Phu Jack, Khuu Sieu K, Bui Bang V, Kalloniatis Michael
Centre for Eye Health, University of New South Wales, Kensington, NSW, Australia.
School of Optometry and Vision Science, University of New South Wales, Kensington, NSW, Australia.
Transl Vis Sci Technol. 2018 Sep 4;7(5):3. doi: 10.1167/tvst.7.5.3. eCollection 2018.
To assess the diagnostic utility of a new hemifield asymmetry analysis derived using pattern recognition contrast sensitivity isocontours (CSIs) within the Humphrey Field Analyzer (HFA) 24-2 visual field (VF) test grid. The performance of an optimal CSI-derived map was compared against a commercially available clustering method (Glaucoma Hemifield Test, GHT).
Five hundred VF results of 116 healthy subjects were used to determine normative distribution limits for comparisons. Pattern recognition analysis was applied to HFA 24-2 sensitivity data to determine CSI theme maps delineating clusters for hemifield comparisons. Then, 1019 VF results from 228 glaucoma patients were assessed using different clustering methods to determine the true-positive rate. We also assessed additional 354 VF results of 145 healthy subjects to determine the false-positive rate.
The optimum clustering method was the CSI-derived seven-theme class map, which identified more glaucomatous VFs compared with the GHT map. The seven-class theme map also identified more cases compared with the five-, six-, and eight-class maps, suggesting no effect of number of clusters. Integrating information regarding the location of glaucomatous defects to the CSI clusters did not improve detection rate.
A clustering map derived using CSIs improved detection of glaucomatous VFs compared with the currently available GHT. An optimized CSI-derived map may serve as an additional means to aid earlier detection of glaucoma.
Pattern recognition-derived theme maps provide a means for guiding test point selection for asymmetry analysis in glaucoma assessment.
评估在 Humphrey 视野分析仪（HFA）24 - 2 视野（VF）测试网格中，使用模式识别对比敏感度等视线（CSIs）得出的新半视野不对称分析的诊断效用。将最优 CSI 得出的地图的性能与一种商用聚类方法（青光眼半视野检测，GHT）进行比较。
使用 116 名健康受试者的 500 份 VF 结果来确定用于比较的正常分布界限。对 HFA 24 - 2 敏感度数据应用模式识别分析，以确定用于半视野比较的 CSI 主题地图，该地图描绘了聚类情况。然后，使用不同聚类方法评估 228 名青光眼患者的 1019 份 VF 结果，以确定真阳性率。我们还评估了 145 名健康受试者的另外 354 份 VF 结果，以确定假阳性率。
最优聚类方法是 CSI 得出的七主题分类地图，与 GHT 地图相比，它识别出更多青光眼性 VF。与五、六和八类地图相比，七类主题地图也识别出更多病例，这表明聚类数量没有影响。将青光眼性缺损位置的信息整合到 CSI 聚类中并没有提高检测率。
与目前可用的 GHT 相比，使用 CSIs 得出的聚类地图提高了青光眼性 VF 的检测率。优化的 CSI 得出的地图可能作为辅助早期青光眼检测的额外手段。
模式识别得出的主题地图为青光眼评估中不对称分析的测试点选择提供了指导方法。
