Levin L A, Rizzo J F, Lessell S
Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, USA.
Br J Ophthalmol. 1996 Sep;80(9):835-9. doi: 10.1136/bjo.80.9.835.
The efficacy of an artificial intelligence technique, neural network analysis, was examined in differentiating two optic neuropathies with overlapping clinical profiles-idiopathic optic neuritis (ON) and non-arteritic anterior ischaemic optic neuropathy (AION).
A neural network was trained with data from 116 patients with 'gold standard' diagnoses of ON or AION. It was then tested with data from 128 patients with presumed ON or AION, and the correlation of the network's diagnosis with that of expert clinicians tabulated.
The network agreed with the clinicians on 97.8% (88 of 90) of the patients with presumed ON and 94.7% (36 of 38) of the patients with presumed AION. Youth, female sex, better initial acuity, a central scotoma, subsequent improvement in acuity, or progressive disease biased the network towards a diagnosis of ON, while advanced age, male sex, presence of hypertension, poor initial acuity, an altitudinal field defect, disc oedema, or less improvement in acuity biased the network towards a diagnosis of AION.
Neural network analysis is a useful technique for classification of optic neuropathies, particularly where there is overlap of clinical findings.
研究一种人工智能技术——神经网络分析，在鉴别两种具有重叠临床特征的视神经病变（特发性视神经炎（ON）和非动脉炎性前部缺血性视神经病变（AION））中的有效性。
使用116例经“金标准”诊断为ON或AION患者的数据训练神经网络。然后用128例疑似ON或AION患者的数据对其进行测试，并将该网络诊断结果与专家临床医生的诊断结果的相关性制成表格。
该网络与临床医生对97.8%（90例中的88例）疑似ON患者和94.7%（38例中的36例）疑似AION患者的诊断意见一致。年轻、女性、初始视力较好、中心暗点、视力随后改善或病情进展使网络倾向于诊断为ON，而高龄、男性、高血压、初始视力差、视野缺损、视盘水肿或视力改善较少使网络倾向于诊断为AION。
神经网络分析是一种对视神经病变进行分类的有用技术，尤其是在临床发现存在重叠的情况下。
