Diagnostic Imaging for Infectious Keratitis

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Maria Cabrera-Aguas	--	3486	2023-11-15 00:20:21	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	3486	2023-11-17 01:41:46	\|

This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13213358

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 diagnosing fungal and Acanthamoeba keratitis with overall sensitivities of 66–74% and 80–100% and specificity of 78–100% and 84–100%, respectively. Deep learning (DL) models have been shown to be promising aids for diagnosing IK via image recognition.

infectious keratitis corneal imaging in vivo confocal microscopy optical coherence tomography artificial intelligence deep learning microbial keratitis

1. Introduction

The cornea is essential for vision and contributes three-quarters of the eye’s refractive power. While cataracts in the developing world and age-related macular degeneration (in older patients) in the developed world are recognised as the leading causes of visual impairment, corneal blindness affects all ages and is a leading cause of irreversible visual impairment ^[1]. Despite continuous efforts to combat this disease, infectious corneal ulceration (infectious keratitis) still receives insufficient global attention ^[1]. Corneal ulceration (opacity) is among the top five causes of blindness and vision impairment globally ^[1]^[2]^[3]. In 2020, it was reported that 2.096 million people over 40 years of age suffered from blindness, and 3.372 million people have moderate to severe vision impairment from non-trachomatous corneal opacity ^[2].

Infectious keratitis is an ocular emergency requiring prompt attention as it can progress very rapidly, leading to severe complications, such as losing eyesight or even the eye ^[4]. Infectious keratitis is diagnosed using the patient’s history, clinical examination under a slit lamp and the microbiology results from staining and culture of the scrapings from the corneal ulcer ^[4]^[5]. The key to making the diagnosis is the identification of key features on slit lamp examination with fluorescein staining ^[6]^[7]. Along with aiding with diagnosis, slit lamp biomicroscopy is used in determining the severity of the infection ^[8].

Infectious keratitis is most often caused by bacteria. Other important causal organisms include viruses, fungi and parasites ^[4]. Bacterial keratitis is mostly caused by Staphylococci spp., Pseudomonas aeruginosa and Streptococcus pneumoniae. Patients generally manifest with a red eye, discharge, an epithelial defect, corneal infiltrates and sometimes hypopyon ^[9]. Viral keratitis is commonly caused by herpes simplex virus (HSV). The type of HSV keratitis is determined based on the clinical features observed on the slit lamp examination.

Fungal keratitis is caused by filamentous (Fusarium spp., Aspergillus spp.) or yeast (Candida spp.) fungi. Clinical findings may include a corneal ulcer with irregular feathery margins, elevated borders, dry, rough texture, satellite lesions, Descemet’s folds, hypopyon, ring infiltrate, endothelial plaque, anterior chamber cells and keratic precipitates ^[10]^[11]^[12]. Parasitic keratitis caused by Acanthamoeba spp. is a usual cause of infectious keratitis, which is a typically chronic and progressive condition. A unilateral or paracentral corneal ulcer with a ring infiltrate is commonly seen in patients with this infection.

The culture of corneal scrapings is the gold standard for diagnosis and to identify and isolate the causal organism of the infection ^[4]^[5]^[13]. However, the positive rate of such cultures ranges from 38–66% ^[4]. Antimicrobial resistance testing is routinely performed on bacterial isolates, with the results typically available after 48 h. Empiric antimicrobial therapy is commenced whilst awaiting these results to prevent a severe complication ^[4]. Due to the limitations of corneal scraping in recent years, a range of imaging techniques have been used to aid the diagnosis of and severity grading of infectious keratitis. Understanding the current status of corneal imaging for keratitis will be of benefit to clinicians in practice and researchers in the field. Imaging technologies may enable early diagnosis of the different types of infectious keratitis.

