Literature DB >> 21051727

Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs.

Carol Yim-lui Cheung1, Huiqi Li, Ecosse L Lamoureux, Paul Mitchell, Jie Jin Wang, Ava Grace Tan, Lily K Johari, Jiang Liu, Joo Hwee Lim, Tin Aung, Tien Yin Wong.   

Abstract

PURPOSE: To validate a new computer-aided diagnosis (CAD) imaging program for the assessment of nuclear lens opacity.
METHODS: Slit-lamp lens photographs from the Singapore Malay Eye Study (SiMES) were graded using both the CAD imaging program and manual assessment method by a trained grader using the Wisconsin Cataract Grading System. Cataract was separately assessed clinically during the study using Lens Opacities Classification System III (LOCS III). The repeatability of CAD and Wisconsin grading methods were assessed using 160 paired images. The agreement between the CAD and Wisconsin grading methods, and the correlations of CAD with Wisconsin and LOCS III were assessed using the SiMES sample (5547 eyes from 2951 subjects).
RESULTS: In assessing the repeatability, the coefficient of variation (CoV) was 8.10% (95% confidence interval [CI], 7.21-8.99), and the intraclass correlation coefficient (ICC) was 0.96 (95% CI, 0.93-0.96) for the CAD method. There was high agreement between the CAD and Wisconsin methods, with a mean difference (CAD minus Wisconsin) of -0.02 (95% limit of agreement, -0.91 and 0.87) and an ICC of 0.81 (95% CI, 0.80-0.82). CAD parameters were also significantly correlated with LOCS III grading (all P < 0.001).
CONCLUSIONS: This new CAD imaging program assesses nuclear lens opacity with results comparable to the manual grading using the Wisconsin System. This study shows that an automated, precise, and quantitative assessment of nuclear cataract is possible.

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Year:  2011        PMID: 21051727     DOI: 10.1167/iovs.10-5427

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  3 in total

1.  DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Authors:  Tiarnan D L Keenan; Qingyu Chen; Elvira Agrón; Yih-Chung Tham; Jocelyn Hui Lin Goh; Xiaofeng Lei; Yi Pin Ng; Yong Liu; Xinxing Xu; Ching-Yu Cheng; Mukharram M Bikbov; Jost B Jonas; Sanjeeb Bhandari; Geoffrey K Broadhead; Marcus H Colyer; Jonathan Corsini; Chantal Cousineau-Krieger; William Gensheimer; David Grasic; Tania Lamba; M Teresa Magone; Michele Maiberger; Arnold Oshinsky; Boonkit Purt; Soo Y Shin; Alisa T Thavikulwat; Zhiyong Lu; Emily Y Chew
Journal:  Ophthalmology       Date:  2022-01-03       Impact factor: 14.277

2.  Age and axial length on peripapillary retinal nerve fiber layer thickness measured by optical coherence tomography in nonglaucomatous Taiwanese participants.

Authors:  Pai Huei Peng; Sheng Yao Hsu; Wei Shin Wang; Mei Lan Ko
Journal:  PLoS One       Date:  2017-06-08       Impact factor: 3.240

Review 3.  Application of artificial intelligence in cataract management: current and future directions.

Authors:  Laura Gutierrez; Jane Sujuan Lim; Li Lian Foo; Wei Yan Ng; Michelle Yip; Gilbert Yong San Lim; Melissa Hsing Yi Wong; Allan Fong; Mohamad Rosman; Jodhbir Singth Mehta; Haotian Lin; Darren Shu Jeng Ting; Daniel Shu Wei Ting
Journal:  Eye Vis (Lond)       Date:  2022-01-07
  3 in total

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