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Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening
  1. Sang Beom Han1,
  2. Hee Kyung Yang2,
  3. Ji Eun Oh3,
  4. Kwang Gi Kim3,
  5. Jeong-Min Hwang2
  1. 1Department of Ophthalmology, Kangwon National University Hospital, Kangwon National University Graduate School of Medicine, Chuncheon, Korea
  2. 2Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul, Korea
  3. 3Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, Korea
  1. Correspondence to Dr Jeong-Min Hwang, Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 166 Gumiro, Bundang-gu, Seongnam, Gyeonggi-do 463-707, Korea; hjm{at}snu.ac.kr and Dr Kwang Gi Kim, Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, 410-769, Korea; kimkg{at}ncc.re.kr

Abstract

Background/aims To evaluate the efficacy of a new automatic computer-aided detection (CAD) system for mass screening of retinal nerve fibre layer (RNFL) defects in a large population using fundus photographs.

Methods Among the fundus photographs of 1200 consecutive subjects who visited a healthcare centre, a total of 2270 photographs appropriate for analysis were tested. The photographs were first reviewed by two expert ophthalmologists for detection of RNFL defects (gold standard manual detection). The images were then analysed using an automatic CAD system for detecting RNFL defects in various cases of glaucomatous and non-glaucomatous optic neuropathy. A free-response receiver operating characteristics curve was generated to evaluate the validity of the CAD system. The results of the automatic detection were compared with those of manual detection, and sensitivity and specificity of the CAD system were calculated.

Results In manual detection of 2270 photographs, 41 RNFL defects from 36 photographs (1.6%) were detected, and no RNFL defects were found in 2234 photographs (98.4%). The sensitivity of the CAD system for detecting RNFL defects was 90.2% (37/41 RNFL defects) and the specificity was 72.5% (1620/2234 photographs with no RNFL defects) at a false-positive rate of 0.36 per image.

Conclusions The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre. The proposed algorithm can be useful for clinicians in screening RNFL defects in healthcare centres. The false-positive rate is still unsatisfactory, although improved compared with the previous study. Further studies are needed to enhance the speed and specificity of image analysis using the CAD system.

  • Glaucoma
  • Imaging

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