Combining computational techniques with movement data to predict adult autism diagnosis

将计算技术与运动数据相结合来预测成人自闭症诊断

• 批准号: 2501675
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2501675
• 关键词: Combining; computational; techniques; movement; data

Autism is a life-long developmental condition that affects how a person communicates and interacts with people. In addition to these social aspects, ~80% of autistic individuals have coordination difficulties such as poor eye-hand coordination, unstable balance and unusual gait. The healthcare aim of this project is to uncover whether these coordination difficulties can be used to diagnose autistic adults. Currently, diagnosis of autistic adults is difficult and time consuming and autistic adults have placed the need for earlier and improved diagnosis in their top 10 research priorities. This is because existing diagnostic criteria have not been validated in an adult population, autistic adults have developed compensatory strategies and the subjective nature of the observational inventories mean that diagnosis can vary between clinicians. Consequently, access to valuable support is delayed. This project will combine motion tracking data collected during movement tasks with data science methods to investigate whether an automated test based on coordination skills could provide added value for diagnostic precision, when used in combination with current observational inventories. Using movement tasks to diagnose adults is advantageous over current methods as coordination difficulties occur throughout the lifespan and movement can be measured quantitatively and objectively, providing a rich dataset to identify discriminating features. Our recent published EPSRC-funded work demonstrates the potential of this approach: Machine Learning (ML) techniques were successfully applied to motion tracking data from 44 autistic and non-autistic participants. We now need to create new models on a wider repertoire of movements and larger sample size to enable identification of consistent motor patterns that will increase classification accuracy.ObjectivesObjective 1: To collect data on a wider range of motor tasks and develop robust classification tools Objective 2: To collect data and test the robustness of the models on a larger group of autistic individuals, as well as those with motor disorders (Parkinson's Disease (PD), Developmental Coordination Disorder (DCD)).Objective 3: To identify whether coordination difficulties can be divided into different subgroups, using deep clustering approaches.Approach Autistic and non-autistic adults will perform different actions (e.g. grasping a cup, walking, balancing) while motion sensors track their movements. Algorithms will be developed to extract the relevant movement parameters for each task. First, group means and discriminative features for classification will be identified for each movement task using feature extraction and selection methods. Second, supervised learning, classification and deep neural learning methods will be investigated to generate robust classifiers, which enable the detection of autistics from controls. We will continue our recent developments on interpretable deep learning (understanding why the network made a particular decision) by comparing variability based classical statistics with deep learning approaches. New autistic and non-autistic participants will then be used to test the robustness of models along with participants with PD and DCD. Clustering techniques such as K-nearest neighbours will be used to identify any subgroups within the autistic data, which differ from non-autistic individuals. Novelty/Potential outcomes The result will be a prototype ML/artificial intelligence tool to identify those at risk from having autism, supporting clinical decision making and leading to earlier and quicker diagnosis. In addition, the project will develop novel analytical science tools using ML and deep neural learning to create robust models. The research aligns with the grand challenge within the Healthcare Technologies theme: "Optimising Treatment: Optimising care through effective diagnosis, patient-specific prediction and evidence-based intervention"

自闭症是一种终生的发展状况，影响一个人如何与人沟通和互动。除了这些社会方面，约80%的自闭症患者有协调困难，如眼手协调差，平衡不稳定和步态不正常。该项目的医疗保健目的是揭示这些协调困难是否可以用于诊断自闭症成年人。目前，自闭症成年人的诊断是困难和耗时的，自闭症成年人已经把早期和改善诊断的需要放在他们的十大研究重点。这是因为现有的诊断标准尚未在成年人群中得到验证，自闭症成年人已经制定了补偿策略，并且观察清单的主观性质意味着临床医生之间的诊断可能会有所不同。因此，获得宝贵支助的机会受到拖延。该项目将联合收割机在运动任务中收集的运动跟踪数据与数据科学方法相结合，以研究基于协调技能的自动化测试是否可以为诊断精度提供附加值，当与当前的观测库存相结合时。使用运动任务来诊断成年人比现有方法更有优势，因为协调困难在整个生命周期中都会发生，并且可以定量和客观地测量运动，从而提供丰富的数据集来识别区分特征。我们最近发表的EPSRC资助的工作证明了这种方法的潜力：机器学习（ML）技术成功应用于44名自闭症和非自闭症参与者的运动跟踪数据。我们现在需要在更广泛的动作和更大的样本量上创建新的模型，以识别一致的运动模式，从而提高分类准确性。目标目标1：收集更广泛的运动任务数据，并开发强大的分类工具目标2：收集数据，并在更大的自闭症患者和运动障碍患者群体中测试模型的鲁棒性（帕金森氏病（PD），发展性协调障碍（DCD））。目标3：使用深度聚类方法确定协调困难是否可以分为不同的亚组。方法自闭症和非自闭症成年人将执行不同的动作（例如抓杯子，走路，平衡），同时运动传感器跟踪他们的运动。将开发算法来提取每个任务的相关运动参数。首先，使用特征提取和选择方法为每个运动任务识别用于分类的组均值和判别特征。其次，将研究监督学习、分类和深度神经学习方法，以生成强大的分类器，从而能够从对照组中检测出自闭症患者。我们将通过比较基于变异性的经典统计与深度学习方法，继续我们最近在可解释深度学习（理解网络为什么做出特定决策）方面的发展。然后，新的自闭症和非自闭症参与者将被用于测试模型的稳健性，沿着有PD和DCD的参与者。聚类技术，如K-最近邻将用于识别自闭症数据中的任何亚组，这些亚组与非自闭症个体不同。新奇/潜在成果结果将是一个原型ML/人工智能工具，用于识别那些有自闭症风险的人，支持临床决策，并导致更早更快的诊断。此外，该项目还将使用ML和深度神经学习开发新型分析科学工具，以创建强大的模型。该研究符合医疗保健技术主题中的重大挑战：“优化治疗：通过有效诊断，患者特异性预测和循证干预优化护理”