Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma
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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DOI：
10.1111/j.1755-3768.2012.02435.x
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发表时间：
2013-08-01
影响因子：
3.4
通讯作者：
Bengtsson, Boel
中科院分区：
文献类型：
--
作者：
Andersson, Sabina;Heijl, Anders;Bengtsson, Boel
． 目的：比较临床医生和训练后的人工神经网络（ANN）在视野评估诊断青光眼的准确性和确定性方面的差异。方法：30位具有不同青光眼管理知识和经验水平的医生评估了来自99名青光眼视神经病变患者和66名健康受试者的30-2份SITA标准视野打印结果，包括完整的Statpac信息。眼周平均偏差值小于-10dB的青光眼不合格。这些视野按1-10的等级进行分级，其中1表示绝对健康，10表示青光眼；5.5分是健康与青光眼的分界点。相同的字段由先前训练过的人工神经网络进行分类。人工神经网络的输出被转换成与主观评估中使用的尺度相匹配的线性尺度。使用分类错误评分评估分类确定性。结果：在医生中，敏感性从61%到96%（平均83%），特异性从59%到100%（平均90%）。我们的人工神经网络达到了93%的敏感性和91%的特异性，明显比医生更敏感(p
. 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 -10dB 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