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Effective automatic classification methods via deep learning for multi-type infectious keratitis diagnosis
Yang Zhang1, Yuning Wang2,3, Yingnan Xu4
1Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing Tongren Eye Center, Capital Medical University, Beijing, 100005, China.
Background:
Infectious keratitis (IK) is a leading cause of corneal blindness, typically caused by bacteria, fungi, viruses, or parasites. Prompt diagnosis and treatment are crucial, yet the absence of a gold standard for pathogen identification complicates timely interventions. Corneal cultures can be time-consuming and prone to false positives, highlighting the need for an automated classification system.
Methods:
From March 2018 to November 2023, 1,065 diffuse pattern slit-lamp images were collected to develop a deep learning system. Five models-EfficientNet_B0, EfficientNet_V2_S, ResNet50, Vision Transformer (ViT), and DeepIK-were trained for corneal infection classification. Key evaluation metrics included accuracy, precision, recall, F1-score, weighted Cohen's Kappa, and the Receiver Operating Characteristic (ROC) curve.
Results:
The EfficientNet_B0 model achieved superior performance across all metrics, with an accuracy of 75.2% (95% CI: 69.6% - 80.8%), sensitivity of 74.9% (95% CI: 69.9% - 80.3%), specificity of 93.8% (95% CI: 92.4% - 95.2%), F1-Score of 74.3% (95% CI: 68.7% - 79.8%), Kappa value of 0.689 (95% CI: 0.618-0.759), and AUC of 0.943 (95% CI: 0.920-0.962).
Conclusions:
The EfficientNet_B0 model effectively identified normal eyes and four IK types, showcasing the potential of deep learning in diagnosing keratitis infections. Future enhancements with larger datasets could improve accuracy, facilitating timely treatments and better outcomes for patients.
Insights
A deep learning model, EfficientNet_B0, shows promise for diagnosing infectious keratitis (IK) from eye images. This automated system could speed up diagnosis, improving patient outcomes for this leading cause of corneal blindness.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Infectious keratitis (IK) is a major cause of corneal blindness, often due to microbial infections.
- Current diagnostic methods like corneal cultures are slow and may be inaccurate, necessitating automated solutions.
- Early detection and treatment are vital for preventing vision loss from IK.
Purpose of the Study:
- To develop and evaluate a deep learning system for automated classification of infectious keratitis using slit-lamp images.
- To compare the performance of various deep learning models in identifying corneal infections.
Main Methods:
- A dataset of 1,065 diffuse pattern slit-lamp images was collected between March 2018 and November 2023.
- Five deep learning models (EfficientNet_B0, EfficientNet_V2_S, ResNet50, Vision Transformer, DeepIK) were trained for corneal infection classification.
- Performance was assessed using accuracy, precision, recall, F1-score, Cohen's Kappa, and ROC analysis.
Main Results:
- EfficientNet_B0 demonstrated superior performance with 75.2% accuracy, 74.9% sensitivity, 93.8% specificity, and an AUC of 0.943.
- The model achieved a Kappa value of 0.689, indicating good agreement.
- All key performance metrics favored the EfficientNet_B0 model over others.
Conclusions:
- The EfficientNet_B0 deep learning model effectively distinguishes between normal eyes and four types of infectious keratitis.
- This AI approach shows significant potential for improving the diagnosis of keratitis.
- Larger datasets are recommended for future research to further enhance diagnostic accuracy and patient care.
Related Concept Videos
01:28Methods of Classification and Identification
01:25Rapid Identification of Pathogens
01:24Automated Microbial Diagnostics