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. 2020 Dec;4(12):1211-1213.
doi: 10.1016/j.oret.2020.06.005. Epub 2020 Jun 18.

Predicting Postoperative Vision for Macular Hole with Automated Image Analysis

Declan C Murphy  1 , Amar V Nasrulloh  2 , Clare Lendrem  3 , Sara Graziado  4 , Mark Alberti  5 , Morten la Cour  5 , Boguslaw Obara  6 , David H W Steel  7
Affiliations

Predicting Postoperative Vision for Macular Hole with Automated Image Analysis

Declan C Murphy et al. Ophthalmol Retina. 2020 Dec.
No abstract available

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Figures

Figure 1
Figure 1
Measurements of macular hole (MH) using a 3-dimensional (3D) algorithm. Top: Representation of how the 3D model was used to measure 3D parameters. A, Diagrammatic representation of an MH including annotations that represent several MH measurements: Algorithm-derived measurements included height (computed by a smooth centerline from the center of the MH base to its top area), the maximum and minimum dimensions of the base area (BDmaj and BDmin), base area (BA), the maximum and minimum dimensions of the minimum area (defined as the minimum area in the central 20%–90% of the hole height) (MLDmaj and MLDmin), minimum area, the maximum and minimum dimensions of the top area (TDmaj and TDmin), top area, surface area, and volume. Mean diameters were taken as the mean of the maximum and minimum measurements. Four previously described size ratios were calculated: macular hole index (height/mean BD), tractional hole index (height/MLDmin), diameter hole index (mean MLD/mean BD), and area ratio factor (surface area – [top area + BA])/BA). B, Three-dimensional representation of MH reconstructed using the automated 3D algorithm. C, Spectral-domain OCT (SD-OCT) single-slice image of MH which corresponds to the 3D model in (B). Bottom: Example of differences between clinician-derived 1-dimensional MH measurements and 3D algorithm-derived measurements. Right: The outputted values for the algorithm-derived minimal and base areas (minimum axes in blue and maximum in red), and the clinician manual MLD and BD (both in red) are shown. In this case, the algorithm MLDmin was 355, MLDmaj 390, human MLD 395, BDmin 782, BDmaj 1033, and human BD 955 μm.
See this image and copyright information in PMC

References

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