Optimizing Peripheral Nerve Regeneration using Computational Intelligence based T

使用基于计算智能的 T 优化周围神经再生

基本信息

批准号：
8232817
负责人：
XIAOJUN YU
金额：
$ 42.38万
依托单位：
STEVENS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-15 至 2015-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8232817
关键词：
Advanced Development Affect Algorithms Animal Experiments Animals Ants Area Artificial Intelligence Autologous Transplantation Biocompatible Materials Biological Biological Neural Networks Biomedical Research Breathing Cells Characteristics Classification Data Data Analyses Data Set Decision Trees Defect Disease Exhibits Foundations Future Generic Drugs Individual Insecta Intelligence Knowledge Learning Maps Methods Modeling Morbidity - disease rate Nerve Nerve Regeneration Network-based Neural Network Simulation Operative Surgical Procedures Organ Patients Pattern Perception Peripheral Nerves Peripheral nerve injury Phase Physicians Process Property Psychological reinforcement Rattus Research Research Personnel Research Project Grants Sampling Social Behavior Structure Surgical incisions System Techniques Time Tissue Engineering Tissues Training Treatment Protocols Trees Vertebrates Weight Work base experience in vivo in vivo Model insight nerve gap nerve injury novel particle relating to nervous system sciatic nerve social tool vector

项目摘要

DESCRIPTION (provided by applicant): Peripheral nerve injuries are common diseases that affect a large amount of patients every year. Tissue engineering has emerged as a powerful approach for developing alternative nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. Some research groups have applied artificial neural networks and decision trees to obtain the best model configuration for the prediction of the tissue engineering strategies. For the decision trees based methods, it is hard to tell which classification tree is better than the other. Furthermore, the prediction system using the decision tree algorithm lacks the capability of accumulating the learning experience over time. On the other hand, Artificial Neural Networks (ANNs) exhibit some remarkable properties, but only the connection weights are trained with fixed topology. It is hard to find the best fixed topology in advance for each specific tissue engineering strategy. In this proposal, swarm intelligence (SI) based evolving ANNs technique is proposed to tackle this challenge. Two swarm intelligence based methods, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), will be applied in this project to train the ANN model. More specifically, ACO will be used to optimize the topology structure of the ANN models, while the PSO is used to adjust the connection weights of the ANN models based on the optimized topology structure. For this SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) system, both topology and connection weight of artificial neural networks can be evolved automatically and simultaneously so that an optimal classifier for tissue engineering strategies in peripheral nerve regeneration can be achieved. The research project will include the following phases: Aim 1: Predict tissue engineering strategies in peripheral nerve regeneration using SWarm Intelligence based Reinforcement Learning method for ANNs (SWIRL-ANN) analytical and prediction system. Aim 2: Validate the efficacy of novel unknown tissue engineered nerve grafts as predicted by using SWIRL-ANN based analytical and prediction system for bridging peripheral nerve gaps in rat sciatic nerve injury model in vivo. PUBLIC HEALTH RELEVANCE: Tissue engineering has emerged as a powerful approach for developing nerve grafts for peripheral nerve regeneration. Since tissue engineering strategies in peripheral nerve regeneration involve various possible combinations of variables, it is necessary to develop efficient tools to identify optimal tissue engineering strategies and predict the experimental results based on these tissue engineering strategies for peripheral nerve regeneration. In this proposal, swarm intelligence (SI) based evolving artificial neural networks (ANNs) technique is proposed to tackle this challenge. The proposed research will be helpful to efficiently develop tissue engineered products for tissue and organ replacement.

描述（由申请人提供）：周围神经损伤是每年影响大量患者的常见疾病。组织工程已成为开发外周神经再生的替代神经移植物的强大方法。由于周围神经再生中的组织工程策略涉及变量的各种可能组合，因此有必要开发有效的工具来识别最佳组织工程策略，并根据这些组织工程策略来预测外围神经再生的实验结果。一些研究小组应用了人工神经网络和决策树，以获得最佳的模型配置，以预测组织工程策略。对于基于决策树的方法，很难说出哪种分类树比另一种更好。此外，使用决策树算法的预测系统缺乏随着时间的推移积累学习体验的能力。另一方面，人工神经网络（ANN）具有一些显着的特性，但仅通过固定拓扑训练连接权重。对于每种特定的组织工程策略，很难提前找到最佳的固定拓扑。在此提案中，提出了基于群体智能（SI）不断发展的ANNS技术来应对这一挑战。该项目将应用两种基于群体智能的方法，即蚂蚁菌落优化（ACO）和粒子群优化（PSO），以训练ANN模型。更具体地说，ACO将用于优化ANN模型的拓扑结构，而PSO用于根据优化的拓扑结构来调整ANN模型的连接权重。对于基于智能的ANNS（Swirl-ANN）系统的基于智能的增强学习方法，可以自动且同时自动进化人造神经网络的拓扑和连接重量，以便可以实现外围神经再生中组织工程策略的最佳分类器。该研究项目将包括以下阶段：目标1：使用基于群体智能的ANN（Swirl-ANN）分析和预测系统的基于智能的强化学习方法来预测周围神经再生的组织工程策略。 AIM 2：通过使用基于旋转的ANN的分析和预测系统预测，验证新型未知组织工程神经移植物的功效，以在大鼠坐骨神经损伤模型中弥合周围神经间隙。公共卫生相关性：组织工程已成为开发周围神经再生神经移植物的强大方法。由于周围神经再生中的组织工程策略涉及变量的各种可能组合，因此有必要开发有效的工具来识别最佳组织工程策略，并根据这些组织工程策略来预测外围神经再生的实验结果。在这项建议中，提出了基于群体智能（SI）不断发展的人工神经网络（ANN）技术来应对这一挑战。拟议的研究将有助于有效开发组织和器官置换的组织工程产品。