Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images

AbstractGlobally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.

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AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and “Plus” Lesion Detection in Fundus Images

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.719262 ◽

2021 ◽

Vol 9 ◽

Author(s):

Li Lu ◽

Peifang Ren ◽

Xuyuan Tang ◽

Ming Yang ◽

Minjie Yuan ◽

...

Keyword(s):

Cross Validation ◽

External Validation ◽

Weighted Kappa ◽

Lesion Detection ◽

Validation Dataset ◽

Pathologic Myopia ◽

Fundus Images ◽

Robust Performance ◽

Retinal Fundus Images ◽

Retinal Fundus

Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and “Plus” lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)–models for automatic PM identification, MM classification, and “Plus” lesion detection based on retinal fundus images.Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts’ performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed.Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the “Plus” lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation.Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.

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Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

10.21203/rs.3.rs-402433/v1 ◽

2021 ◽

Author(s):

Edward Korot ◽

Nikolas Pontikos ◽

Xiaoxuan Liu ◽

Siegfried K Wagner ◽

Livia Faes ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Model Performance ◽

Validation Dataset ◽

Internal Validation ◽

Fundus Photographs ◽

The Uk ◽

Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

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Predicting sex from retinal fundus photographs using automated deep learning

Scientific Reports ◽

10.1038/s41598-021-89743-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Edward Korot ◽

Nikolas Pontikos ◽

Xiaoxuan Liu ◽

Siegfried K. Wagner ◽

Livia Faes ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Model Performance ◽

Validation Dataset ◽

Internal Validation ◽

Fundus Photographs ◽

The Uk ◽

Retinal Fundus

AbstractDeep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

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Automatic Classification of Exudates in Color Fundus Images Using an Augmented Deep Learning Procedure

Proceedings of the Third International Symposium on Image Computing and Digital Medicine - ISICDM 2019 ◽

10.1145/3364836.3364843 ◽

2019 ◽

Author(s):

Lei Wang ◽

Ying Huang ◽

Bing Lin ◽

Wencan Wu ◽

Hao Chen ◽

...

Keyword(s):

Deep Learning ◽

Automatic Classification ◽

Fundus Images ◽

Learning Procedure ◽

Color Fundus Images

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Dehazing buried tissues in retinal fundus images using a multiple radiance pre-processing with deep learning based multiple feature-fusion

Optics & Laser Technology ◽

10.1016/j.optlastec.2020.106908 ◽

2021 ◽

Vol 138 ◽

pp. 106908

Author(s):

Laishram Mona Devi ◽

Kanan Wahengbam ◽

Aheibam Dinamani Singh

Keyword(s):

Deep Learning ◽

Feature Fusion ◽

Fundus Images ◽

Multiple Feature ◽

Multiple Feature Fusion ◽

Retinal Fundus Images ◽

Retinal Fundus

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A deep learning framework for the detection of Plus disease in retinal fundus images of preterm infants

Journal of Applied Biomedicine ◽

10.1016/j.bbe.2021.02.005 ◽

2021 ◽

Author(s):

Sivakumar Ramachandran ◽

P. Niyas ◽

Anand Vinekar ◽

Renu John

Keyword(s):

Deep Learning ◽

Preterm Infants ◽

Fundus Images ◽

Learning Framework ◽

Retinal Fundus Images ◽

Retinal Fundus

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An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3469841 ◽

2021 ◽

Vol 17 (3s) ◽

pp. 1-24

Author(s):

Mohammad Shorfuzzaman ◽

M. Shamim Hossain ◽

Abdulmotaleb El Saddik

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Vision Loss ◽

Final Diagnosis ◽

Learning Rate ◽

Ensemble Model ◽

Fundus Images ◽

Robust Detection ◽

Retinal Fundus Images ◽

Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

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