An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis
An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis
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DOI：
10.1167/iovs.18-25568
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发表时间：
2019-01-01
影响因子：
4.4
通讯作者：
Elze, Tobias
中科院分区：
文献类型：
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作者：
Wang, Mengyu;Shen, Lucy Q.;Elze, Tobias
目的。通过分析空间模式的变化来检测视野(VF)的进展。方法：我们选择了7360名患者中的12217只眼，至少有5个可靠的24-2VF，并进行了5年的随访，间隔至少6个月。VF被分解为16个先前由人工智能技术派生的原型模式。对VF序列随时间变化的16个原型权重进行线性回归。我们将进展定义为正常原型的减少率或15个VF缺陷原型超出正常范围的任何增加率。将原型方法与平均偏差斜率(MD斜率)、晚期青光眼介入研究(AGIS)评分、协作性初始青光眼治疗研究(CIGTS)评分和点状线性回归(PoPLR)的排列进行比较，并通过3名青光眼专家评估的VFS子集进行验证。结果：在11,817只眼的方法开发队列中，原型方法与MD斜率(kappa：0.37)和PoPLR(0.33)的符合率高于AGIS(0.12)和CIGTS(0.22)。最常见的进展型包括正常形态减少(63.7%)、鼻步增加(16.4%)、高度丧失(15.9%)、上周缘缺失(12.1%)、中心旁/中央缺失(10.5%)和近全缺失(10.4%)。在397只眼的临床验证队列中，确认进展的27.5%，原型方法的符合率(Kappa)和准确性(命中率和正确排斥率的平均值)(P<；0.001)显著优于AGS0.06和0.52，CIGTS(0.24和0.59)，MD斜率(0.21和0.59)和POPLR(0.26和0.60)。
PURPOSE. To detect visual field (VF) progression by analyzing spatial pattern changes.METHODS. We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists.RESULTS. In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60).CONCLUSIONS. The archetype method can inform clinicians of VF progression patterns.