Biomedical subjects

Pamela A Sample

Publications and source records attributed to Pamela A Sample.

43 records · Page 3Linked to original sources

PubMed2002

Structure and function evaluation (SAFE): I. criteria for glaucomatous visual field loss using standard automated perimetry (SAP) and short wavelength automated perimetry (SWAP).

PURPOSE: To develop criteria for detecting glaucomatous visual field loss for standard automated perimetry (SAP) and short wavelength automated perimetry (SWAP). DESIGN: Longitudinal observational study. METHODS: Three populations were evaluated: (1) 348 normal subjects (348 eyes) were tested to develop normative databases and statistical analysis packages for SAP and SWAP. (2) An independent group of 47 normal subjects (94 eyes) with 4 years of longitudinal follow-up was evaluated to determine specificity of different criteria. (3) A group of 298 patients (479 eyes) with elevated intraocular pressure and normal baseline SAP visual fields were evaluated to determine the sensitivity of different criteria for detecting early glaucomatous visual field loss. RESULTS: Six criteria demonstrated high specificity for correctly identifying eyes with normal visual fields (98%-100%) for both SAP and SWAP: (1) a pattern standard deviation (PSD) worse than the normal 1% level, (2) a glaucoma hemifield test (GHT) "outside normal limits," (3) one hemifield cluster worse than the normal 1% level, (4) two hemifield clusters worse than the normal 5% level, (5) four abnormal (P <.05) locations, (6) five abnormal locations (P <.05) on the pattern deviation probability plot. For all criteria, confirmation on a second visual field was required for high specificity. The GHT "outside normal limits," two hemifield clusters worse than the normal 5% level and four abnormal (P <.05) test locations on the pattern deviation probability plot provided the highest percentages of conversion from a normal to a glaucomatous visual field. CONCLUSIONS: Criteria based on the GHT, GHT hemifield clusters, and the pattern deviation probability plot provide high sensitivity and specificity for detecting early glaucomatous visual field changes.

Adolescent↗

PubMed2002

Progression of visual field loss in untreated glaucoma patients and glaucoma suspects in St. Lucia, West Indies.

PURPOSE: A 1986-1987 survey found 8.8% prevalence of open-angle glaucoma in the black population of St. Lucia, West Indies. This follow-up study assessed visual field loss progression in untreated glaucoma patients and glaucoma suspects 10 years later. DESIGN: Cohort study. METHODS: Subjects were 205 glaucoma patients and suspects; 1987 data included age, sex, visual acuity, and visual fields measured by automated threshold perimetry (Humphrey C 30-2 test), and 1997 data included intraocular pressure, visual acuity, and visual fields measured by the same test. Exclusion criteria included field unreliability, field improvement due to vision improvement, nonglaucomatous vision deterioration, glaucoma treatment since 1988, and scoring of a visual field as end stage in 1987. Visual fields were scored by algorithms for the Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS). RESULTS: By AGIS criteria, 55% of 146 right eyes and 52% of 141 left eyes showed progression of visual field loss. In linear regressions, progression severity was unassociated with sex, intraocular pressure, or baseline visual field score, but was positively associated with age (P <.001, right; P =.002, left). The cumulative probability of reaching end stage in 10 years in at least one eye was approximately 16% by AGIS criteria. By CIGTS criteria, 73% of 146 right eyes and 72% of 141 left eyes progressed. CONCLUSIONS: These data provide a unique opportunity to study progression of untreated glaucoma. The percentage of eyes showing visual field loss progression and the percentage reaching end stage were considerably higher than in studies of visual field progression in treated eyes.

Adult↗

PubMed2002

Infrequent confirmation of visual field progression.

