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Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma.
- Source :
- Acta Ophthalmologica (1755375X); Aug2013, Vol. 91 Issue 5, p413-417, 5p
- Publication Year :
- 2013
-
Abstract
- . 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1755375X
- Volume :
- 91
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Acta Ophthalmologica (1755375X)
- Publication Type :
- Academic Journal
- Accession number :
- 88979892
- Full Text :
- https://doi.org/10.1111/j.1755-3768.2012.02435.x