advertisement
Editors Selection IGR 21-2
Clinical Examination Methods: Enhancing Detection of Visual Field Progression
Comment by Riccardo Cheloni & Giovanni Montesano & David Crabb on:
112677 Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma, Mohammadzadeh V; Li L; Fei Z et al., Ophthalmology science, 2024; 4: 100423
The idea of post-processing visual fields (VFs) with some sort of spatial filter is 'as old as the hills'.1 Conventional smoothing algorithms (such as the nearest neighbour smoothing, NN) modify the sensitivity at each VF location based on sensitivities of adjacent locations according to given criteria. Deep-learning algorithms, such as variational autoencoders (VAE), can offer a more sophisticated approach to the same problem. The authors conducted a retrospective longitudinal analysis of 4232 patients (7150 eyes) with glaucoma, comparing VF progression using un-smoothed (original) and data smoothed with either a NN or a VA algorithm. Progression was detected using different methods, including trend analysis of mean total deviation (mTD) and VF index (VFI) and various pointwise progression methods. The improvements in detection for smoothed data were modest, although statistically significant (except for the VFI). Time do detect progression was shorter for smoothed data only for some of the pointwise progression methods. These effects were also statistically significant, but time gains were modest (~0.7 years).
This result is somewhat expected, because noise in global indices, such as VFI or mTD, is mostly affected by visit-to-visit performance fluctuations, which would not be addressed by smoothing methods applied to single tests.2,3
Unfortunately, the authors did not report the specificity in detecting progression with the smoothed and original data. This takes away important context to determine whether the small increase in detection rate came at the cost of increased false positive detections.
Unfortunately, the authors did not report the specificity in detecting progression with the smoothed and original data. This takes away important context to determine whether the small increase in detection rate came at the cost of increased false positive detections. One conventional approach in the literature has been to estimate false positive rates from permutations of reordered VF series.4-7
Interestingly, the authors make statements about specificity in their conclusions. Overall, authors concluded that 'smoothing' of VFs data allows earlier detection of glaucoma progression, without reducing specificity. Yet, the accuracy of the increased detection remains unclear. The results, despite being statistically significant, may be less compelling clinically, especially without the context provided by a clear quantification of specificity.
References
- Crabb DP, Fitzke FW, McNaught AI, Edgar DF, Hitchings RA. Improving the prediction of visual field progression in glaucoma using spatial processing. Ophthalmology. 1997;104(3):517-524.
- Bryan SR, Eilers PHC, Lesaffre EMEH, Lemij HG, Vermeer KA. Global Visit Effects in Point-Wise Longitudinal Modeling of Glaucomatous Visual Fields. Invest Ophthalmol Vis Sci. 2015;56(8):4283-4289.
- Wu Z, Medeiros FA. Development of a Visual Field Simulation Model of Longitudinal Point-Wise Sensitivity Changes From a Clinical Glaucoma Cohort. Transl Vis Sci Technol. 2018;7(3):22.
- Marín-Franch I, Artes PH, Turpin A, Racette L. Visual Field Progression in Glaucoma: Comparison Between PoPLR and ANSWERS. Transl Vis Sci Technol. 2021;10(14):13.
- O'Leary N, Chauhan BC, Artes PH. Visual Field Progression in Glaucoma: Estimating the Overall Significance of Deterioration with Permutation Analyses of Pointwise Linear Regression (PoPLR). Invest Ophthalmol Vis Sci. 2012;53(11):6776-6784.
- Montesano G, Garway-Heath DF, Ometto G, Crabb DP. Hierarchical Censored Bayesian Analysis of Visual Field Progression. Transl Vis Sci Technol. 2021;10(12):4.
- Zhu H, Crabb DP, Ho T, Garway-Heath DF. More Accurate Modeling of Visual Field Progression in Glaucoma: ANSWERS. Invest Ophthalmol Vis Sci. 2015;56(10):6077-6083.
Comments
The comment section on the IGR website is restricted to WGA#One members only. Please log-in through your WGA#One account to continue.Log-in through WGA#One
