Biomedical subjects

John Pascual

Publications and source records attributed to John Pascual.

4 recordsLinked to original sources

PubMed2005

Relationship of SITA and full-threshold standard perimetry to frequency-doubling technology perimetry in glaucoma.

PURPOSE: To compare full-threshold (FT) and SITA algorithms for standard automated perimetry (SAP) with frequency-doubling technology perimetry (FDT) in glaucoma, to help clinicians to relate results in patients who have had two or more of these tests during follow-up. METHODS: This study was a retrospective analysis of data from a longitudinal prospective study at the University of California, San Diego. One hundred four eyes of 104 patients with glaucomatous optic neuropathy detected by optic disc stereophotographs were included. All patients had standard perimetry (SITA and FT) and FDT within 3 months of each other. Global indices, abnormality and severity using two threshold algorithms of standard perimetry were compared with FDT. RESULTS: More eyes had normal visual fields by SAP-FT (57 eyes) than by either SAP-SITA (42 eyes) or FDT (45 eyes), although SAP-FT agreed more closely with FDT (kappa = 0.54 +/- 0.08) on the presence of a visual field defect than did SAP-SITA (kappa = 0.34 +/- 0.08). Correlations of FDT to standard perimetry global indices were similar regardless of the threshold strategy used for standard perimetry, yielding r2= 0.38-0.57 for SAP-FT with FDT, 0.36-0.54 for SAP-SITA with FDT. CONCLUSIONS: Despite many similarities of SAP-SITA and SAP-FT, switching the standard of comparison from SAP-FT to SAP-SITA changes the relationship to FDT with regard to visual field abnormality, but not correlations of global indices. FDT detected abnormal fields in more eyes than SAP-FT. SAP-FT tended to detect a subset of eyes found abnormal by FDT. Visual field defects may be detected more often by FDT and SAP-SITA in eyes with early visual field loss, but these two tests may not agree on which eyes show field loss in patients who undergo both tests at follow-up.

Algorithms↗

PubMed2005

Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

PURPOSE: To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. METHODS: In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 +/- 2.65 years and 8.11 +/- 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual's series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria. RESULTS: Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT. CONCLUSIONS: The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.

Adult↗

PubMed2004

Patterns of glaucomatous visual field progression identified by three progression criteria.

PURPOSE: To determine typical patterns of repeatable glaucomatous visual field progression. DESIGN: Retrospective analysis of data obtained from two prospective studies. METHODS: Included were 72 eyes of 72 patients tested up to six times over 2 years, and 40 eyes of 40 patients followed annually for up to 12 years. Each patient had two abnormal baseline visual fields, abnormal optic nerves, and serial fields. Progression was identified using three methods: by glaucoma change probability using total deviation (GCP-TD) and pattern deviation (GCP-PD) plots and by a clinical criteria. Progression was categorized as deepening or expansion of an existing scotoma, or a new scotoma. RESULTS: The percentage of eyes repeatably progressed ranged from 17% to 27%. The most common pattern of progression was a deepening of an existing scotoma in the annual group, followed by expansion. With two follow-ups required, percentages for deepening only were 20% (clinical classifier). A combination of expansion and deepening was most common for the GCP criteria: 15% (GCP-TD classifier), and 10% (GCP-PD classifier) for the annual group. For the semiannual group, deepening was most common with the clinical criteria (11% of eyes), and deepening with expansion was most common by GCP criteria (14%, GCP-TD and GCP-PD). No eyes showed repeatable new scotomas. CONCLUSIONS: Glaucomatous visual fields progress in the area of the visual field where baseline testing showed an existing scotoma. Follow-up testing might be improved by concentrating on already defective locations and using sparser test patterns or screening algorithms in normal areas of the visual field.

Diagnostic Techniques, Ophthalmological↗

PubMed2004

Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

PURPOSE: To determine whether an unsupervised machine learning classifier can identify patterns of visual field loss in standard visual fields consistent with typical patterns learned by decades of human experience. METHODS: Standard perimetry thresholds for 52 locations plus age from one eye of each of 156 patients with glaucomatous optic neuropathy (GON) and 189 eyes of healthy subjects were clustered with an unsupervised machine classifier, variational Bayesian mixture of factor analysis (vbMFA). RESULTS: The vbMFA formed five distinct clusters. Cluster 5 held 186 of 189 fields from normal eyes plus 46 from eyes with GON. These fields were then judged within normal limits by several traditional methods. Each of the other four clusters could be described by the pattern of loss found within it. Cluster 1 (71 GON + 3 normal optic discs) included early, localized defects. A purely diffuse component was rare. Cluster 2 (26 GON) exhibited primarily deep superior hemifield defects, and cluster 3 (10 GON) held deep inferior hemifield defects only or in combination with lesser superior field defects. Cluster 4 (6 GON) showed deep defects in both hemifields. In other words, visual fields within a given cluster had similar patterns of loss that differed from the predominant pattern found in other clusters. The classifier separated the data based solely on the patterns of loss within the fields, without being guided by the diagnosis, placing 98.4% of the healthy eyes within the same cluster and spreading 70.5% of the eyes with GON across the other four clusters, in good agreement with a glaucoma expert and pattern standard deviation. CONCLUSIONS: Without training-based diagnosis (unsupervised learning), the vbMFA identified four important patterns of field loss in eyes with GON in a manner consistent with years of clinical experience.

Algorithms↗