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

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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.

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