The clinical diagnosis of microbial keratitis

Treatment Strategies for Filamentous Fungi Keratitis

INTRODUCTION

This research aims to describe clinical findings, epidemiology and treatment outcomes in patients with filamentous fungi keratitis of a tertiary centre in Germany over a 7-year period and to compare the efficacy of different antifungal treatments and the effect of additive topical steroids.

METHODS

This retrospective study included 25 eyes of 23 patients from October 2013 to December 2020 with cultural isolates of filamentous fungi and corresponding keratitis. Best-corrected visual acuity (BCVA), clinical signs, symptoms, risk factors and outcome were extracted from medical records.

RESULTS

Improvement of BVCA was noted in 68% of eyes. Mean BCVA of the study population increased from 0.75 logMAR [median 0.40, standard deviation (SD) 0.82, range 0-2.3] to 0.48 logMAR (median 0.10, SD 0.88, range - 0.1 to 3). The most commonly used antifungal topical treatment was a combination of natamycin 5% and voriconazole 2% (44% of eyes), followed by voriconazole 2% in 36% of cases. An antiinflammatory topical steroid was applied in 52%. In 16% of the eyes, penetrating keratoplasty (pKP) was performed.

CONCLUSION

Diagnosis of filamentous fungi keratitis is often difficult or delayed. Outcomes can be poor even with intensive treatment because of high resistance to common antifungals. Access to natamycin 5% seems to lead to favourable outcomes in filamentous fungi keratitis.

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.

Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study

The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.

Locality is the strongest predictor of expert performance in image-based differentiation of bacterial and fungal corneal ulcers from India

PURPOSE

This study sought to identify the sources of differential performance and misclassification error among local (Indian) and external (non-Indian) corneal specialists in identifying bacterial and fungal keratitis based on corneal photography.

METHODS

This study is a secondary analysis of survey data assessing the ability of corneal specialists to identify acute bacterial versus fungal keratitis by using corneal photography. One-hundred images of 100 eyes from 100 patients with acute bacterial or fungal keratitis in South India were previously presented to an international cohort of cornea specialists for interpretation over the span of April to July 2021. Each expert provided a predicted probability that the ulcer was either bacterial or fungal. Using these data, we performed multivariable linear regression to identify factors predictive of expert performance, accounting for primary practice location and surrogate measures to infer local fungal ulcer prevalence, including locality, latitude, and dew point. In addition, Brier score decomposition was used to determine experts' reliability ("calibration") and resolution ("boldness") and were compared between local (Indian) and external (non-Indian) experts.

RESULTS

Sixty-six experts from 16 countries participated. Indian practice location was the only independently significant predictor of performance in multivariable linear regression. Resolution among Indian experts was significantly better (0.08) than among non-Indian experts (0.01; P < 0.001), indicating greater confidence in their predictions. There was no significant difference in reliability between the two groups ( P = 0.40).

CONCLUSION

Local cornea experts outperformed their international counterparts independent of regional variability in tropical risk factors for fungal keratitis. This may be explained by regional characteristics of infectious ulcers with which local corneal specialists are familiar.

Antimicrobial resistance in ocular infection: A review

Antimicrobial resistance (AMR) is a global public health threat with significant impact on treatment outcomes. The World Health Organization's Global Action Plan on AMR recommended strengthening the evidence base through surveillance programs and research. Comprehensive, timely data on AMR for organisms isolated from ocular infections are needed to guide treatment decisions and inform researchers and microbiologists of emerging trends. This article aims to provide an update on the development of AMR in ocular organisms, AMR in bacterial ocular infections and on AMR stewardship programs globally. The most common ocular pathogens are Pseudomonas aeruginosa, Staphylococcus spp., Streptococcus pneumoniae, and Haemophilus influenzae in ocular infections. A variety of studies and a few surveillance programs worldwide have reported on AMR in these infections over time. Fluoroquinolone resistance has increased particularly in Asia and North America. For conjunctivitis, the ARMOR cumulative study in the USA reported a slight decrease in resistance to ciprofloxacin. For keratitis, resistance to methicillin has remained stable for S. aureus and CoNS, while resistance to ciprofloxacin has decreased for MRSA globally. Methicillin-resistance and multidrug resistance are also emerging, requiring ongoing monitoring. Antimicrobial stewardship (AMS) programmes have a critical role in reducing the threat of AMR and improving treatment outcomes. To be successful AMS must be informed by up-to-date AMR surveillance data. As a profession it is timely for ophthalmology to act to prevent AMR leading to greater visual loss through supporting surveillance programmes and establishing AMS.

