Comparison of perimetric 24-2 and 30-2 test patterns in detecting visual field defects in patients with tumours in the pituitary region.

PURPOSE: Automated perimetry provides a standardized method of measuring the visual field. The Humphrey Field Analyser (HFA) uses the 24-2 test pattern to cover 24 degrees centrally or the 30-2 test pattern to cover a slightly broader region of 30 degrees. The aim of this study was to determine whether the 24-2 test pattern provides comparable information to the 30-2 test pattern in detecting visual field defects in patients with tumours in the pituitary region. METHODS: A retrospective cohort study was carried out on patients with tumours in the pituitary region and radiologically confirmed compression of the visual pathway. Included patients (79 of 133) had been examined using the Humphrey 30-2 visual field test, after which the 30-2 test patterns were reduced into corresponding 24-2 test patterns. The location of visual field defects, visual acuity and the perimetric parameters mean deviation (MD) and visual field index (VFI) were also recorded. RESULTS: No patient was classified differently when evaluated with the 24-2 test pattern, compared to the 30-2 test pattern. Interestingly, although the majority of patients had visual field defects located in the temporal visual field of each eye, a significant minority did not. In addition, it was found that a large proportion of patients had normal visual acuity (≥0.8). CONCLUSIONS: The use of the HFA 24-2 test pattern reliably detected visual field defects in patients with tumours in the pituitary region. The present study indicates that MD and VFI are not reliable parameters for evaluating visual field defects due to compression.

Patient experience and repeatability of measurements made with the Pentacam HR in patients with keratoconus.

BACKGROUND: To investigate whether the repeatability of measurements with the Pentacam HR in patients with keratoconus is improved by patients gaining more experience of the measurement situation. Such an improvement could enhance the accuracy with which progressive keratoconus can be detected. METHODS: Four replicate measurements were performed on Day 0 and on Day 3. Parameters commonly used in the diagnosis of progressive keratoconus were included in the analysis, namely the flattest central keratometry value (K1), the steepest central keratometry value (K2), the maximum keratometry value (Kmax), and the parameters A, B and C from the Belin ABCD Progression Display. In addition, quality parameters used by the Pentacam HR to assess the quality of the measurements were included, namely the analysed area (front + back), 3D (front + back), XY, Z, and eye movements. RESULTS: Neither the diagnostic parameters nor the quality parameters showed any statistically significant improvement on Day 3 compared to Day 0. The quality parameter "eye movements" deteriorated significantly with increasing Kmax. CONCLUSION: Gaining experience of the measurement situation did not increase the accuracy of the measurements. Further investigations should be performed to determine whether the increasing number of eye movements with increasing disease severity has a negative effect on the repeatability of the measurements.

The intra- and inter-day repeatability of corneal densitometry measurements in subjects with keratoconus and in healthy controls.

The healthy cornea is transparent, however, disease can affect its structure, rendering it more or less opaque. The ability to assess the clarity of the cornea objectively could thus be of considerable interest for keratoconus patients. It has previously been suggested that densitometry can be used to diagnose early keratoconus, and that the values of densitometry variables increase with increasing disease severity, indicating that densitometry could also be used to assess progressive keratoconus. Previous studies have only assessed the repeatability of corneal densitometry measurements on the same day, which does not reflect the clinical setting in which changes are evaluated over time. We have therefore evaluated the inter-day repeatability of densitometry measurements in both patients with keratoconus and healthy controls. Measurements in the middle layer of the 2-6 mm zone of the cornea showed the best repeatability. Although an objective measure of the corneal transparency could be interesting, the generally poor repeatability of densitometry measurements limits their use. The repeatability of corneal clarity measurements could be improved by using other approaches such as optical coherence tomography, but this remains to be investigated. Such improvements would allow the more widespread use of corneal densitometry in clinical practice.

Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma.

PURPOSE: To compare clinicians and a trained artificial neural network (ANN) regarding accuracy and certainty of assessment of visual fields for the diagnosis of glaucoma. METHODS: Thirty physicians with different levels of knowledge and experience in glaucoma management assessed 30-2 SITA Standard visual field printouts that included full Statpac information from 99 patients with glaucomatous optic neuropathy and 66 healthy subjects. Glaucomatous eyes with perimetric mean deviation values worsethan -10 dB were not eligible. The fields were graded on a scale of 1-10, where 1 indicated healthy with absolute certaintyand 10 signified glaucoma; 5.5 was the cut-off between healthy and glaucoma. The same fields were classified by a previously trained ANN. The ANN output was transformed into a linear scale that matched the scale used in the subjective assessments. Classification certainty was assessed using a classification error score. RESULTS: Among the physicians, sensitivity ranged from 61% to 96% (mean 83%) and specificity from 59% to 100% (mean 90%). Our ANN achieved 93% sensitivity and 91% specificity, and it was significantly more sensitive than the physicians (p < 0.001) at a similar level of specificity. The ANN classification error score was equivalent to the top third scores of all physicians, and the ANN never indicated a high degree of certainty for any of its misclassified visual field tests. CONCLUSION: Our results indicate that a trained ANN performs at least as well as physicians in assessments of visual fields for the diagnosis of glaucoma.

