Computer aided diagnosis of neurological damage to improve care for infants born prematurely

计算机辅助诊断神经损伤以改善对早产儿的护理

• 批准号: EP/I000445/1
• 负责人: Daniel Rueckert
• 金额: $ 130.41万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI000445%2F1
• 关键词: Computer; aided; diagnosis; neurological; damage

Preterm birth is a major cause of neuropsychiatric impairment in childhood and leads to significant long-term clinical, educational and social problems. The incidence of preterm birth and low birth weight has increased over the last decade in industrialised countries, and preterm delivery has a higher prevalence among the unemployed and poorly educated. The burden of impairment is considerable: about 10% of all infants born before 33 weeks of age develop cerebral palsy; over 30% have neurocognitive problems; and half of all surviving infants born at 25 weeks or less show neurodevelopmental impairment at 30 months of age. These problems persist into later life which can have devastating consequences for the individuals and their families. A major issue confronting clinicians who work with preterm infants and their families is the identification of infants who are most at risk for subsequent neurodevelopmental disability and who may benefit from early intervention services. Improved prediction of later handicaps has the potential immediately to improve the delivery of care for preterm infants and their families. At the same time, the improved diagnosis will also aid the growing search for specific treatments to reduce brain injury. Several promising approaches are under active investigation, all of which rely or would be aided by improved diagnosis of adverse outcomes. Currently, the early assessment of brain development in preterm infants and prognosis of outcome is heavily dependent on a subjective assessment of clinical and low resolution imaging data. The aim of this project is the creation of tools and algorithms that enable the detection and diagnosis of abnormal brain development based on high-resolution magnetic resonance imaging (MRI) information. By interpreting these images within an evidence-based statistical framework, a more complete and objective, evidence-based interpretation will be possible. The project will combine two emerging paradigms in computer and imaging science to address the challenge of identifying abnormal brain development and predicting outcome: Machine learning techniques and computational anatomy. In combination these approaches have the potential to provide useful and descriptive models of the underlying anatomy that can be used for comparisons across subjects and over time. This offers the possibility to learn patterns of normal and abnormal brain development and to predict the pattern of future brain development. The result of the research will be a significantly improved ability to predict neurodevelopmental outcome in later life. The ability to predict outcome improves parental counseling and selection of infants for early therapeutic strategies aiming at preventing or ameliorating cerebral injury.

早产是儿童期神经精神损害的主要原因，并导致重大的长期临床、教育和社会问题。在过去十年中，工业化国家早产和低出生体重的发生率有所增加，早产在失业和受教育程度低的人群中更普遍。损伤的负担相当大：33周前出生的所有婴儿中，约有10%患有脑瘫;超过30%的婴儿存在神经认知问题; 25周或以下出生的所有存活婴儿中，有一半在30个月大时表现出神经发育障碍。这些问题持续到晚年，可能对个人及其家庭造成破坏性后果。临床医生面临的一个主要问题是谁的工作与早产儿和他们的家庭是识别婴儿谁是最有风险的后续神经发育障碍，谁可能受益于早期干预服务。改善对后期障碍的预测有可能立即改善对早产儿及其家庭的护理。同时，改进的诊断也将有助于寻找减少脑损伤的特定治疗方法。几种有前途的方法正在积极研究中，所有这些方法都依赖于或将有助于改善不良结局的诊断。目前，早产儿脑发育的早期评估和预后结果严重依赖于临床和低分辨率成像数据的主观评估。该项目的目的是创建工具和算法，以便基于高分辨率磁共振成像（MRI）信息检测和诊断异常大脑发育。通过在基于证据的统计框架内解释这些图像，将有可能进行更完整和客观的基于证据的解释。该项目将联合收割机结合计算机和成像科学中的两种新兴范式，以应对识别异常大脑发育和预测结果的挑战：机器学习技术和计算解剖学。结合这些方法，有可能提供有用的和描述性的模型，可用于比较跨学科和随着时间的推移的基础解剖结构。这提供了学习正常和异常大脑发育模式并预测未来大脑发育模式的可能性。这项研究的结果将显著提高预测晚年神经发育结果的能力。预测结果的能力改善了父母的咨询和选择婴儿的早期治疗策略，旨在预防或改善脑损伤。

项目成果

Regional growth and atlasing of the developing human brain.

• DOI: 10.1016/j.neuroimage.2015.10.047
• 发表时间: 2016-01-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Makropoulos A;Aljabar P;Wright R;Hüning B;Merchant N;Arichi T;Tusor N;Hajnal JV;Edwards AD;Counsell SJ;Rueckert D
• 通讯作者: Rueckert D