![]() The test type and eye are firstly selected and the patient's details are entered, including their refractive error. There are multiple steps which need to be done before commencement of the test to ensure reliable results are attained. The analyser test takes approximately 5–8 minutes, excluding patient set up. Method of assessment Figure 2 - Chin Rest and Lens Holder SITA SWAP: Short Wavelength Automated Perimetry (SWAP) is used for detection of early glaucomatous loss.Esterman – Used to test the functionality of a patient's vision to ensure they are safe to drive, as requested by VicRoads, Australia.There are additional tests for more specific purposes such as the following: It produces similar results compared to SITA-Standard, however repeatability is questionable and it is slightly less sensitive SITA-Fast is a quicker method of testing. The above tests can be performed in either SITA-Standard or SITA-Fast. Used for general screening, early glaucoma and neurological conditions 30-2: Measures 30 degrees temporally and nasally and tests 76 points.Used for neuro-ophthalmic conditions and general screening as well as early detection of glaucoma 24-2: Measures 24 degrees temporally and 30 degrees nasally and tests 54 points.Used for macula, retinal and neuro-ophthalmic conditions and advanced glaucoma 10-2: Measures 10 degrees temporally and nasally and tests 68 points.The '-2' represents the pattern of the points tested. The first number denotes the extent of the field measured on the temporal side, from the centre of fixation, in degrees. There are numerous testing protocols to select, based on the purpose. The analyser can be used for screening, monitoring and assisting in the diagnosis of certain conditions. These results are stored and used for monitoring the progression of vision loss and the patient's condition. This guides and contributes to the diagnosis of the condition affecting the patient's vision. Therefore, it provides information regarding the location of any disease processes or lesion(s) throughout the visual pathway. ![]() The results of the analyser identify the type of vision defect. Humphrey field analyser ( HFA) is a tool for measuring the human visual field that is commonly used by optometrists, orthoptists and ophthalmologists, particularly for detecting monocular visual field. March 10 Available from: 10.1001/ used by eye care professionals Figure 1 - Humphrey field analyser Course of Glaucomatous Visual Field Loss Across the Entire Perimetric Range. Otarola F, Chen A, Morales E, Yu F, Afifi A, Caprioli J. Analysis of progressive change in automated visual fields in glaucoma. Prediction of glaucomatous visual field loss by extrapolation of linear trends. Causes of vision loss worldwide, 1990–2010: a systematic analysis. 10.1136/bjophthalmol-2015-307223īourne RRA, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, et al. Global variations and time trends in the prevalence of primary open angle glaucoma (POAG): a systematic review and meta-analysis. Kapetanakis VV, Chan MPY, Foster PJ, Cook DG, Owen CG, Rudnicka AR. Using unfiltered real-world datasets, deep learning networks show the ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF. The 100 fully trained models successfully predicted future HVFs in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF and an average difference of 0.41 dB. The overall point-wise PMAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB), and deep learning showed a statistically significant improvement over linear models. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The point-wise mean absolute error (PMAE) and difference in Mean Deviation (MD) between predicted and actual future HVF were calculated. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. To determine if deep learning networks could be trained to forecast future 24-2 Humphrey Visual Fields (HVFs).Īll data points from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a university database.
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