2. Slit Lamp Biomicroscopy

The slit lamp is a stereoscopic biomicroscope which produces a focused beam of light with different heights, widths and angles to visualise and measure the anatomy of the adnexa and anterior segment of the eye ^[14]. The slit lamp is essential for the examination and diagnosis of patients with infectious keratitis ^[4]. Slit lamp photography started in the late 1950s, but the arrival of digital cameras in the 2000s substantially facilitated its use in ophthalmology ^[15]. There are two types of digital cameras: single lens reflex (SLR) or ‘point-and-shoot’. The choice of either type of camera for use in clinics depends on the budget, ease of use, photographic requirements, and ability of the user. SLRs are heavier, bulkier, and more costly than ‘point-to-view’ cameras. Another key feature to consider in selecting a camera is the megapixel resolution. One megapixel is equivalent to 1 million pixels. A photograph taken at 6 megapixels can be printed up to 11 inches (28 cm) × 14 inches (35.5 cm) without ‘pixelation’ (visible pixels). A 3.2-megapixel camera can meet the needs of clinical photography ^[16].

An alternative to digital cameras is the ‘smartphone’, which was released in the late 2000s. A smartphone is a mobile phone that has the technology to run many advanced applications. In ophthalmology, such applications may include patient and physician education tools, testing tools, and photography ^[17]. Newer smartphones have rear camera resolution of up to 50 megapixels with image sensors, lens correction and optical plus electronic image stabilisation ^[18]. Due to the difficulty of holding a smartphone while operating the slit lamp, adapters to mount smartphones have been developed ^[15]. Limitations with the use of adapters include that they are not universal; that is, they are designed for certain smartphones or slit lamp models, and when they are attached to the slit lamp, binocular operation of the slit lamp is not feasible.

To overcome these issues, Muth et al. evaluated a new adapter that could be mounted in any smartphone or slit lamp and easily moved aside to enable binocular use of the slit lamp ^[15]. The images taken with the smartphone had an overall high quality and were as equally as good as the images taken with a slit lamp camera ^[15]. Currently, smartphones have built-in cameras appropriate for slit lamp imaging. If the smartphone is placed and handled adequately, the slit lamp image quality depends on three factors: the smartphone camera sensor’s resolution, the resolution of the slit lamp or microscope and the focal length of the smartphone camera system. The final image result depends on the smartphone’s software settings including autofocus, shutter speed, and internal post-processing algorithms when a compressed image format is used (i.e: .jpg).

3. Optical Coherence Tomography

In 1994, it was shown that the anterior chamber could be imaged using optical coherence tomography (OCT) with the same frequency (830 nm) used in posterior chamber imaging ^[7]^[19]. This advance enabled imaging and analysis of structures, such as the cornea and the anterior chamber angle. The predominant market for OCT applications has been for retinal imaging. The first commercial anterior segment devices, known as anterior segment optical coherence tomography (AS-OCT) that became commercially available were modified posterior chamber devices. Some of these AS-OCT devices were stand-alone, and others required modifications, such as an additional lens to modify the posterior chamber OCT devices. Modifications in 2001 to the light source and lens of the AS-OCT enabled higher frequency waves (1310 nm) to allow a higher resolution of the image and more precise measurement ^[7]^[20].

AS-OCT is now a well-recognised method for imaging the cornea and is often used in anterior chamber angle imaging for glaucoma ^[7]^[13]. The advantage of the AS-OCT is its ability to accurately measure the depth and width of the corneal ulcer, infiltrates, and haze to monitor the progress of corneal pathologies, such as superficial and deep infectious keratitis ^[13]. Other applications of the AS-OCT include the ability to measure the corneal thickness to determine the risk of corneal perforation, prediction of corneal cross-linking (PACK-CXL) response in infectious keratitis and highlighting corneal interface pathologies, such as interface infectious keratitis following lamellar keratoplasty (hyper-reflective band at the graft-host interface) ^[21], post-LASIK epithelial ingrowth (flap-host interface) ^[22] and valvular and direct non-traumatic corneal perforations associated with infectious keratitis ^[13]^[23]. With the increasing popularity of Small Incision Lenticule Extraction (SMILE) as a refractive procedure, AS-OCT will have a role in identifying and defining interface infections ^[24]^[25].

