Evaluation of a Deep Learning–Derived Quantitative Retinopathy of Prematurity Severity Scale

医学 早产儿视网膜病变 阶段(地层学) 眼底(子宫) 胎龄 疾病严重程度 多元分析 眼科 内科学 怀孕 遗传学 生物 古生物学
作者
J. Peter Campbell,Sang Jin Kim,James M. Brown,Susan Ostmo,R.V. Paul Chan,Jayashree Kalpathy‐Cramer,Michael F. Chiang,Michael F. Chiang,Susan Ostmo,Sang Jin Kim,Kemal Sönmez,Robert L. Schelonka,J. Peter Campbell,R.V. Paul Chan,Karyn Jonas,Jason Horowitz,Osode Coki,Cheryl-Ann Eccles,Leora Sarna,Anton Orlin
出处
期刊:Ophthalmology [Elsevier BV]
卷期号:128 (7): 1070-1076 被引量:55
标识
DOI:10.1016/j.ophtha.2020.10.025
摘要

Purpose To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale. Design Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity. Participants Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9. Methods A quantitative vascular severity score (1–9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement. Main Outcome Measures Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3–6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale. Results For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale. Conclusions A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis. To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale. Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity. Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9. A quantitative vascular severity score (1–9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement. Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3–6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale. For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale. A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.
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