Characterization of functional and structural deficits in a canine model of compressive optic neuropathy using optical coherence tomography and pattern electroretinography

Purpose: To evaluate functional and structural deficits in a canine model of compressive optic neuropathy (CON). Methods: CON was induced in healthy beagles by implanting a silicone implant into the orbit and inducing optic nerve compression for 24 hours. Retinal nerve fiber layer (RNFL) thickness was evaluated using optical coherence tomography (OCT). Pattern electroretinography (pERG) was performed to evaluate retinal ganglion cell (RGC) function 10 minutes and 30, 90 and 180 days after CON induction. Results: Optic nerve compression resulted in significant immediate pERG deficits (P50-N95=0.4+0.1yV; mean+SEM) when compared to control (6.2+0.4 yV; p\u3c0.0001). Analysis of OCT scans in the area centralis immediately after compression showed significant increase in RNFL thickness in CON dogs (39.5+1.8 ym) when compared to control values (26.4+1.5 ym, p\u3c0.0001). Increased area centralis RNFL thickness correlated significantly with pERG deficits (r2= 0.43, p=0.03). Analysis of peripapillary RNFL showed significantly decreased thickness (p=0.0098), which did not correlate with pERG deficits. Analysis of area centralis showed progressive loss of RNFL thickness at 90 and 180 days post compression. PERG amplitudes showed significant recovery at 90 days post compression (p\u3c0.05), but this effect was gone by 180 days. Full-field ERG recordings did not reveal deficits at any time. Conclusions: CON resulted in initial thickening of area centralis RNFL, followed by progressive RNFL loss. Pattern ERG analysis showed significant temporary improvement in RGC function. Inclusion of large retinal blood vessel profile in peripapillary RNFL analysis seems to decrease detection sensitivity and specificity for RNFL changes in early stages of compressive injury

Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks

In this paper, we purpose a diagnostic procedure to identify the macular disease from pattern electroretionography (PERG) signals using artificial neural networks (ANN) methods. Multilayer feed forward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented. The designed classification structure has about 96% sensitivity, 100% specifity and correct classification is calculated to be 98%. The end results are classified as Healthy and Diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis, angiography and Arden ratio of electrooculogram (EOG). The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. (c) 2005 Elsevier Ltd. All rights reserved

A multilayered approach to the automatic analysis of the multifocal electroretinogram

The multifocal electroretinogram (mfERG) provides spatial and temporal information on the retina’s function in an objective manner, making it a valuable tool for monitoring a wide range of retinal abnormalities. Analysis of this clinical test can however be both difficult and subjective, particularly if recordings are contaminated with noise, for example muscle movement or blinking. This can sometimes result in inconsistencies in the interpretation process. An automated and objective method for analysing the mfERG would be beneficial, for example in multi-centre clinical trials when large volumes of data require quick and consistent interpretation. The aim of this thesis was therefore to develop a system capable of standardising mfERG analysis. A series of methods aimed at achieving this are presented. These include a technique for grading the quality of a recording, both during and after a test, and several approaches for stating if a waveform contains a physiological response or no significant retinal function. Different techniques are also utilised to report if a response is within normal latency and amplitude values. The integrity of a recording was assessed by viewing the raw, uncorrelated data in the frequency domain; clear differences between acceptable and unacceptable recordings were revealed. A scale ranging from excellent to unreportable was defined for the recording quality, first in terms of noise resulting from blinking and loss of fixation, and secondly, for muscle noise. 50 mfERG tests of varying recording quality were graded using this method with particular emphasis on the distinction between a test which should or should not be reported. Three experts also assessed the mfERG recordings independently; the grading provided by the experts was compared with that of the system. Three approaches were investigated to classify a mfERG waveform as ‘response’ or ‘no response’ (i.e. whether or not it contained a physiological response): artificial neural networks (ANN); analysis of the frequency domain profile; and the signal to noise ratio. These techniques were then combined using an ANN to provide a final classification for ‘response’ or ‘no response’. Two methods were studied to differentiate responses which were delayed from those within normal timing limits: ANN; and spline fitting. Again the output of each was combined to provide a latency classification for the mfERG waveform. Finally spline fitting was utilised to classify responses as ‘decreased in amplitude’ or ‘not decreased’. 1000 mfERG waveforms were subsequently analysed by an expert; these represented a wide variety of retinal function and quality. Classifications stated by the system were compared with those of the expert to assess its performance. An agreement of 94% was achieved between the experts and the system when making the distinction between tests which should or should not be reported. The final system classified 95% of the 1000 mfERG waveforms correctly as ‘response’ or ‘no response’. Of those said to represent an area of functioning retina it concurred with the expert for 93% of the responses when categorising them as normal or abnormal in terms of their P1 amplitude and latency. The majority of misclassifications were made when analysing waveforms with a P1 amplitude or latency close to the boundary between normal and abnormal. It was evident that the multilayered system has the potential to provide an objective and automated assessment of the mfERG test; this would not replace the expert but can provide an initial analysis for the expert to review