Corneal Specialists' Confidence in Identifying Causal Organisms of Microbial Keratitis

PURPOSE

Microbial keratitis (MK) is a potentially blinding corneal disease caused by an array of microbial etiologies. However, the lack of early organism identification is a barrier to optimal care. We investigated clinician confidence in their diagnosis of organism type on initial presentation and the relationship between confidence and presenting features.

METHODS

This research presents secondary data analysis of 72 patients from the Automated Quantitative Ulcer Analysis (AQUA) study. Cornea specialists reported their confidence in organism identification. Presenting sample characteristics were recorded including patient demographics, health history, infection morphology, symptoms, and circumstances of infection. The association between confidence and presenting characteristics was investigated with 2-sample t-tests, Wilcoxon tests, and Chi-square or Fisher's exact tests.

RESULTS

Clinicians reported being "confident or very confident" in their diagnosis of the causal organism in MK infections for 39 patients (54%) and "not confident" for 33 patients (46%). Confidence was not significantly associated with patient demographics, morphologic features, or symptoms related to MK. MK cases where clinicians reported they were confident, versus not confident in their diagnosis, showed significantly smaller percentages of previous corneal disease (0% versus 15%, p = 0.017), were not seen by an outside provider first (69% versus 94%, p = 0.015), or had no prior labs drawn (8% versus 33%, p = 0.046), and a significantly larger percentage of cases wore contact lenses (54% versus 28%, p = 0.029).

CONCLUSION

In almost half of MK cases, cornea specialists reported lack of confidence in identifying the infection type. Confidence was related to ocular history and circumstances of infection but not by observable signs and symptoms or patient demographics. Tools are needed to assist clinicians with early diagnosis of MK infection type to expedite care and healing.

Antifungal susceptibility profiles for fungal isolates from corneas and contact lenses in the United Kingdom

OBJECTIVE

To report the identification and results of susceptibility testing for fungal isolates from the cornea or contact lens care systems.

MATERIALS AND METHODS

In this retrospective epidemiological study, we searched the results of fungal cultures from cornea or contact lens systems referred for identification and susceptibility testing to the United Kingdom National Mycology Reference Laboratory between October 2016 and March 2022. For each fungal isolate, we recorded the genus and species of the fungus and the minimum inhibitory concentration (MIC) to six antifungal agents available to treat corneal infection (amphotericin, econazole, itraconazole, natamycin, posaconazole, and voriconazole).

RESULTS

There were 600 isolates from 585 patients, comprising 374 (62%) from corneal samples and 226 from contact lenses and care systems, of which 414 (69%) isolates were moulds (filamentous fungi) and 186 (31%) were yeasts. The most frequent moulds isolated were Fusarium spp (234 isolates, 39%) and Aspergillus spp (62, 10%). The most frequent yeasts isolated were Candida spp (112, 19%), predominantly Candida parapsilosis (65, 11%) and Candida albicans (33, 6%), with 35 isolates (6%) of Meyerozyma guilliermondii. In vitro susceptibility was greatest for natamycin (347 moulds tested, mode 4 mg/L, range 0.25-64 mg/L; 98 yeasts tested, mode 4 mg/L, range 0.5-32 mg/L), with susceptibility for 94% for moulds and 99% yeasts. Of the 16 isolates interpreted as highly resistant to natamycin (MIC ≥16 mg/L), 13 were Aspergillus flavus complex.

CONCLUSIONS

In vitro susceptibility supports the use of natamycin for the empiric treatment of fungal keratitis in the UK.