OBJECTIVE: To evaluate the effects of the repeatability criteria on the detection of change in visual fields by six progression algorithms used in standard automated perimetry. DESIGN: Retrospective, observational case series PARTICIPANTS: Fifty-one glaucoma patients, each with multiple visual fields performed between May 1990 and December 1998, were included. METHODS: Each patient's set of visual fields were analyzed using the glaucoma change probability, the Early Manifest Glaucoma Trial (EMGT) algorithm, a modified glaucoma change probability score, a modified EMGT score, the Advanced Glaucoma Intervention Study algorithm, and the Collaborative Initial Glaucoma Treatment Study algorithm. MAIN OUTCOME MEASURES: The effects of repeatability on the detection of field change, the level of agreement among algorithms, as well as the number of eyes identified as changed with each algorithm, were assessed. RESULTS: Mean follow-up was 34 months (range, 12-87 months). The average percentage of eyes with change based on three consecutive follow-up fields was 8.2% (4.0%-12.5%). However, of those showing change on the initial follow-up, this change from baseline was observed in subsequent examinations on average in 23% (18%-33%), depending on the algorithm. When change was based on just one field, four of the six algorithms noted a significantly greater number of eyes with change. The algorithms, however, did not differ significantly when confirmation of field change required two versus three consecutive follow-up visual fields. CONCLUSIONS: Although current algorithms may help identify change, there are inconsistencies among them. We found that requiring repeatable change from baseline significantly reduces the number of changed eyes identified with each subsequent follow-up field. Identification and confirmation of change in visual fields plays an important role in helping to identify true glaucoma progression; however, the specific methods to do so have yet to be determined.

Adult↗

PubMed2002

Comparison of machine learning and traditional classifiers in glaucoma diagnosis.

Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.

Artificial Intelligence↗

PubMed2002

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

PURPOSE: To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS: Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs. RESULTS: MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices. CONCLUSIONS: MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.

Diagnosis, Computer-Assisted↗

PubMed2002

Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

PURPOSE: To compare the ability of several machine learning classifiers to predict development of abnormal fields at follow-up in ocular hypertensive (OHT) eyes that had normal visual fields in baseline examination. METHODS: The visual fields of 114 eyes of 114 patients with OHT with four or more visual field tests with standard automated perimetry over three or more years and for whom stereophotographs were available were assessed. The mean (+/-SD) number of visual field tests was 7.89 +/- 3.04. The mean number of years covered (+/-SD) was 5.92 +/- 2.34 (range, 2.81-11.77). Fields were classified as normal or abnormal based on Statpac-like methods (Humphrey Instruments, Dublin, CA) and by several machine learning classifiers. The machine learning classifiers were two types of support vector machine (SVM), a mixture of Gaussian (MoG) classifier, a constrained MoG, and a mixture of generalized Gaussian (MGG). Specificity was set to 96% for all classifiers, using data from 94 normal eyes evaluated longitudinally. Specificity cutoffs required confirmation of abnormality. RESULTS: Thirty-two percent (36/114) of the eyes converted to abnormal fields during follow-up based on the Statpac-like methods. All 36 were identified by at least one machine classifier. In nearly all cases, the machine learning classifiers predicted the confirmed abnormality, on average, 3.92 +/- 0.55 years earlier than traditional Statpac-like methods. CONCLUSIONS: Machine learning classifiers can learn complex patterns and trends in data and adapt to create a decision surface without the constraints imposed by statistical classifiers. This adaptation allowed the machine learning classifiers to identify abnormality in visual field converts much earlier than the traditional methods.

Algorithms↗

PubMedDate not supplied

Primary open-angle glaucoma in blacks: a review.

Glaucoma is one of the leading causes of blindness worldwide. Primary open-angle glaucoma (POAG) is the most prevalent form of glaucoma and has a particularly devastating impact in blacks. In the black American population, POAG prevalence is estimated to be six times as high in certain age groups compared to whites. POAG is more likely to result in irreversible blindness, appears approximately 10 years earlier and progresses more rapidly in blacks than in whites. Racial differences in optic disk parameters have been reported and show that blacks have larger optic disks than whites. This finding is robust and may account for the reported differences in other optic disk parameters. The existence of racial differences in intraocular pressure remains to be demonstrated, as conflicting findings are reported in the literature. Intraocular pressure may actually be underestimated in blacks, perhaps because they have thinner corneas. The prevalence of diabetes and hypertension is higher in blacks than in whites, and although no causal relationship has been established between POAG and each of these systemic diseases, some reports suggest that they often occur together, perhaps through an indirect relationship with intraocular pressure. Compounding the problem, there is evidence that blacks are less responsive to both drug and surgical treatment for POAG. Finally, they often have reduced accessibility to treatment and are less aware of the risks of having POAG. This article provides a comprehensive review of the current knowledge pertaining to POAG in blacks.

Black People↗