September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Evaluation of posterior capsular opacification using new automated detector opacification software(ADOS)
Author Affiliations & Notes
  • Christina Mastromonaco
    Ocular Pathology McGill, Henry C. Witelson Ocular Pathology Laboratory, Montreal, Quebec, Canada
  • Matthew Balazsi
    Engineering , Medical Parachute, Montreal, Quebec, Canada
  • Pablo Zoroquiain
    Ocular Pathology McGill, Henry C. Witelson Ocular Pathology Laboratory, Montreal, Quebec, Canada
  • Patrick Logan
    Ocular Pathology McGill, Henry C. Witelson Ocular Pathology Laboratory, Montreal, Quebec, Canada
  • Shawn C Maloney
    Ocular Pathology McGill, Henry C. Witelson Ocular Pathology Laboratory, Montreal, Quebec, Canada
  • Miguel N Burnier
    Ocular Pathology McGill, Henry C. Witelson Ocular Pathology Laboratory, Montreal, Quebec, Canada
  • Footnotes
    Commercial Relationships   Christina Mastromonaco, None; Matthew Balazsi, Medical Parachute (I), Medical Parachute (P); Pablo Zoroquiain, None; Patrick Logan, None; Shawn Maloney, None; Miguel Burnier, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 2015. doi:
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    • Get Citation

      Christina Mastromonaco, Matthew Balazsi, Pablo Zoroquiain, Patrick Logan, Shawn C Maloney, Miguel N Burnier; Evaluation of posterior capsular opacification using new automated detector opacification software(ADOS). Invest. Ophthalmol. Vis. Sci. 2016;57(12):2015.

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

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Abstract

Purpose : Posterior capsular opacification(PCO) is the leading complication following cataract surgery. However, PCO grading methods vary between studies involving eyebank eyes and animal models, and no unified scoring is available to allow global analysis of PCO. Furthermore, most grading relies on subjective scoring. Therefore, software that reduces subjectivity and enhance reproducibility is needed. The aim of the study was to evaluate the reproducibility and objectivity of a novel automated custom-designed PCO detection software.

Methods : Twenty-five fixed eyes with intra ocular lenses(IOLs) were obtained from the Minnesota Eye Bank and sectioned to obtain a Miyake Apple view(MAV) image. The capsular bag(CB) with the in situ IOL was removed and imaged using the Olympus DSX100 Stereoscope. Peripheral(Soemmering’s ring area[SRA] and Soemmering’s ring intensity[SRI]) and central PCO were graded by two evaluators using the standard scale of 0=clear;1=mild;2=mild-to-moderate;3=more marked;4=severe. Agreement between the two evaluators was analysed using the weighted kappa test for SRA, SRI and central PCO scores in both MAV and CB images. Spearman’s and Pearson’s correlation was performed between the scores of the two evaluators for MAV and CB images. Finally, a custom image analyzer(Medical Parachute ADOS) was developed to detect PCO on the CB images and these data were correlated with the subjective scoring.

Results : Subjective grading had slight to moderate agreement for SRA, SRI and central PCO between graders on MAV(k=0.302, 0.489, 0.202 respectively) and CB view(k=0.457, 0.585, 0.564, respectively). Correlation between MAV and CB images on SRA, SRI and central was high for peripheral PCO and low for central PCO in both grader 1(r=0.809, 0.816, 0.466 respectively) and grader 2(r=0.766, 0.876, 0.130 respectively). Software grading was 100% reproducible(k=1.0), and SRA, SRI and central evaluation correlated strongly to moderately with standard subjective grading(r=0.41, 0.73, 0.46 respectively).

Conclusions : The ADOS correlates with previous scoring methods and is a reliable and reproducible system that allows the comparison between worldwide studies without variability. Moreover, the CB view as opposed to the MAV, allows visualization of the capsular bag directly and avoids the overlapping of other altered structures(vitreous, cornea) that may interfere with proper central PCO quantification.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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