Fungal Keratitis: Diagnosis, Management, and Recent Advances

Fungal keratitis is one of the major causes of microbial keratitis that may lead to corneal blindness. Many problems related to diagnosis and therapy are encountered in fungal keratitis, including difficulty in obtaining laboratory diagnoses and the availability and efficacy of antifungal medications. Intensive and prolonged use of antifungal topical preparations may not be enough. The use of antifungal medications is considered the main treatment for fungal keratitis. It is recommended to start antifungal therapy after confirmation of the clinical diagnosis with a smear or positive cultures. Topical application of antifungal medications is a mainstay for the treatment of fungal keratitis; however, systemic, intra-stromal, or intra-cameral routes may be used. Therapeutic keratoplasty is the main surgical procedure approved for the management of fungal keratitis with good success rate. Intrastromal corneal injection of antifungal medications may result in steady-state drug levels within the corneal tissue and prevent intervals of decreased antifungal drug concentration below its therapeutic level. In cases of severe fungal keratitis with deep stromal infiltration not responding to treatment, intracameral injection of antifungal agents may be effective. Collagen cross-linking has been proposed to be beneficial for cases of fungal keratitis as a stand-alone therapy or as an adjunct to antifungal medications. Although collagen cross-linking has been extensively studied in the past few years, its protocol still needs many modifications to optimize UV fluence levels, irradiation time, and concentration of riboflavin to achieve 100% microbial killing.

From Clinical Suspicion to Diagnosis: A Review of Diagnostic Approaches and Challenges in Fungal Keratitis

Fungal keratitis is a relatively rare yet severe ocular infection that can lead to profound vision impairment and even permanent vision loss. Rapid and accurate diagnosis plays a crucial role in the effective management of the disease. A patient's history establishes the initial clinical suspicion since it can provide valuable clues to potential predisposing factors and sources of fungal exposure. Regarding the evaluation of the observed symptoms, they are not exclusive to fungal keratitis, but their timeline can aid in distinguishing fungal keratitis from other conditions. Thorough clinical examination of the affected eye with a slit-lamp microscope guides diagnosis because some clinical features are valuable predictors of fungal keratitis. Definitive diagnosis is established through appropriate microbiological investigations. Direct microscopic examination of corneal scrapings or biopsy specimens can assist in the presumptive diagnosis of fungal keratitis, but culture remains the gold standard for diagnosing fungal keratitis. Advanced molecular techniques such as PCR and MALDI-ToF MS are explored for their rapid and sensitive diagnostic capabilities. Non-invasive techniques like in vivo confocal microscopy (IVCM) and optical coherence tomography (OCT) are useful for real-time imaging. Every diagnostic technique has both advantages and drawbacks. Also, the selection of a diagnostic approach can depend on various factors, including the specific clinical context, the availability of resources, and the proficiency of healthcare personnel.

Screening Candida auris through a multiplex stepwise PCR algorithm directly from clinical samples of patients suspected of otomycosis in south of Iran; Detection of five cases

BACKGROUND

Otomycosis is an infection of the external auditory canal caused by molds and yeasts with descending frequency. Laboratory diagnosis is usually confirmed by microscopy and culture. However, they are not specific enough to reliably differentiate the causative agents, especially for rare pathogens such as Candida auris. The purpose of the current study was to the molecular screening of C. auris species from direct clinical samples of patients with suspected otomycosis in Southern of Iran.

MATERIALS AND METHODS

A total of 221 ear aspirates collected from 221 patients with suspected otomycosis over a four-year period. All the ear aspirations were examined with pan-fungal primers, then those with a positive result was included in two separate reaction mixtures simultaneously to identify the most clinically relevant Aspergillus and Candida species. The validity of positive samples for C. auris was assessed by sequencing.

RESULTS

Of the 189 pan-fungal positive PCRs, 78 and 39 specimens contained Aspergillus spp. and Candida spp., respectively. Furthermore, 65 specimens showed simultaneous positive bands in both Candida and Aspergillus species-specific multiplex PCR including five samples/patients with positive result for C. auris (5/189; 2.6%). Four out of five cases with C. auris species-specific PCR were reconfirmed by sequencing, while none were positive for C. auris in culture.

CONCLUSION

Unfortunately, due to high treatment failure rates of antifungal classes against C. auris species, rapid and accurate identification of patients colonised with C. auris is critical to overcome the challenge of preventing transmission. This PCR assay can be successfully applied for rapid and accurate detection of C. auris directly in patient samples and is able to differentiate C. auris from closely related Candida species.

