Screen4SpLDs - Development of an Automated Pre-Screening Tool for Specific Learning Disabilities in Children.

Screen4SpLDs - 开发针对儿童特定学习障碍的自动预筛查工具。

• 批准号: EP/Y002121/1
• 负责人: PRATHEEPAN YOGARAJAH
• 金额: $ 21.76万
• 依托单位: University of Ulster
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY002121%2F1
• 关键词: Screen4SpLDs; Development; Automated; Pre; Screening

Specific Learning Disabilities (SpLDs) is a common term in today's society, which manifests in different ways and can cause various difficulties in daily life. For one person it might be the lack of attention, for another, it might be struggling to read fluently or conduct basic mathematical calculations; these are different groups of Learning Disabilities. Early detection and treatment of SpLDs are crucial, as it enables the start of interventions that support the best outcomes for children living with SLDs. Not addressing SLDs at a young age has a major influence on development into adulthood and results in a high economic cost, exceeding the lifetime costs of asthma, intellectual disability, and diabetes have a huge shortage of special educators to conduct SLDs screening and subsequently providing treatment post diagnosis. There are nearly 90% of the world's children reside in Low and Middle-Income Countries (LMICs). The challenge of early detection and early intervention of SpLDs is exacerbated by limited expertise, including limited screening, diagnostic and treatment resources in LMICs. For instance, in the Global South, the skilled human resource and tools to assess SpLDs are very limited. Thus, these children are undiagnosed and negatively reinforced by the community by stigmatizing and labelling them. These factors all lead to low self-esteem and behavior problems that further interfere with their ability to learn. In vulnerable communities, which are often already poverty stricken, this operates as a vicious cycle, simply because optimal education is the main method of breaking this vicious cycle. We aim to target these developmental issues by developing and piloting low-cost mobile app-based solution for the screening of SpLDs that will lead to early intervention. Specific learning disorder may affect handwriting in a way that can be visually distinguished. The purpose of the proposed research is to evaluate the ability of deep learning to distinguish between those who have SpLDs and those who do not, from their handwriting. The proposed solution requires no more than taking a photo of the handwritten image on a mobile phone and passing it to the prediction model and getting the prediction results.Based on the proposed solution, the SpLDs screening can be conducted at home, in a school study area without any additional special setting. The important factors of this app are simplicity, ease of use, less training requirement, the accuracy of the results, and reliability. This app can serve from individual to national level for screening SpLDs in children. This will reduce the burden of the shortage of special educators, and this will be a huge relief for LMICs.This will, in general, reduce the inequalities faced by vulnerable and marginalized children, by providing an opportunity to receive optimal health and educational services. This will lead to the improvement of quality education received by ALL which in turn will contribute to wider societal improvements. In addition to the direct impact on the child, the spillover effects on the family and community development are significant. Further, creating an opportunity to screen a larger population will increase societal awareness of SpLDs and reduce the stigma and enhance public and parent involvement and engagement in managing SpLDs and supporting the affected families.

特定学习障碍（SpLD）是当今社会的一个常见术语，它以不同的方式表现出来，并可能在日常生活中造成各种困难。对于一个人来说，可能是缺乏注意力，对于另一个人来说，可能是难以流利地阅读或进行基本的数学计算；这些是不同的学习障碍群体。 SpLD 的早期发现和治疗至关重要，因为它可以启动干预措施，为患有 SLD 的儿童提供最佳结果。在年轻时不解决 SLD 会对成年期的发育产生重大影响，并导致高昂的经济成本，超过哮喘、智力障碍和糖尿病的终生成本。 严重缺乏特殊教育工作者来进行 SLD 筛查并随后提供治疗诊断后。世界上近 90% 的儿童居住在低收入和中等收入国家 (LMIC)。专业知识有限，包括中低收入国家筛查、诊断和治疗资源有限，加剧了 SpLD 早期发现和早期干预的挑战。例如，在南半球国家，用于评估 SpLD 的熟练人力资源和工具非常有限。因此，这些儿童没有得到诊断，并且社区通过污蔑和贴标签来消极强化他们。这些因素都会导致自卑和行为问题，进一步干扰他们的学习能力。在往往已经陷入贫困的弱势社区，这是一个恶性循环，因为最佳教育是打破这种恶性循环的主要方法。我们的目标是通过开发和试点基于移动应用程序的低成本解决方案来筛查 SpLD，从而实现早期干预，从而解决这些发育问题。特定的学习障碍可能会以视觉上可区分的方式影响笔迹。拟议研究的目的是评估深度学习从笔迹中区分患有 SpLD 和未患有 SpLD 的人的能力。所提出的解决方案只需在手机上拍摄手写图像的照片并将其传递给预测模型并获得预测结果。基于所提出的解决方案，SpLD 筛选可以在家里、在学校学习中进行区域，无需任何额外的特殊设置。该应用程序的重要因素是简单、易于使用、培训要求少、结果的准确性和可靠性。该应用程序可用于从个人到国家层面的儿童 SpLD 筛查。这将减轻特殊教育工作者短缺的负担，对中低收入国家来说将是一个巨大的缓解。总的来说，这将通过提供获得最佳健康和教育服务的机会来减少弱势和边缘化儿童所面临的不平等。这将提高所有人接受的优质教育，进而促进更广泛的社会进步。除了对儿童的直接影响外，对家庭和社区发展的溢出效应也很显着。此外，创造一个筛查更多人群的机会将提高社会对 SpLD 的认识，减少耻辱感，并加强公众和家长对 SpLD 管理和支持受影响家庭的参与和参与。