Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis

Loo, Jessica; Woodward, Maria A.; Prajna, Venkatesh; Kriegel, Matthias; Pawar, Mercy; Khan, Mariam; Niziol, Leslie M.; Farsiu, Sina

doi:10.1167/tvst.10.12.2

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Loo et al used a deep learning-based auto-segmentation algorithm and a machine learning approach (support vector machine) to extract biomarkers of microbial keratitis (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, and epithelial defect). 30 They found a statistically significant correlation between best-corrected visual acuity and the above biomarkers (Pearson's correlation coefficient r = 0.59–0.74) except for corneal edema ( r = 0.38). However, they did not use these biomarkers to predict visual outcomes.…”

Section: Discussionmentioning

confidence: 96%

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks

Kuo,

Hsu,

Lin

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose For this study, we aimed to determine whether a convolutional neural network (CNN)-based method (based on a feature extractor and an identifier) can be applied to monitor the progression of keratitis while managing suspected microbial keratitis (MK). Methods This multicenter longitudinal cohort study included patients with suspected MK undergoing serial external eye photography at the 5 branches of Chang Gung Memorial Hospital from August 20, 2000, to August 19, 2020. Data were primarily analyzed from January 1 to March 25, 2022. The CNN-based model was evaluated via F1 score and accuracy. The area under the receiver operating characteristic curve (AUROC) was used to measure the precision-recall trade-off. Results The model was trained using 1456 image pairs from 468 patients. In comparing models via only training the identifier, statistically significant higher accuracy ( P < 0.05) in models via training both the identifier and feature extractor (full training) was verified, with 408 image pairs from 117 patients. The full training EfficientNet b3-based model showed 90.2% (getting better) and 82.1% (becoming worse) F1 scores, 87.3% accuracy, and 94.2% AUROC for 505 getting better and 272 becoming worse test image pairs from 452 patients. Conclusions A CNN-based approach via deep learning applied in suspected MK can monitor the progress/regress during treatment by comparing external eye image pairs. Translational Relevance The study bridges the gap between the investigation of the state-of-the-art CNN-based deep learning algorithm applied in ocular image analysis and the clinical care of suspected patients with MK.

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Section: Discussionmentioning

confidence: 96%

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks

Kuo,

Hsu,

Lin

et al. 2023

Trans. Vis. Sci. Tech.

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“…We used a de novo CNN architecture to quantify microbial keratitis features on SLP imaging with promising results [30]. That architecture was refined, and feature sizes were found to be correlated with the patient's visual acuity [31 ▪ ].…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and corneal diseases

Kang¹,

Ballouz²,

Woodward

2022

Current Opinion in Ophthalmology

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Purpose of reviewArtificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy.Recent findingsMost current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics.SummaryArtificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.

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Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers

Loo¹,

Teo²,

Vyas³

et al. 2023

Ophthalmology Science

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Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis

Cited by 3 publications

References 42 publications

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks

Artificial intelligence and corneal diseases

Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers

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