Data Integrity and Intelligent Data Analysis Techniques Applied to a Glaucoma Progression Dataset

青光眼是一种影响人类眼睛的疾病，是一系列相关眼睛疾病的总称。这些疾病的一个共同特征是视网膜和视神经的功能异常，导致视野丧失。这种视力丧失通常只是视野的一部分，尽管青光眼如果不治疗，往往会导致失明。据认为，到2010年，全球将有6000多万人患有各种形式的青光眼。视野检查对所有类型的青光眼的诊断和治疗都至关重要。这类测试需要在多个点上采样视网膜对光的敏感度，根据测试类型的不同，通常在50到100之间；然后为眼睛分配一个数值，范围从“无感知”(最低)到“完美感知”(最高)。一台专门的机器被用来进行这些测试--一次典型的临床测试每只眼睛可能需要6到7分钟。一旦被诊断为青光眼或疑似青光眼，就会对患者进行监测，并建议他们每六个月进行一次同样的检查(在某些情况下，检查频率会更高)。然而，由于青光眼测试的心理物理性质，结果可能会在质量上有很大的不同。例如，他们可能会受到患者疲劳(因为测试可能持续很长时间)和注意力持续缺陷(特别是在老年人和儿童中)的影响。这项建议旨在使用数据质量指标(如假阳性率和负性率)将不确定性纳入计算模型，该模型还将考虑视野数据的空间和时间性质。这项拟议的研究将使用概率细胞自动机(CA)和适当的规则学习方法作为建模青光眼患者视野恶化的技术。该项目的目的是准确地模拟视野进展，并为临床医生提供帮助。该项目是与英国伦敦穆尔菲尔德眼科医院合作的。

Glaucoma is a condition that affects the human eye and is an umbrella term for a family of related eye conditions. A common trait of these conditions is a functional abnormality of the retina and optic nerve, leading to loss of visual field. This vision loss is usually only part of the visual field, although, untreated, glaucoma often leads to blindness. It is thought that by 2010, there will be over 60 million people worldwide suffering from the various forms of Glaucoma. Visual field tests are crucial to the diagnosis and management of all types of glaucoma. Such tests require the level of retinal sensitivity to light to be sampled at a number of points typically between 50 and 100, depending on the type of test; the eye is then assigned a numerical value in the range 'no perception' (lowest) to 'perfect perception' (highest). A specialised machine is used to conduct these tests - a typical clinical test can take between six and seven minutes per eye. Once diagnosed with glaucoma or suspected glaucoma, a patient is monitored and recommended to undergo the same tests every six months (or more frequently in some cases). However due to the psychophysical nature of the glaucoma test, the results can therefore vary in quality dramatically. For example, they can be affected by patient fatigue (as the test can last for long periods) and attention span deficits (particularly in the elderly and children).This proposal aims to use data quality metrics (such as false positive and negative rates) to incorporate uncertainty into computational models that will also take into account the spatial and temporal nature of visual field data. The proposed research will use probabilistic Cellular Automata (CA) with an appropriate rule learning approach as the technique to model the visualfield deterioration of glaucoma sufferes. The aim is to accurately model visual field progression and to provide an aid to the clinical practitioners.This project is in collaboration with Moorfields Eye Hospital, London, UK.