Clinical validation of RIA-G, an automated optic nerve head analysis software

被引:1
作者
Singh, Digvijay [1 ,2 ]
Gunasekaran, Srilathaa [3 ]
Hada, Maya [4 ]
Gogia, Varun [1 ,5 ]
机构
[1] Noble Eye Care, 1347,DLF Phase 4, Gurugram 122009, Haryana, India
[2] Narayana Superspecialty Hosp, Gurugram, Haryana, India
[3] Medanta Medicity, Div Ophthalmol, Gurugram, Haryana, India
[4] SMS Med Coll & Hosp, Jaipur, Rajasthan, India
[5] IClinix Adv Eye Ctr, New Delhi, India
关键词
Disc damage likelihood scale; fundus photograph; glaucoma; optic nerve head; RIA-G; screening; software; DIGITAL FUNDUS IMAGES; COLOR RETINAL IMAGES; GLAUCOMA DETECTION; DISC; CUP; SEGMENTATION; CLASSIFICATION; DIAGNOSIS; FEATURES; DAMAGE;
D O I
10.4103/ijo.IJO_1509_18
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose: To clinically validate a new automated glaucoma diagnosis software RIA-G. Methods: A double-blinded study was conducted where 229 valid random fundus images were evaluated independently by RIA-G and three expert ophthalmologists. Optic nerve head parameters [vertical and horizontal cup-disc ratio (CDR) and neuroretinal rim (NRR) changes] were quantified. Disc damage likelihood scale (DDLS) staging and presence of glaucoma were noted. The software output was compared with consensus values of ophthalmologists. Results: Mean difference between the vertical CDR output by RIA-G and the ophthalmologists was -0.004 +/- 0.1. Good agreement and strong correlation existed between the two [interclass correlation coefficient (ICC) 0.79; r = 0.77, P < 0.005]. Mean difference for horizontal CDR was -0.07 +/- 0.13 with a moderate to strong agreement and correlation (ICC 0.48; r = 0.61, P < 0.05). Experts and RIA-G found a violation of the inferior-superior NRR in 47 and 54 images, respectively (Cohen's kappa = 0.56 +/- 0.07). RIA-G accurately detected DDLS in 66.2% cases, while in 93.8% cases, output was within +/- 1 stage (ICC 0.51). Sensitivity and specificity of RIA-G to diagnose glaucomatous neuropathy were 82.3% and 91.8%, respectively. Overall agreement between RIA-G and experts for glaucoma diagnosis was good (Cohen's kappa = 0.62 +/- 0.07). Overall accuracy of RIA-G to detect glaucomatous neuropathy was 90.3%. A detection error rate of 5% was noted. Conclusion: RIA-G showed good agreement with the experts and proved to be a reliable software for detecting glaucomatous optic neuropathy. The ability to quantify optic nerve head parameters from simple fundus photographs will prove particularly useful in glaucoma screening, where no direct patient-doctor contact is established.
引用
收藏
页码:1089 / 1094
页数:6
相关论文
共 32 条
[1]   Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features [J].
Abramoff, Michael D. ;
Alward, Wallace L. M. ;
Greenlee, Emily C. ;
Shuba, Lesya ;
Kim, Chan Y. ;
Fingert, John H. ;
Kwon, Young H. .
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2007, 48 (04) :1665-1673
[2]   Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features [J].
Acharya, U. Rajendra ;
Dua, Sumeet ;
Du, Xian ;
Sree, Vinitha S. ;
Chua, Chua Kuang .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2011, 15 (03) :449-455
[3]   Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques [J].
Akram, M. Usman ;
Tariq, Anam ;
Khalid, Shehzad ;
Javed, M. Younus ;
Abbas, Sarmad ;
Yasin, Ubaid Ullah .
AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE, 2015, 38 (04) :643-655
[4]  
Almazroa A, 2017, CLIN OPHTHALMOL, V11, P2017, DOI 10.2147/OPTH.S140061
[5]  
[Anonymous], 2011, Eur J Sci Res
[6]   Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection? [J].
Banister, Katie ;
Boachie, Charles ;
Bourne, Rupert ;
Cook, Jonathan ;
Burr, Jennifer M. ;
Ramsay, Craig ;
Garway-Heath, David ;
Gray, Joanne ;
McMeekin, Peter ;
Hernandez, Rodolfo ;
Azuara-Blanco, Augusto .
OPHTHALMOLOGY, 2016, 123 (05) :930-938
[7]   Glaucoma risk index: Automated glaucoma detection from color fundus images [J].
Bock, Ruediger ;
Meier, Joerg ;
Nyul, Laszlo G. ;
Hornegger, Joachim ;
Michelson, Georg .
MEDICAL IMAGE ANALYSIS, 2010, 14 (03) :471-481
[8]   Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs [J].
Chakrabarty, Lipi ;
Joshi, Gopal Datt ;
Chakravarty, Arunava ;
Raman, Ganesh V. ;
Krishnadas, S. R. ;
Sivaswamy, Jayanthi .
JOURNAL OF GLAUCOMA, 2016, 25 (07) :590-597
[9]   Similarity regularized sparse group lasso for cup to disc ratio computation [J].
Cheng, Jun ;
Zhang, Zhuo ;
Tao, Dacheng ;
Wong, Damon Wing Kee ;
Liu, Jiang ;
Baskaran, Mani ;
Aung, Tin ;
Wong, Tien Yin .
BIOMEDICAL OPTICS EXPRESS, 2017, 8 (08) :3763-3777
[10]   Sparse Dissimilarity-Constrained Coding for Glaucoma Screening [J].
Cheng, Jun ;
Yin, Fengshou ;
Wong, Damon Wing Kee ;
Tao, Dacheng ;
Liu, Jiang .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2015, 62 (05) :1395-1403