人工智能在青光眼中央视野模式分类中的应用。
Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.
机构信息
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts.
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
出版信息
Ophthalmology. 2020 Jun;127(6):731-738. doi: 10.1016/j.ophtha.2019.12.004. Epub 2019 Dec 12.
PURPOSE
To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence.
DESIGN
Retrospective study.
PARTICIPANTS
VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses.
METHODS
Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC).
MAIN OUTCOME MEASURES
The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results.
RESULTS
From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time.
CONCLUSIONS
We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.
目的
利用人工智能量化青光眼的中央视野（VF）损失模式。
设计
回顾性研究。
参与者
对 13951 只眼的 8712 例患者的 VF 进行横断面分析，这些患者的视野检查结果来自 13951 只眼的 10-2 次 Humphrey 测试；对 1191 只眼中至少有 5 次可靠的 10-2 次测试结果，且间隔 6 个月或更长时间进行了纵向分析。
方法
使用最近的 10-2 次测试结果，使用总偏差值确定中央 VF 模式。在基线阶段，使用 Hodapp-Anderson-Parrish 量表，将 3 个月内的 24-2VF 作为评估眼睛轻度、中度或严重功能丧失的指标。应用原型分析确定中央 VF 模式。进行交叉验证以确定最佳模式数量。应用逐步回归选择全局指数、中央 VF 原型的平均基线分解系数以及其他因素的最佳特征组合，以基于贝叶斯信息准则（BIC）预测中央 VF 平均偏差（MD）斜率。
主要观察指标
基于 24-2 测试结果的严重程度阶段分层的中央 VF 模式和基于基线测试结果预测中央 VF MD 随时间变化的模型。
结果
通过横断面分析，在疾病严重程度的范围内，对 13951 只眼中的 17 种不同的中央 VF 模式进行了确定。这些中央 VF 模式可以分为孤立性上侧损失、孤立性下侧损失、弥漫性损失和其他损失模式。值得注意的是，弥漫性 VF 损失的 5 种模式中有 4 种保留了较不易受影响的下颞区，而这些模式失去了大部分由 Hood 模型描述的更易受影响的剩余区域。纳入中央 VF 原型模式的系数可以大大提高中央 VF MD 斜率的预测能力（BIC 降低 35；BIC 降低超过 6 表示预测能力显著提高），优于仅使用 2 个基线 VF 结果的全局指数。基线 VF 结果中鼻上和鼻下损失越多的眼睛，随时间推移 MD 恶化的可能性越大。
结论
我们量化了青光眼的中央 VF 模式，这有助于提高中央 VF 恶化的预测能力，优于仅使用全局指数。
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