Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Hu, Shaodan; Sun, Yiming; Li, Jinhao; Xu, Pinghong; Xu, Mingyu; Zhou, Yifan; Wang, Yaqi; Wang, Shuai; Ye, Juan

doi:10.3390/jpm13030519

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…The EffecientNetV2-M showed the best performance with accuracy of 0.735 and specificity of 0.904, which was also superior to two ophthalmologists. The overall AUC of the EffecientNetV2-M was 0.85 with 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK [61]. Kuo et al explored eight single and four ensemble DL models to diagnose BK caused by Pseudomonas aeruginosa.…”

Section: Artificial Intelligence -Deep Learning Methodsmentioning

confidence: 99%

Diagnostic Imaging in Infectious Keratitis: A Review

Cabrera-Aguas,

Watson

2023

Preprint

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Infectious keratitis (IK) is among the top 5 leading causes of blindness globally. Early diagnosis is key to guide an appropriate therapy to avoid complications such as vision impairment and blindness. Culture of corneal scrapes is the initial diagnostic test to grow and identify the causal organism. Alternative diagnostic tools include imaging such as with optical coherence tomography (OCT) and in vivo confocal microscopy (IVCM). OCT’s advantage is its ability to accurately determine the depth and extent of the corneal ulceration, infiltrates and haze; therefore, characterizing the severity and progression of the infection. However, it is not a preferred choice in the diagnostic tool package for infectious keratitis. IVCM is a great aid in the diagnosis of fungal and Acanthamoeba keratitis with overall sensitivities of 66-74% and 80-100%, and specificity of 78-100% and 84-100%, respectively. Recently the use of deep learning (DL) models in IK has been shown to aid diagnosis via image recognition. Most of the studies that have developed DL models to diagnose the different types of IK have utilised photographs from digital camaras, slit-lamp images, or IVCM images. Some studies have used extremely efficient single DL algorithms to train their models and others used ensemble approaches. This technology is likely to assist in the diagnosis of IK in some years. Further work is needed to examine and validate the clinical performance of the DL models in the real-world setting and to evaluate whether this technology improves patient clinical outcomes. The scope of this review was to provide a recent update on the diagnostic imaging tools in IK

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Section: Artificial Intelligence -Deep Learning Methodsmentioning

confidence: 99%

Diagnostic Imaging in Infectious Keratitis: A Review

Cabrera-Aguas,

Watson

2023

Preprint

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“…Moreover, in a study by Hu and colleagues [35], six deep learning algorithms were tested and compared with the performance of two ophthalmologists. Out of these, the best algorithm showed an accuracy and a specificity of 73.5% and 90.4%, respectively, in differentiating between different keratitis types.…”

Section: Discriminative Modelsmentioning

confidence: 99%

Artificial Intelligence and Infectious Keratitis: Where Are We Now?

Soleimani,

Cheraqpour,

Sadeghi

et al. 2023

Life

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Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit–lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.

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“…The EffecientNetV2-M showed the best performance with 0.735 accuracy, 0.68 sensitivity and 0.904 specificity, which was also superior to two ophthalmologists (accuracy of 0.661 and 0.685). The overall AUROC of the EffecientNetV2-M was 0.85, with 1.00 for normal cornea, 0.87 for VK, 0.87 for FK and 0.64 for BK [71]. Kuo et al, in 2022, explored eight single and four ensemble DL models to diagnose BK caused by Pseudomonas aeruginosa.…”

Section: Deep Learning Models In Infectious Keratitismentioning

confidence: 99%

Updates in Diagnostic Imaging for Infectious Keratitis: A Review

Cabrera-Aguas,

Watson

2023

Diagnostics

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Infectious keratitis (IK) is among the top five leading causes of blindness globally. Early diagnosis is needed to guide appropriate therapy to avoid complications such as vision impairment and blindness. Slit lamp microscopy and culture of corneal scrapes are key to diagnosing IK. Slit lamp photography was transformed when digital cameras and smartphones were invented. The digital camera or smartphone camera sensor’s resolution, the resolution of the slit lamp and the focal length of the smartphone camera system are key to a high-quality slit lamp image. Alternative diagnostic tools include imaging, such as optical coherence tomography (OCT) and in vivo confocal microscopy (IVCM). OCT’s advantage is its ability to accurately determine the depth and extent of the corneal ulceration, infiltrates and haze, therefore characterizing the severity and progression of the infection. However, OCT is not a preferred choice in the diagnostic tool package for infectious keratitis. Rather, IVCM is a great aid in the diagnosis of fungal and Acanthamoeba keratitis with overall sensitivities of 66–74% and 80–100% and specificity of 78–100% and 84–100%, respectively. Recently, deep learning (DL) models have been shown to be promising aids for the diagnosis of IK via image recognition. Most of the studies that have developed DL models to diagnose the different types of IK have utilised slit lamp photographs. Some studies have used extremely efficient single convolutional neural network algorithms to train their models, and others used ensemble approaches with variable results. Limitations of DL models include the need for large image datasets to train the models, the difficulty in finding special features of the different types of IK, the imbalance of training models, the lack of image protocols and misclassification bias, which need to be overcome to apply these models into real-world settings. Newer artificial intelligence technology that generates synthetic data, such as generative adversarial networks, may assist in overcoming some of these limitations of CNN models.

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Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Cited by 4 publications

References 37 publications

Diagnostic Imaging in Infectious Keratitis: A Review

Diagnostic Imaging in Infectious Keratitis: A Review

Artificial Intelligence and Infectious Keratitis: Where Are We Now?

Updates in Diagnostic Imaging for Infectious Keratitis: A Review

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