Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening

Purpose Glaucoma screening can be performed by assessing the vertical-cup-to-disk ratio (VCDR) of the optic nerve head from fundus photography, but VCDR grading is inherently subjective. This study investigated whether computer software could improve the accuracy and repeatability of VCDR assessment. Methods In this cross-sectional diagnostic accuracy study, 5 ophthalmologists independently assessed the VCDR from a set of 200 optic disk images, with the median grade used as the reference standard for subsequent analyses. Eight non-ophthalmologists graded each image by two different methods: by visual inspection and with assistance from a custom-made publicly available software program. Agreement with the reference standard grade was assessed for each method by calculating the intraclass correlation coefficient (ICC), and the sensitivity and specificity determined relative to a median ophthalmologist grade of ≥0.7. Results VCDR grades ranged from 0.1 to 0.9 for visual assessment and from 0.1 to 1.0 for software-assisted grading, with a median grade of 0.4 for each. Agreement between each of the 8 graders and the reference standard was higher for visual inspection (median ICC 0.65, interquartile range 0.57 to 0.82) than for software-assisted grading (median ICC 0.59, IQR 0.44 to 0.71); P = 0.02, Wilcoxon signed-rank test). Visual inspection and software assistance had similar sensitivity and specificity for detecting glaucomatous cupping. Conclusion The computer software used in this study did not improve the reproducibility or validity of VCDR grading from fundus photographs compared with simple visual inspection. More clinical experience was correlated with higher agreement with the ophthalmologist VCDR reference standard.

[1]  Andrew Hunter,et al.  Optic nerve head segmentation , 2004, IEEE Transactions on Medical Imaging.

[2]  E. O'Neill,et al.  Glaucomatous optic neuropathy evaluation (GONE) project: the effect of monoscopic versus stereoscopic viewing conditions on optic nerve evaluation. , 2014, American journal of ophthalmology.

[3]  M. He,et al.  Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. , 2018, Ophthalmology.

[4]  Joachim M. Buhmann,et al.  Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation , 2017, Comput. Medical Imaging Graph..

[5]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[6]  Jakob Grauslund,et al.  Automated Screening for Diabetic Retinopathy – A Systematic Review , 2018, Ophthalmic Research.

[7]  E. O'Neill,et al.  Glaucomatous optic neuropathy evaluation project: factors associated with underestimation of glaucoma likelihood. , 2014, JAMA ophthalmology.

[8]  A. Sevastopolsky,et al.  Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network , 2017, Pattern Recognition and Image Analysis.

[9]  Clinical research methodology in ophthalmology. , 1980 .

[10]  Chaithanya A Ramachandra,et al.  Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis , 2016, Journal of diabetes science and technology.

[11]  Baihua Li,et al.  A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis , 2017, Journal of Medical Systems.

[12]  Francisco Fumero,et al.  RIM-ONE: An open retinal image database for optic nerve evaluation , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[13]  P. Mitchell,et al.  Validity of a new optic disc grading software for use in clinical and epidemiological research , 2013, Clinical & experimental ophthalmology.

[14]  R V North,et al.  Digital imaging of the optic nerve head: monoscopic and stereoscopic analysis , 2005, British Journal of Ophthalmology.

[15]  N. Ehlers,et al.  Precision and accuracy of the ICare tonometer – Peripheral and central IOP measurements by rebound tonometry , 2012, Acta ophthalmologica.

[16]  Ana Beatriz D Grisolia,et al.  Teleophthalmology: where are we now? , 2017, Arquivos brasileiros de oftalmologia.

[17]  Douglas R. Anderson,et al.  The Ocular Hypertension Treatment Study: reproducibility of cup/disk ratio measurements over time at an optic disc reading center. , 2002, American journal of ophthalmology.

[18]  P. Lichter Variability of expert observers in evaluating the optic disc. , 1976, Transactions of the American Ophthalmological Society.

[19]  J. Bartlett,et al.  Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[20]  Yongli Xu,et al.  Optic cup segmentation from fundus images for glaucoma diagnosis , 2017, Bioengineered.

[21]  Ashish Issac,et al.  An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection , 2018, Int. J. Medical Informatics.

[22]  H. Uusitalo,et al.  Telemedicine in ophthalmology. , 2003, Acta ophthalmologica Scandinavica.

[23]  J. O'Brien,et al.  Non-physician grader reliability in measuring morphological features of the optic nerve head in stereo digital images , 2019, Eye.

[24]  Ben Parkin,et al.  A comparison of stereoscopic and monoscopic evaluation of optic disc topography using a digital optic disc stereo camera , 2001, The British journal of ophthalmology.

[25]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[26]  M. Ramsay,et al.  Tele-ophthalmology: Opportunities for improving diabetes eye care in resource- and specialist-limited Sub-Saharan African countries , 2016, Journal of telemedicine and telecare.

[27]  Kaamran Raahemifar,et al.  An Automatic Image Processing System for Glaucoma Screening , 2017, Int. J. Biomed. Imaging.

[28]  E. O'Neill,et al.  Glaucomatous optic neuropathy evaluation project: a standardized internet system for assessing skills in optic disc examination , 2011, Clinical & experimental ophthalmology.

[29]  Sunil Gupta,et al.  Diabetic retinopathy screening and the use of telemedicine , 2015, Current opinion in ophthalmology.