3.1. Types of AS-OCT

3.1.1. Spectral Domain OCT

Spectral-domain (SD) AS-OCT assesses the frequency spectrum of the interference between a stationary reference mirror and the reflected light. Spatial and structural information are measured at the same time at all echo time delays (axial pixels). The advantage of a concurrent evaluation of all axial-depth scan (A-scan) pixels is that it enables an increase in scanning speeds of up to 100,000 A-scans/s with commercial devices and up to 20.8 million A-scans/s with research devices ^[26]. A higher resolution, up to 2 microns, had been achieved via a broader spectrum light source with SDAS-OCT ^[7]^[27]. SDAS-OCT has been used to assess corneal pathology, such as scarring and thinning and measure corneal thickness, including epithelial layer thickness.

The use of SD AS-OCT in infectious keratitis has been reported in one cross-sectional study of 22 eyes by Soliman et al. ^[28], one case series with four eyes by Yamazaki et al. ^[7]^[29] and one cross-sectional study of 25 patients by Oliveira et al. ^[30]. Using SD-OCT (RTVue-100; Optovue, Freemont, CA, USA), Soliman et al. were able to distinguish infiltrates from scars, potentially allowing for the distinction of different stages of infectious keratitis as well as potentially identifying non-infective causes ^[7]^[13]^[28].

The characteristics identified on SDAS-OCT were grouped by the causal microbes to try and identify patterns in infectious keratitis. For example, the localized small stromal cystic spaces interpreted as localised stromal necrosis and full-thickness large stromal cystic spaces as diffuse stromal necrosis were found to be only associated with fungal infections due to Aspergillus species. On the other hand, diffuse stromal thinning with an epithelial defect and positive fluorescein staining was only found with Staphylococcus aureus infections. Therefore, SD AS-OCT has the potential to assist in the identification of the causal microbe in cases of IK.

3.1.2. Swept Source

Swept-source OCT (SS AS-OCT) scans faster than SD AS-OCT with speeds of up to 200,000 A-scans/s with modern commercial devices and millions of A-scans/s with laboratory devices. This type of tomography utilises a laser that rapidly sweeps through frequencies across a broad spectrum opposite to SD AS-OCT, which utilises a broad-bandwidth light source ^[26]^[31]. SS AS-OCT allows high scan speeds (shorter scan speeds and higher scan density), less depth-dependent signal-to-noise ratio and resolution drop-off, and improved scan quality (less eye movement). Most SS AS-OCT devices also utilise a centre wavelength of approximately 1050 nm (SD AS-OCT uses a centre wavelength of approximately 850 nm), which allows for greater axial depth imaging and better visualisation of deeper ocular structures, such as the choroid and the lamina cribrosa (LC) ^[26]^[32]^[33].

Nineteen of 68 (28%) patients in this study presented complications from infectious keratitis. Twelve patients required tarsorrhaphy or corneal glueing; six, deep anterior lamellar keratoplasty (DALK); and one, vitrectomy. The average score of the surgical patients was 19. The patients who needed surgical interventions had a significantly higher score than those who resolved without intervention (p = 0.042). There was no statistical association between a single feature of AS-OCT and a surgical outcome. There was a significant correlation between patients whose scores on day six were the same or higher than day zero and the requirement of surgery (p = 0.003).

4. In Vivo Confocal Microscopy

In vivo confocal microscopy (IVCM) provides a high-resolution, in vivo assessment of corneal structures and pathologies at a cellular and subcellular level ^[13]. IVCM provides corneal images with 1 µm resolution of the three cornea layers, nerves and cells and is sufficient to produce images larger than a few micrometres of filamentous fungi or Acanthamoeba cysts ^[4]^[11]^[12]^[34]. A third-generation laser scanning confocal microscope Heidelberg Retinal Tomograph (HRT3) in conjunction with the Rostock Cornea Module (RCM) (Heidelberg Engineering, Germany) utilised 670 nm red wavelength and produced high-resolution images with lateral resolution close to 1 µm, axial resolution of 7.6 µm and 400× magnification.

This advance permitted the identification of yeasts, which first-generation confocal microscopes could not resolve ^[11]^[12]^[13]^[35]^[36].

IVCM has mainly been used in the evaluation of fungal and Acanthamoeba keratitis (AK) due to its axial limitation of 5–7 µm, which is not sufficient to detect bacteria (less than 5 µm) and viruses (in nanometres) ^[4]^[13]^[36]. IVCM has been a great ally to microbiological diagnostic tests as it can identify these organisms rapidly, overcoming the test’s variable positive rate of between 40–99% and a turnaround time of up to 2 weeks ^[13]. Therefore, IVCM is an imaging diagnostic test that is valuable in guiding initial therapy.

