Artificial neural network analysis of noisy visual field data in glaucoma

Henson, David B.; Spenceley, S.E.; Bull, David

doi:10.1016/s0933-3657(97)00388-6

Cited by 36 publications

(18 citation statements)

References 11 publications

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“…18,19 Henson et al 18 used a Kohonen self-organizing map to classify fields based on the pattern and severity of defect. The emphasis on pattern and severity is similar to our approach, but while Henson et al gave examples of how this approach might be used for progression, they did not analyze serial fields from their participants.…”

Section: Discussionmentioning

confidence: 99%

“…As noted before, there is no agreement on such a standard. Finally, both Brigatti et al 19 and Henson et al 18,20 used supervised learning, that trained the classifiers to identify progression based on AGIS criteria in the first case and defined defects with variability modeling in the second case. The vB-ICA-mm is unsupervised and classifies the data without any training or biases.…”

Section: Discussionmentioning

confidence: 99%

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Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields

Sample¹,

Boden²,

Zhang

et al. 2005

Invest. Ophthalmol. Vis. Sci.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields

Sample¹,

Boden²,

Zhang

et al. 2005

Invest. Ophthalmol. Vis. Sci.

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“…This model which was subsequently validated in prospective studies had a diagnostic accuracy of 90%, with a sensitivity of 81% and specificity of 92%. Some of the other surgically relevant diagnostic applications of ANNs include abdominal pain and appendicitis, 16 retained common bile duct stones, 17 glaucoma, 18 and back pain. 19 ANNs have also been used in diagnosing cytological and histological specimens.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence in medicine

Ramesh

Kambhampati²,

Monson³

et al. 2004

Ann R Coll Surg Engl

692

396

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A rtificial intelligence (AI) is defined as 'a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behaviour, and with the creation of artefacts that exhibit such behaviour'.1 Aristotle attempted to formalise 'right thinking' (logic) through his syllogisms (a three part deductive reasoning). Much of the work in the modern era was inspired by this and the early studies on the operation of mind helped to establish contemporary logical thinking. Programs which enable computers to function in the ways, that make people seem intelligent are called artificial intelligent systems. The British mathematician Alan Turing (1950) was one of the founders of modern computer science and AI. He defined intelligent behaviour in a computer as the ability to achieve human-level performance in cognitive tasks, this later became popular as the 'Turing test'.2 Since the middle of the last century, researchers have explored the potential applications of intelligent techniques in every field of medicine.3,4 The application of AI technology in the field of surgery was first successively investigated by Gunn in 1976, when he explored the possibility of diagnosing acute abdominal pain with computer analysis. 5 The last two decades have seen a surge in the interest in medical AI.Modern medicine is faced with the challenge of acquiring, analysing and applying the large amount of knowledge necessary to solve complex clinical problems. The development of medical artificial intelligence has been related to the development of AI programs intended Artificial intelligence in medicine AN

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“…Some approaches cluster the locations based on known physiological retinal cell maps [18,24], and apply regression to the mean of the clusters [20,22]. Others use neural networks as classifiers, relying on the network to learn any spatial relations [3,11,16, and references therein].…”

Section: Determining Progressionmentioning

confidence: 99%

Determining Progression in Glaucoma Using Visual Fields

Turpin

Frank

Hall

et al. 2001

Advances in Knowledge Discovery and Data Mining

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Abstract. The standardized visual field assessment, which measures visual function in 76 locations of the central visual area, is an important diagnostic tool in the treatment of the eye disease glaucoma. It helps determine whether the disease is stable or progressing towards blindness, with important implications for treatment. Automatic techniques to classify patients based on this assessment have had limited success, primarily due to the high variability of individual visual field measurements. The purpose of this paper is to describe the problem of visual field classification to the data mining community, and assess the success of data mining techniques on it. Preliminary results show that machine learning methods rival existing techniques for predicting whether glaucoma is progressing-though we have not yet been able to demonstrate improvements that are statistically significant. It is likely that further improvement is possible, and we encourage others to work on this important practical data mining problem.

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Artificial neural network analysis of noisy visual field data in glaucoma

Cited by 36 publications

References 11 publications

Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields

Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields

Artificial intelligence in medicine

Determining Progression in Glaucoma Using Visual Fields

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