Dataset Information

Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

ABSTRACT: To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is composed of a family of multi-task multi-label learning classifiers representing different levels of eye diseases derived from a predefined hierarchical eye disease taxonomy was designed. Next, we proposed a multi-level eye disease-guided loss function to learn the fine-grained variability of eye diseases features. The proposed algorithm was trained end-to-end directly using 5,325 ocular surface images from a retrospective dataset. Finally, the algorithm's performance was tested against 10 ophthalmologists in a prospective clinic-based dataset with 510 outpatients newly enrolled with diseases of infectious keratitis, non-infectious keratitis, corneal dystrophy or degeneration, and corneal neoplasm. The area under the ROC curve of the algorithm for each corneal disease type was over 0.910 and in general it had sensitivity and specificity similar to or better than the average values of all ophthalmologists. Confusion matrices revealed similarities in misclassification between human experts and the algorithm. In addition, our algorithm outperformed over all four previous reported methods in identified corneal diseases. The proposed algorithm may be useful for computer-assisted corneal disease diagnosis.

SUBMITTER: Gu H

PROVIDER: S-EPMC7576153 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

Gu Hao H Guo Youwen Y Gu Lei L Wei Anji A Xie Shirong S Ye Zhengqiang Z Xu Jianjiang J Zhou Xingtao X Lu Yi Y Liu Xiaoqing X Hong Jiaxu J

Scientific reports 20201020 1

To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is composed of a family of multi-task multi-label learning classifiers representing different levels of eye diseases derived from a predefined hierarchical eye disease taxonomy was designed. Next, we proposed a multi-level eye disease-guided loss function to learn the fine-grained variability of eye diseases features. The proposed algorithm was trained en ...[more]

PMID: 33082530

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Project description:PurposeTo develop and validate an automated deep learning (DL)-based artificial intelligence (AI) platform for diagnosing and grading cataracts using slit-lamp and retroillumination lens photographs based on the Lens Opacities Classification System (LOCS) III.DesignCross-sectional study in which a convolutional neural network was trained and tested using photographs of slit-lamp and retroillumination lens photographs.ParticipantsOne thousand three hundred thirty-five slit-lamp images and 637 retroillumination lens images from 596 patients.MethodsSlit-lamp and retroillumination lens photographs were graded by 2 trained graders using LOCS III. Image datasets were labeled and divided into training, validation, and test datasets. We trained and validated AI platforms with 4 key strategies in the AI domain: (1) region detection network for redundant information inside data, (2) data augmentation and transfer learning for the small dataset size problem, (3) generalized cross-entropy loss for dataset bias, and (4) class balanced loss for class imbalance problems. The performance of the AI platform was reinforced with an ensemble of 3 AI algorithms: ResNet18, WideResNet50-2, and ResNext50.Main outcome measuresDiagnostic and LOCS III-based grading prediction performance of AI platforms.ResultsThe AI platform showed robust diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.9992 [95% confidence interval (CI), 0.9986-0.9998] and 0.9994 [95% CI, 0.9989-0.9998]; accuracy, 98.82% [95% CI, 97.7%-99.9%] and 98.51% [95% CI, 97.4%-99.6%]) and LOCS III-based grading prediction performance (AUC, 0.9567 [95% CI, 0.9501-0.9633] and 0.9650 [95% CI, 0.9509-0.9792]; accuracy, 91.22% [95% CI, 89.4%-93.0%] and 90.26% [95% CI, 88.6%-91.9%]) for nuclear opalescence (NO) and nuclear color (NC) using slit-lamp photographs, respectively. For cortical opacity (CO) and posterior subcapsular opacity (PSC), the system achieved high diagnostic performance (AUC, 0.9680 [95% CI, 0.9579-0.9781] and 0.9465 [95% CI, 0.9348-0.9582]; accuracy, 96.21% [95% CI, 94.4%-98.0%] and 92.17% [95% CI, 88.6%-95.8%]) and good LOCS III-based grading prediction performance (AUC, 0.9044 [95% CI, 0.8958-0.9129] and 0.9174 [95% CI, 0.9055-0.9295]; accuracy, 91.33% [95% CI, 89.7%-93.0%] and 87.89% [95% CI, 85.6%-90.2%]) using retroillumination images.ConclusionsOur DL-based AI platform successfully yielded accurate and precise detection and grading of NO and NC in 7-level classification and CO and PSC in 6-level classification, overcoming the limitations of medical databases such as few training data or biased label distribution.

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.

Project description:ObjectiveTo evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure on standard retinal fundus photographs.MethodsA DLS was trained to automatically classify papilledema severity in 965 patients (2,103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1,052 photographs with mild/moderate papilledema (MP) and 1,051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of 3 independent neuro-ophthalmologists were tested in 111 patients (214 photographs, 92 with MP and 122 with SP) by calculating the area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Kappa agreement scores between the DLS and each of the 3 graders and among the 3 graders were calculated.ResultsThe DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95% confidence interval [CI] 0.89-0.96) and an accuracy, sensitivity, and specificity of 87.9%, 91.8%, and 86.2%, respectively. This performance was comparable with that of the 3 neuro-ophthalmologists (84.1%, 91.8%, and 73.9%, p = 0.19, p = 1, p = 0.09, respectively). Misclassification by the DLS was mainly observed for moderate papilledema (Frisén grade 3). Agreement scores between the DLS and the neuro-ophthalmologists' evaluation was 0.62 (95% CI 0.57-0.68), whereas the intergrader agreement among the 3 neuro-ophthalmologists was 0.54 (95% CI 0.47-0.62).ConclusionsOur DLS accurately classified the severity of papilledema on an independent set of mydriatic fundus photographs, achieving a comparable performance with that of independent neuro-ophthalmologists.Classification of evidenceThis study provides Class II evidence that a DLS using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure.

Dataset Information

Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

Publications

Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

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