May 2006
Volume 47, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2006
Automated Analysis of Heidelberg Retina Tomograph Optic Disc Images by Glaucoma Probability Score – Comparison With Moorfields Regression Analysis
Author Affiliations & Notes
  • A. Coops
    University of Manchester, Manchester, United Kingdom
    School of Medicine,
  • P.H. Artes
    University of Manchester, Manchester, United Kingdom
    Life Sciences,
  • A.J. Kwartz
    University of Manchester, Manchester, United Kingdom
    School of Medicine,
  • D.B. Henson
    University of Manchester, Manchester, United Kingdom
    School of Medicine,
    Life Sciences,
  • Footnotes
    Commercial Relationships  A. Coops, None; P.H. Artes, None; A.J. Kwartz, None; D.B. Henson, None.
  • Footnotes
    Support  NHS HTA Project 95/18/04
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 4344. doi:
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      A. Coops, P.H. Artes, A.J. Kwartz, D.B. Henson; Automated Analysis of Heidelberg Retina Tomograph Optic Disc Images by Glaucoma Probability Score – Comparison With Moorfields Regression Analysis . Invest. Ophthalmol. Vis. Sci. 2006;47(13):4344.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

To compare the diagnostic performance of the Glaucoma Probability Score (GPS), an automated method of optic disc analysis (Swindale et al., IOVS 2000) with that of the Moorfields Regression Analysis (MRA).

 
Methods:
 

Heidelberg Retina Tomograph (HRT) images were obtained from one eye of 123 glaucoma patients (mean age, 69.1 yrs; mean MD, –3.7, range +1.9 to –9.7 dB) and 96 healthy controls (mean age 59.6 yrs; mean MD –0.2, range +2.5 to –3.7 dB). For MRA, contour lines were drawn by experienced clinicians. The diagnostic performances of GPS and MRA were evaluated by including "borderline" classifications either as test–positives (most sensitive criteria) or as test–negatives (most specific criteria). Effects of disc size were evaluated by stratifying the sample into 3 equal subgroups based on disc area.

 
Results:
 

In 5 (4%) of glaucoma patients, and 11 (8%) of controls, the GPS failed to provide a complete global and sectoral classification. While we could not identify a single distinct cause for failure in the glaucoma group, failures in the controls occurred most often (8/11) with small crowded discs. In those subjects that were successfully classified by the GPS (118 glaucoma patients, 85 controls; Table 1), the diagnostic performances of GPS and MRA were similar (ROC areas of 0.81 and 0.78, respectively; p=0.41). With the GPS, sensitivity/specificity values were 81% and 62% (most sensitive criteria) and 65% and 92% (most specific criteria). Combining GPS and MRA analyses did not increase diagnostic performance significantly (ROC area 0.84). Both GPS and MRA classified small optic discs (<1.75 mm2) more conservatively (with lower sensitivity and greater specificity) than medium and large discs (p<0.05, chi–square test).  

 
Conclusions:
 

The diagnostic performance of the contour–line independent GPS analysis was very similar to that of the MRA. However, clinicians need to take into account disc size when interpreting automated and semi–automated classification of optic disc status with the HRT.

 
Keywords: imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • imaging/image analysis: clinical • optic disc 
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