IVCM sensitivity ranges between 66.7% and 94%, and specificity between 78% and 100% in fungal keratitis ^[4]^[11]^[36]^[37]^[38]^[39]. Aspergillus spp. and Fusarium spp. are the main causal organisms of fungal keratitis ^[4]^[36]. With IVCM, Aspergillus spp. are identified as 5–10 µm in diameter and have septate hyphae with 45-degree angle dichotomous branches. On the other hand, Fusarium spp. typically branch at an angle of 90 degrees. In comparison, basal corneal epithelial nerves have more regular branching than hyper-reflective elements, and stromal nerves’ are between 25–50 µm in diameter versus Aspergillus spp. and Fusarium spp. diameter of 200–400 µm in length ^[13]^[36].

IVCM is a key diagnostic test in AK with an overall sensitivity of 80–100% and specificity of 84–100% ^[13]^[37]^[38]^[39]^[40]^[41]. Acanthamoeba spp. can present as cysts or trophozoites. Cysts (dormant form) appear as hyper-reflective, spherical and well-defined double-wall structures of ~15–30 µm in diameter in the epithelium or stroma. Trophozoites (active form) appear as hyper-reflective structures of 25–40 µm, which are difficult to discriminate from leukocytes and keratocyte nuclei ^[4]^[13]^[42]. Acanthamoeba spp. can also present as bright spots, signet rings and perineural infiltrates. Perineural infiltrates are a pathognomonic characteristic of AK, which appear as reflective patchy lesions with surrounding hyper-reflective spindle-shaped materials ^[13].

The advantages of IVCM include ‘non-invasiveness’: the ability to rapidly identify in real time the causal organism and to determine the depth of the infection. This can guide the antimicrobial therapy and assist in monitoring the infection. Early identification and treatment of AK have been associated with improved prognosis ^[4]^[43]. Imaging by IVCM also facilitates longitudinal exams in the same patient, which may be of use in determining resolution and provides quantitative analysis of all cornea layers, nerves and cells to assess severity. The disadvantages of IVCM include the need for an experienced technician, patient cooperation, unsuitability for diagnosing bacteria and viruses due to axial limitation of 5–7 µm, high costs and the presence of motion artefacts.

5. Artificial Intelligence—Deep Learning Methods

5.1. Background

The applications of artificial intelligence in health care are now a reality due to the advancement of computational power, refinement of learning algorithms and architectures, availability of big data and easy accessibility to deep neural networks by the public ^[44]^[45]^[46]. Deep learning algorithms mostly use multimedia data (images, videos and sounds) and involve the application of large-scale neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN) and recurrent neural networks (RNN) ^[45]. The advantage of deep CNNs is that they enable learning from data without human knowledge and the capability of processing large training data with high dimensionality ^[47]. A CNN model contains multiple convolutional layers, pooling layers and activation units, which are trained using model images by minimising a pre-defined loss function. A convolutional layer applies a number of filters to the input image calculated from the previous layer. This results in enhanced features at certain locations in the image. The weights in these filters are learned during the training process.

The DL CNN model training needs to consider several aspects. For instance, the quality of the training dataset is essential to the performance of the DL CNN model. Image annotation refers to the process of labelling images of a dataset to train the DL models. The image annotation is given by the clinicians and usually includes pixel-level annotation, image-level annotation or both. Further, the model may suffer from ‘over-fitting’; that is, it cannot be generalised well to new test data due to many model parameters and relatively small training examples. A validation dataset is generally used to determine the training termination point to avoid model over-fitting. Drop-out, data augmentation, and transfer learning have been used to improve the generalisability of a trained model. An independent external test set is used to evaluate the trained DL model for assessing the generalisability of the method. A lower performance normally occurs when testing on an independent test set, which is mainly due to model overfitting to the training dataset or a data distribution discrepancy between the training and testing datasets.