Corneal ring infiltrate- far more than Acanthamoeba keratitis: review of pathophysiology, morphology, differential diagnosis and management

OBJECTIVE

Ring infiltrates usually accompany numerous infectious and sterile ocular disorders. Nevertheless, systemic conditions, drugs toxicity and contact lens wear may present with corneal ring infiltrate in substantial part. Considering its detrimental effect on vision, detailed knowledge on etiology, pathophysiology, differential diagnosis, and management should be considered essential for every ophthalmologist.

METHODS

The PUBMED database was searched for "corneal ring infiltrate" and "ring infiltrate" phrases, "sterile corneal infiltrate" and "corneal infiltrate". We analyzed articles written in English on risk factors, pathophysiology, clinical manifestation, morphological features, ancillary tests (anterior-segment optical coherence tomography, corneal scraping, in vivo confocal microscopy), differential diagnosis and management of corneal ring infiltrate.

RESULTS

Available literature depicts multifactorial origin of corneal ring infiltrate. Dual immunological pathophysiology, involving both antibodies-dependent and -independent complement activation, is underlined. Furthermore, we found that the worldwide most prevalent among non-infectious and infectious ring infiltrates are ring infiltrates related to contact-lens wear and bacterial keratitis respectively. Despite low incidence of Acanthamoeba keratitis, it manifests with corneal ring infiltrate with the highest proportion of the affected patients (one third). However, similar ring infiltrate might appear as a first sign of general diseases manifestation and require targeted treatment. Every corneal ring infiltrate with compromised epithelium should be scraped and treat as an infectious infiltrate until not proven otherwise. Of note, microbiological ulcer might also lead to immunological ring and therefore require anti-inflammatory treatment.

CONCLUSION

Corneal ring infiltrate might be triggered not only by ocular infectious and non-infectious factors, but also by systemic conditions. Clinical assessment is crucial for empirical diagnosis. Furthermore, treatment is targeted towards the underlying condition but should begin with anti-infectious regimen until not proven otherwise.

From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study

Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the worst prognosis. Fungal cases can be subdivided into filamentous and yeasts, which shows fundamental differences. Delays in diagnosis or initiation of treatment increase the risk of ocular complications. Currently, the diagnosis of IK is mainly based on slit-lamp examination and corneal scrapings. Notably, these diagnostic methods have their drawbacks, including experience-dependency, tissue damage, and time consumption. Artificial intelligence (AI) is designed to mimic and enhance human decision-making. An increasing number of studies have utilized AI in the diagnosis of IK. In this paper, we propose to use AI to diagnose IK (model 1), differentiate between bacterial keratitis and fungal keratitis (model 2), and discriminate the filamentous type from the yeast type of fungal cases (model 3). Overall, 9329 slit-lamp photographs gathered from 977 patients were enrolled in the study. The models exhibited remarkable accuracy, with model 1 achieving 99.3%, model 2 at 84%, and model 3 reaching 77.5%. In conclusion, our study offers valuable support in the early identification of potential fungal and bacterial keratitis cases and helps enable timely management.

Beauveria bassiana Keratitis: A Case Series and Review of Literature

BACKGROUND

Beauveria bassiana is a filamentous fungus commonly used as an insecticide that rarely causes keratitis.

METHODS

Patients affected by Beauveria bassiana keratitis were retrospectively recruited at San Raffaele Hospital (Milan, Italy) between 2020 and 2022. All subjects underwent comprehensive ophthalmic evaluation, including in vivo confocal microscopy (IVCM) and microbiologic examination of corneal scrapings. Beauveria bassiana was identified using 18S rDNA targeted PCR.

RESULTS

Four eyes of four patients (51 ± 8.8 years old) were evaluated. The main risk factors were soft contact lens wear (75%) and trauma with vegetative matter (50%). A superficial infiltrate was displayed in the majority of patients. Three cases (75%) showed hyphae on IVCM. All patients showed clinical improvement after topical antifungal therapy, although mostly through a combination of two antifungals (75%). One patient with a deeper infection required a systemic antifungal agent after one month of topical therapy. All cases required debridement to reduce the microbial load and enhance drug penetration. All patients experienced keratitis resolution following medical treatment (average: 3.3 months).

