Dataset Information

Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

ABSTRACT: Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the "face" of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.

SUBMITTER: Koyama A

PROVIDER: S-EPMC8608802 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

Koyama Ayumi A Miyazaki Dai D Nakagawa Yuji Y Ayatsuka Yuji Y Miyake Hitomi H Ehara Fumie F Sasaki Shin-Ichi SI Shimizu Yumiko Y Inoue Yoshitsugu Y

Scientific reports 20211122 1

Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the "face" of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse fe ...[more]

PMID: 34811468

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Project description:PurposeTo assess clinical applicability of automatic image analysis in microbial keratitis (MK) by evaluating the relationship between biomarker measurements on slit-lamp photography (SLP) and best-corrected visual acuity (BCVA).MethodsSeventy-six patients with MK with SLP images and same-day logarithm of the minimum angle of resolution (logMAR) BCVA were evaluated. MK biomarkers (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, epithelial defect) were segmented manually by ophthalmologists and automatically by a novel, open-source, deep learning-based segmentation algorithm. Five measurements (presence, maximum width, total area, proportion of the corneal limbus area affected, centrality) were calculated. Correlations between the measurements and BCVA were calculated. An automatic regression model estimated BCVA from the measurements. Differences in performance between using manual and automatic measurements were evaluated using William's test (for correlation) and the paired-sample t-test (for absolute error).ResultsMeasurements had high correlations of 0.86 (manual) and 0.84 (automatic) with true BCVA. Estimated BCVA had average (mean ± SD) absolute errors of 0.39 ± 0.27 logMAR (manual, median: 0.30) and 0.35 ± 0.28 logMAR (automatic, median: 0.30) and high correlations of 0.76 (manual) and 0.80 (automatic) with true BCVA. Differences between using manual and automatic measurements were not statistically significant for correlations of measurements with true BCVA (P = .66), absolute errors of estimated BCVA (P = .15), or correlations of estimated BCVA with true BCVA (P = .60).ConclusionsThe proposed algorithm measured MK biomarkers as accurately as ophthalmologists. Measurements were highly correlated with and estimative of visual acuity.Translational relevanceThis study demonstrates the potential of developing fully automatic objective and standardized strategies to aid ophthalmologists in the clinical assessment of MK.

Project description:BackgroundCorneal neovascularization (CoNV) is a common sign in anterior segment eye diseases, the level of which can indicate condition changes. Current CoNV evaluation methods are time-consuming and some of them rely on equipment which is not widely available in hospitals. Thus, a fast and efficient evaluation method is now urgently required. In this study, a deep learning (DL)-based model was developed to automatically segment and evaluate CoNV using anterior segment images from a slit-lamp microscope.MethodsA total of 80 cornea slit-lamp photographs (from 80 patients) with clinically manifested CoNV were collected from December 2021 to July 2022 at Tianjin Medical University Eye Hospital. Of these, 60 images were manually labelled by ophthalmologists using ImageJ software to train the vessel segmentation network IterNet. To evaluate the performance of this automated model, evaluation metrics including accuracy, precision, area under the receiver operating characteristic (ROC) curve (AUC), and F1 score were calculated between the manually labelled ground truth and the automatic segmentations of CoNV of 20 anterior segment images. Furthermore, the vessels pixel count was automatically calculated and compared with the manually labelled results to evaluate clinical usability of the automated segmentation network.ResultsThe IterNet model achieved an AUC of 0.989, accuracy of 0.988, sensitivity of 0.879, specificity of 0.993, area under precision-recall of 0.921, and F1 score of 0.879. The Bland-Altman plot between manually labelled ground truth and automated segmentation results produced a concordance correlation coefficient of 0.989, 95% limits of agreement between 865.4 and -562.4, and the vessels pixel count's Pearson coefficient of correlation was 0.981 (P<0.01).ConclusionsThe fully automated network model IterNet provides a time-saving and efficient method to make a quantitative evaluation of CoNV using slit-lamp anterior segment images. This method demonstrates great value and clinical application potential for patient care and future research.

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Dataset Information

Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

Publications

Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

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