Amini Navid, Alizadeh Reza, Parivisutt Nucharee, Kim EunAh, Nouri-Mahdavi Kouros, Caprioli Joseph
Transl Vis Sci Technol. 2017 Oct 27;6(5):14. doi: 10.1167/tvst.6.5.14. eCollection 2017 Oct.
To present a digital image subtraction technique to alert clinicians to signs of glaucomatous optic disc progression.
Ninety-two glaucomatous eyes (65 patients) were included. Thirty-three eyes were identified as progressive and 59 as stable based on comparison of baseline and follow-up stereoscopic disc photographs by three masked glaucoma specialists. The disc images were aligned and converted to gray scale and underwent histogram matching to enhance contrast and account for illumination differences. The difference in image intensity between baseline and follow-up images was shown as a colormap superimposed on the grayscale follow-up image. A graded scale (1, no progression, to 5, definitive progression) was used by three masked glaucoma experts to score progression probability on the colormap images. Sensitivity, specificity, and accuracy of the classification were computed. Weighted κ statistics summarized agreement of categorical gradings.
Median time interval between two visits was 4.4 years (range: 1.0-16.8). Clinicians detected glaucoma deterioration in 25 to 27 of the progressive group and 8 to 10 of stable eyes based on subtraction maps. Sensitivities/specificities of the clinicians were 0.76 to 0.82 and 0.86 to 0.89, respectively. Classification accuracy ranged from 81.5% to 84.8%. Agreement among clinicians was good (weighted κ = 0.68; 95% confidence interval [CI]: 0.60-0.77) for progression grades (1-5 scales) and was substantial (weighted κ = 0.81; 95% CI: 0.74-0.85) for binary scores.
The proposed software provides a single static image that clinicians can use with other structural/functional tests to detect glaucoma progression.
Provision of a subtraction colormap in the setting of electronic medical records can improve monitoring of glaucoma by alerting clinicians to possible signs of progression.
介绍一种数字图像减法技术,以提醒临床医生注意青光眼性视盘进展的迹象。
纳入92只青光眼患眼(65例患者)。通过3名不知情的青光眼专家比较基线和随访立体视盘照片,33只眼被确定为进展性,59只为稳定性。将视盘图像对齐并转换为灰度图,进行直方图匹配以增强对比度并校正光照差异。基线图像和随访图像之间的图像强度差异以彩色图形式叠加在灰度随访图像上。3名不知情的青光眼专家使用分级量表(1级为无进展,5级为明确进展)对彩色图图像的进展概率进行评分。计算分类的敏感性、特异性和准确性。加权κ统计量总结了分类分级的一致性。
两次就诊之间的中位时间间隔为4.4年(范围:1.0 - 16.8年)。基于减法图,临床医生在进展性组中检测到25至27只眼的青光眼病情恶化,在稳定性组中检测到8至10只眼。临床医生的敏感性/特异性分别为0.76至0.82和0.86至0.89。分类准确率在81.5%至84.8%之间。临床医生之间对于进展分级(1 - 5级量表)的一致性良好(加权κ = 0.68;95%置信区间[CI]:0.60 - 0.77),对于二元评分的一致性很强(加权κ = 0.81;95%CI:0.74 - 0.85)。
所提出的软件提供了一张单一的静态图像,临床医生可将其与其他结构/功能测试一起用于检测青光眼进展。
在电子病历环境中提供减法彩色图可通过提醒临床医生注意可能的进展迹象来改善青光眼监测。