In 2016, the use of artificial intelligence (AI) with deep learning (DL) in ophthalmology initially focused on posterior segment diseases such as diabetic retinopathy and age-related macular degeneration, but its application in diseases of the cornea, cataract and anterior chamber structures has surged in the last years ^[44]. Corneal AI research has focused on diseases that require corneal imaging for determining appropriate management and has utilised slit lamp photography, corneal topography and anterior segment optical coherence tomography ^[44]. For infectious keratitis, the use of DL with CNNs has been shown to be a potentially more accessible diagnostic method via image recognition ^[4]^[48]^[49].

5.2. Deep Learning Models in Infectious Keratitis

=Li et al. developed a DL system to classify corneal images in keratitis, other corneal abnormalities and normal cornea. The authors used three DL algorithms to train an internal image dataset and had three external and smartphone datasets to externally evaluate the DL system. In terms of the smartphone dataset, the DenseNet121 algorithm elicited the best performance in classifying keratitis, other corneal abnormalities and normal cornea with an AUROC of 0.967 (95% CI, 0.955–0.977), a sensitivity of 91.9% (95% CI, 89.4–94.4) and a specificity of 96.9% (95% CI, 95.6–98.2) in detecting keratitis ^[50]. To differentiate the types of infectious keratitis, Redd et al. used images from handheld cameras to train five CNNs to differentiate FK from BK and compare their performance against human experience. The best-performing CNN was MobileNet, with an AUROC of 0.86. The CNNs group achieved a statistically significant higher AUROC (0.84) than the experts (0.76, p < 0.01). CNNs elicited higher accuracy for FK (81%) versus BK (75%) compared to the experts who showed more accuracy for BK (88%) versus FK (56%) ^[49].

Hu et al. proposed a DL system with slit lamp images to automatically screen and diagnose IK (BK, FK and viral keratitis (VK)). Six CNNs were trained. 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 ^[51].

Koyama et al. developed a hybrid DL algorithm to determine the causal organism of IK by analysing slit lamp images. Facial recognition techniques were also used as they accommodate different angles, different levels of lighting and different degrees of resolution. ResNet-50 and InceptionResNetV2 were used. The final model was built based on InceptionResNetV2 using 4306 images consisting of 3994 clinical and 312 web images. This algorithm had a high overall accuracy of diagnosis: accuracy/AUROC 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 ^[52].

5.3. Future Perspectives

Up to now, the majority of studies investigating the use of AI in ophthalmology have focused on disease screening and diagnosis using existing clinical data and images based on machine learning and CNNs in conditions such as AMD, diabetic retinopathy, glaucoma and cataract ^[53]. For infectious keratitis diagnosis, the generation of synthetic data using generative adversarial networks (GAN) may be a new method to train AI models without the need for thousands of images from real cases used in CNNs. In the case of less common conditions like fungal or Acanthamoeba keratitis, a GAN could be utilised as a low-shot learning method via data augmentation, meaning that conventional DL models could learn less common conditions using a low number of images ^[54]^[55]. The low-shot learning technique has been used in detecting and classifying retinal diseases ^[56]^[57] and in conjunctival melanoma ^[54].

Another AI technology that generates synthetic data is natural language processing (NLP) models, such as ChatGPT, developed by OpenAI (San Francisco, CA, USA) ^[53]^[58]. ChatGPT utilises DL methods to generate logical text based on the user’s ‘prompt’ in layman’s terms ^[59]. ChatGPT was not conceived for specific tasks, such as reading images or assessing medical notes; however, OpenAI has investigated the potential use of ChatGPT in healthcare and medical applications and research. Some applications include medical note-taking and medical consultations. The medical knowledge embedded in ChatGPT may be utilised in tasks such as medical consultation, diagnosis, and education with variable accuracy ^[60]. For example, Delsoz et al. entered corneal medical cases (including infectious keratitis) on ChatGPT 4.0 and 3.5 to obtain a medical diagnosis, which was compared with the results from three corneal specialists. The provisional diagnosis accuracy was 85% (17 of 20 cases) for ChatGPT-4.0 and 65% for ChatGPT-3.5 versus 100% (specialist 1) and 90% (specialists 2 and 3, each) ^[58]. As a result, ChatGPT may be utilised to analyse clinical data along with DL models (CNNs or GAN) to diagnose and differentiate infectious keratitis.

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