Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163279
Title: Automated lesion segmentation and quantification for prediction of paradoxical worsening in patients with tubercular serpiginous-like choroiditis
Authors: Kalra, Gagan
Agarwal, Aniruddha
Marchese, Alessandro
Agrawal, Rupesh
Bansal, Reema
Gupta, Vishali
Keywords: Science::Medicine
Issue Date: 2022
Source: Kalra, G., Agarwal, A., Marchese, A., Agrawal, R., Bansal, R. & Gupta, V. (2022). Automated lesion segmentation and quantification for prediction of paradoxical worsening in patients with tubercular serpiginous-like choroiditis. Scientific Reports, 12(1), 5392-. https://dx.doi.org/10.1038/s41598-022-09338-y
Journal: Scientific Reports
Abstract: To develop and evaluate a fully automated pipeline that analyzes color fundus images in patients with tubercular serpiginous-like choroiditis (TB SLC) for prediction of paradoxical worsening (PW). In this retrospective study, patients with TB SLC with a follow-up of 9 months after initiation of anti-tubercular therapy were included. A fully automated custom-designed pipeline was developed which was initially tested using 12 baseline color fundus photographs for assessment of repeatability. After confirming reliability using Bland-Altman plots and intraclass correlation coefficient (ICC), the pipeline was deployed for all patients. The images were preprocessed to exclude the optic nerve from the fundus photo using a single-shot trainable WEKA segmentation algorithm. Two automatic thresholding algorithms were applied, and quantitative metrics were generated. These metrics were compared between PW + and PW- groups using non-parametric tests. A logistic regression model was used to predict probability of PW for assessing binary classification performance and receiver operator curves were generated to choose a sensitivity-optimized threshold. The study included 139 patients (139 eyes; 92 males and 47 females; mean age: 44.8 ± 11.3 years) with TB SLC. Pilot analysis of 12 images showed an excellent ICC for measuring the mean area, intensity, and integrated pixel intensity (all ICC > 0.89). The PW + group had significantly higher mean lesion area (p = 0.0152), mean pixel intensity (p = 0.0181), and integrated pixel intensity (p < 0.0001) compared to the PW- group. Using a sensitivity optimized threshold cut-off for mean pixel intensity, an area under the curve of 0.87 was achieved (sensitivity: 96.80% and specificity: 72.09%). Automated calculation of lesion metrics such as mean pixel intensity and segmented area in TB SLC is a novel approach with good repeatability in predicting PW during the follow-up.
URI: https://hdl.handle.net/10356/163279
ISSN: 2045-2322
DOI: 10.1038/s41598-022-09338-y
Rights: © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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