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

<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

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

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

Abstract. 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 </= 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

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

Abstract 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

Introducing a new tool for the assessment of progressive keratoconus: the Scandinavian Keratoconus Progression Application

PurposeTo develop an application for the Pentacam HR for the purpose of optimising the accuracy of the diagnosis of progressive keratoconus.MethodsMeasurements were performed on one randomised eye in twenty-five subjects with keratoconus Stage 1-2 according to the Amsler-Krumeich classification on two occasions, three days apart. Four replicate measurements were made by a single examiner on each occasion. The inter-day repeatability and detection limits for the diagnosis of progressive keratoconus were calculated for the following parameters: K2 and Kmax, and the parameters A, B and C from the Belin ABCD Progression Display. The measurements used as input are automatically extracted from the Pentacam HR database as comma-separated values. The application, developed in the R programming environment, provides a web browser-based user interface that presents these parameters both numerically and graphically.ResultsThe application includes detection limits for the diagnosis of progressive keratoconus obtained from two previous studies on the inter-day repeatability of measurements in subjects with keratoconus. The detection limits are based on inter-day repeatability, stratified according to disease severity, allowing the comparison of single measurements or a mean of four replicates.ConclusionsThis is the first application to provide an assessment of progressive keratoconus using detection limits based on inter-day repeatability. We believe this application will contribute to the more accurate diagnosis of progressive keratoconus. It also facilitates diagnosis and improves the clinical workflow as all the relevant information is presented numerically, graphically, and colour-coded in one interface