learning analytics with subtleties of human expertise for a promising pathway to improve decision-making support in transplant surgery. The framework enhances clinical assessment and sets a precedent for future systems prioritizing human-AI collaboration. Prospective studies will focus on clinical implementation with dynamic interfaces for a more patient-centered, evidencebased model in organ transplantation. The intent is for this approach to be adaptable to individual case scenarios and the diverse needs of key transplant team members 

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