Abstract：
BACKGROUND: Hypospadias phenotype assessment determines if the anatomy is favorable for reconstruction. Glans-Urethral Meatus-Shaft (GMS) has been adopted in an effort to standardize hypospadias classification. While extremely subjective, GMS has been widely used to classify the severity of the phenotype to predict surgical outcomes. The use of digital image analysis has proven to be feasible and prior efforts by our team have demonstrated that machine learning algorithms can emulate an expert\'s assessment of the phenotype. Nonetheless, the creation of these image recognition algorithms is highly subjective. In order to reduce a subjective input in the evaluation of the phenotype, we propose a novel approach to analyze the anatomy using digital image pixel analysis and to compare the results using the GMS score. Our hypothesis is that pixel cluster segmentation can discriminate between favorable and unfavorable anatomy.
OBJECTIVE: To evaluate whether image segmentation and digital pixel analysis are able to analyze favorable vs unfavorable hypospadias anatomy in a less subjective manner than GMS score.
METHODS: A total of 148 patients with different types of hypospadias were classified by 1 of 5 independent experts following the GMS score into \"favorable\" (GG), \"moderately favorable\" (GM) and \"unfavorable\" (GP) glans. From there, 592 images were generated using digital image segmentation. 584 were included for final analysis due to certain images being excluded for poor image quality or inadequate capture of target anatomy. For each image, the region of interest was segmented separately by two evaluators into \"glans,\" \"urethral plate,\" \"foreskin\" and \"periurethral plate\". The values obtained for each segmented region using machine-learning statistical pixel k-means cluster analysis were analyzed and compared to the GMS score given to that image using an ANOVA analysis.
RESULTS: Analysis of image segmentation demonstrated that k-means pixel cluster analysis discriminated \"favorable\" vs \"unfavorable\" urethral plates. There was a significant difference between scores when comparing the GG and GM groups (p = 0.03) and GG and GP groups (p = 0.05). Pixel cluster analysis could not discriminate between \"moderately favorable\" and \"unfavorable\" urethral plates.
CONCLUSIONS: Through our analysis, we found significant pairwise difference for different tissue qualities. Digital image segmentation and statistical k-means cluster analysis can discriminate anatomical features in a similar way to the GMS score. Future research can target discerning between different tissue qualities in an effort to predict surgical outcomes for hypospadias repair.
OBJECTIVE: To evaluate whether image segmentation and digital pixel analysis are able to analyze favorable vs unfavorable hypospadias anatomy in a less subjective manner than GMS score.
METHODS: A total of 148 patients with different types of hypospadias were classified by 1 of 5 independent experts following the GMS score into \"favorable\" (GG), \"moderately favorable\" (GM) and \"unfavorable\" (GP) glans. From there, 592 images were generated using digital image segmentation. 584 were included for final analysis due to certain images being excluded for poor image quality or inadequate capture of target anatomy. For each image, the region of interest was segmented separately by two evaluators into \"glans,\" \"urethral plate,\" \"foreskin\" and \"periurethral plate\". The values obtained for each segmented region using machine-learning statistical pixel k-means cluster analysis were analyzed and compared to the GMS score given to that image using an ANOVA analysis.
RESULTS: Analysis of image segmentation demonstrated that k-means pixel cluster analysis discriminated \"favorable\" vs \"unfavorable\" urethral plates. There was a significant difference between scores when comparing the GG and GM groups (p = 0.03) and GG and GP groups (p = 0.05). Pixel cluster analysis could not discriminate between \"moderately favorable\" and \"unfavorable\" urethral plates.
CONCLUSIONS: Through our analysis, we found significant pairwise difference for different tissue qualities. Digital image segmentation and statistical k-means cluster analysis can discriminate anatomical features in a similar way to the GMS score. Future research can target discerning between different tissue qualities in an effort to predict surgical outcomes for hypospadias repair.
摘要：
背景：尿道下裂表型评估确定解剖结构是否有利于重建。为了标准化尿道下裂的分类，已采用了GMS（GMS）。虽然非常主观，GMS已广泛用于对表型的严重程度进行分类以预测手术结果。使用数字图像分析已被证明是可行的，我们团队的先前努力已经证明机器学习算法可以模拟专家对表型的评估。尽管如此，这些图像识别算法的创建是高度主观的。为了减少表型评估中的主观输入，我们提出了一种新颖的方法，使用数字图像像素分析来分析解剖结构，并使用GMS评分比较结果。我们的假设是像素簇分割可以区分有利和不利的解剖结构。
目的：评估图像分割和数字像素分析是否能够以比GMS评分更少的主观方式分析尿道下裂的有利和不利解剖结构。
方法：根据GMS评分，5名独立专家中的1名将148名不同类型的尿道下裂患者分为“有利”（GG），“中度有利”(GM)和“不利”(GP)龟头。从那里,使用数字图像分割生成592张图像。由于某些图像因图像质量差或目标解剖结构捕获不足而被排除在外，因此包括584个用于最终分析。对于每个图像，感兴趣的区域由两名评估者分别分割成“龟头，尿道板,\"\"包皮\"和\"尿道周围板\"。分析使用机器学习统计像素k均值聚类分析为每个分割区域获得的值,并使用ANOVA分析与给予该图像的GMS得分进行比较。
结果：图像分割的分析表明，k均值像素聚类分析区分“有利”和“不利”尿道板。比较GG和GM组（p=0.03）以及GG和GP组（p=0.05）时，得分之间存在显着差异。像素聚类分析无法区分“中度有利”和“不利”尿道板。
结论：通过我们的分析，我们发现不同的组织质量存在显着成对差异。数字图像分割和统计k均值聚类分析可以以类似于GMS评分的方式区分解剖特征。未来的研究目标可以区分不同的组织质量，以预测尿道下裂修复的手术结果。
目的：评估图像分割和数字像素分析是否能够以比GMS评分更少的主观方式分析尿道下裂的有利和不利解剖结构。
方法：根据GMS评分，5名独立专家中的1名将148名不同类型的尿道下裂患者分为“有利”（GG），“中度有利”(GM)和“不利”(GP)龟头。从那里,使用数字图像分割生成592张图像。由于某些图像因图像质量差或目标解剖结构捕获不足而被排除在外，因此包括584个用于最终分析。对于每个图像，感兴趣的区域由两名评估者分别分割成“龟头，尿道板,\"\"包皮\"和\"尿道周围板\"。分析使用机器学习统计像素k均值聚类分析为每个分割区域获得的值,并使用ANOVA分析与给予该图像的GMS得分进行比较。
结果：图像分割的分析表明，k均值像素聚类分析区分“有利”和“不利”尿道板。比较GG和GM组（p=0.03）以及GG和GP组（p=0.05）时，得分之间存在显着差异。像素聚类分析无法区分“中度有利”和“不利”尿道板。
结论：通过我们的分析，我们发现不同的组织质量存在显着成对差异。数字图像分割和统计k均值聚类分析可以以类似于GMS评分的方式区分解剖特征。未来的研究目标可以区分不同的组织质量，以预测尿道下裂修复的手术结果。