Integration and fusion of standard automated perimetry and optical coherence tomography data for improved automated glaucoma diagnostics.

BACKGROUND: The performance of glaucoma diagnostic systems could be conceivably improved by the integration of functional and structural test measurements that provide relevant and complementary information for reaching a diagnosis. The purpose of this study was to investigate the performance of data fusion methods and techniques for simple combination of Standard Automated Perimetry (SAP) and Optical Coherence Tomography (OCT) data for the diagnosis of glaucoma using Artificial Neural Networks (ANNs). METHODS: Humphrey 24-2 SITA standard SAP and StratusOCT tests were prospectively collected from a randomly selected population of 125 healthy persons and 135 patients with glaucomatous optic nerve heads and used as input for the ANNs. We tested commercially available standard parameters as well as novel ones (fused OCT and SAP data) that exploit the spatial relationship between visual field areas and sectors of the OCT peripapillary scan circle. We evaluated the performance of these SAP and OCT derived parameters both separately and in combination. RESULTS: The diagnostic accuracy from a combination of fused SAP and OCT data (95.39%) was higher than that of the best conventional parameters of either instrument, i.e. SAP Glaucoma Hemifield Test (p < 0.001) and OCT Retinal Nerve Fiber Layer Thickness ≥ 1 quadrant (p = 0.031). Fused OCT and combined fused OCT and SAP data provided similar Area under the Receiver Operating Characteristic Curve (AROC) values of 0.978 that were significantly larger (p = 0.047) compared to ANNs using SAP parameters alone (AROC = 0.945). On the other hand, ANNs based on the OCT parameters (AROC = 0.970) did not perform significantly worse than the ANNs based on the fused or combined forms of input data. The use of fused input increased the number of tests that were correctly classified by both SAP and OCT based ANNs. CONCLUSIONS: Compared to the use of SAP parameters, input from the combination of fused OCT and SAP parameters, and from fused OCT data, significantly increased the performance of ANNs. Integrating parameters by including a priori relevant information through data fusion may improve ANN classification accuracy compared to currently available methods.

Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT.

PURPOSE: To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. METHODS: We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. RESULTS: There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < or = 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters. CONCLUSION: No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.

Trained artificial neural network for glaucoma diagnosis using visual field data: a comparison with conventional algorithms.

PURPOSE: To evaluate and confirm the performance of an artificial neural network (ANN) trained to recognize glaucomatous visual field defects, and compare its diagnostic accuracy with that of other algorithms proposed for the detection of visual field loss. METHODS: SITA Standard 30-2 visual fields, from 100 glaucoma patients and 116 healthy participants, formed the data set. Our ANN was a previously described fully trained network using scored pattern deviation probability maps as input data. Its diagnostic accuracy was compared to that of the Glaucoma Hemifield Test, the Pattern Standard Deviation index at the P<5% and <1%, and also to a technique based on the recognizing clusters of significantly depressed test points. RESULTS: The included tests had early to moderate visual field loss (median MD=-6.16 dB). ANN achieved a sensitivity of 93% at a specificity level of 94% with an area under the receiver operating characteristic curve of 0.984. Glaucoma Hemifield Test attained a sensitivity of 92% at 91% specificity. Pattern Standard Deviation, with a cut off level at P<5% had a sensitivity of 89% with a specificity of 93%, whereas at P<1% the sensitivity and specificity was 72% and 97%, respectively. The cluster algorithm yielded a sensitivity of 95% and a specificity of 82%. CONCLUSIONS: The high diagnostic performance of our ANN based on refined input visual field data was confirmed in this independent sample. Its diagnostic accuracy was slightly to considerably better than that of the compared algorithms. The results indicate the large potential for ANN as an important clinical glaucoma diagnostic tool.

Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields.

PURPOSE: To compare the performance of neural networks for perimetric glaucoma diagnosis when using different types of data inputs: numerical threshold sensitivities, Statpac Total Deviation and Pattern Deviation, and probability scores based on Total and Pattern Deviation probability maps (Carl Zeiss Meditec, Inc., Dublin, CA). METHODS: The results of SITA Standard visual field tests in 213 healthy subjects, 127 patients with glaucoma, 68 patients with concomitant glaucoma and cataract, and 41 patients with cataract only were included. The five different types of input data were entered into five identically designed artificial neural networks. Network thresholds were adjusted for each network. Receiver operating characteristic (ROC) curves were constructed to display the combinations of sensitivity and specificity. RESULTS: Input data in the form of Pattern Deviation probability scores gave the best results, with an area of 0.988 under the ROC curve, and were significantly better (P < 0.001) than threshold sensitivities and numerical Total Deviations and Total Deviation probability scores. The second best result was obtained with numerical Pattern Deviations with an area of 0.980. CONCLUSIONS: The choice of type of data input had important effects on the performance of the neural networks in glaucoma diagnosis. Refined input data, based on Pattern Deviations, resulted in higher sensitivity and specificity than did raw threshold values. Neural networks may have high potential in the production of useful clinical tools for the classification of visual field tests.