CONCLUSIONS

The identification of risk factors and the early diagnosis of Beauveria bassiana keratitis are fundamental in order to avoid its penetration in the deeper corneal stromal layers. Topical antifungal drugs, possibly accompanied by ulcer debridement, may be a successful treatment if instilled from the early phases of the disease.

Antimicrobial Drug-Drug Interactions in the Treatment of Infectious Keratitis

PURPOSE

Infectious keratitis is a serious disease requiring immediate, intensive, and broad-spectrum empiric treatment to prevent vision loss. Given the diversity of organisms that can cause serious corneal disease, current guidelines recommend treatment with several antimicrobial agents simultaneously to provide adequate coverage while awaiting results of microbiology cultures. However, it is currently unknown how the use of multiple ophthalmic antimicrobial agents in combination may affect the efficacy of individual drugs.

METHODS

Using a panel of 9 ophthalmic antibiotics, 3 antifungal agents, and 2 antiacanthamoeba therapeutics, fractional inhibitory concentration testing in the standard checkerboard format was used to study 36 antibiotic-antibiotic combinations, 27 antibiotic-antifungal combinations, and 18 antibiotic-antiacanthamoeba combinations against both Staphylococcus aureus and Pseudomonas aeruginosa for synergistic, additive, neutral, or antagonistic drug-drug interactions.

RESULTS

We demonstrate that while most combinations resulted in no change in antimicrobial efficacy of individual components, the combination of erythromycin + polyhexamethylene biguanide was found to be antagonistic toward P. aeruginosa . Conversely, 18 combinations toward S. aureus and 15 combinations toward P. aeruginosa resulted in additive or synergistic activity, including 4 with improved activity toward both species.

CONCLUSIONS

Understanding how drug-drug interactions may affect drug efficacy is critical to selecting the appropriate combination therapy and improving clinical outcomes of this blinding disease.

Quantifying Clinicians' Diagnostic Uncertainty When Making Initial Treatment Decisions for Microbial Keratitis

PURPOSE

There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling.

METHODS

A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm 2 central, <2 mm 2 peripheral, and >8 mm 2 central).

RESULTS

Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections ( P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States ( P < 0.001) but not international clinicians.

CONCLUSIONS

Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.

Diagnostic Potential of Anterior Segment Optical Coherence Tomography Scans for Pseudomonas Keratitis

Purpose

The purpose of this study was to investigate the diagnostic value of anterior segment optical coherence tomography (AS-OCT) scans for Pseudomonas keratitis.

Methods

Patients with treatment-naïve keratitis underwent AS-OCT imaging. The following parameters were evaluated: corneal thickness (CT), infiltrate thickness (IT), infiltrate diameter (ID), tissue loss/gain, entropy, and distance of the lesion from the corneal pupillary center. Three different OCT devices were used for the analysis. The relationship between the detected pathogen and the OCT patterns was analyzed.

Results

Nineteen eyes of 19 patients were included in the analysis: seven cases in the Pseudomonas group and 12 cases in the Gram-positive group. The mean (SD) values for the Pseudomonas and Gram-positive groups, respectively, were as follows: CT, 834 µm (165 µm) and 760 µm (120 µm); IT, 290 µm (152 µm) and 287 µm (84 µm); ID, 2067 µm (1470 µm) and 1307 µm (745 µm); distance to center, 3.0 mm (1.2 mm) and 3.0 mm (1.6 mm); epithelial defect, 1193 µm (586 µm) and 484 µm (615 µm); tissue gain, +31% (19%) and +10% (12%); and entropy level, 4.0 (0.8) and 3.9 (1.1).

Conclusions

This study introduces novel insights by identifying specific OCT parameters that distinguish Pseudomonas keratitis, including a 30% tissue gain. These findings align with earlier research that underscores the potential of OCT in differentiating various pathogens causing keratitis.

Translational Relevance

The findings of this study could be used to develop new diagnostic strategies for Pseudomonas keratitis. The OCT findings could be used to develop new biomarkers for the infection.

Diagnostic accuracy of confocal scan in detecting acanthamoeba keratitis and fungal keratitis: a systematic review and meta-analysis

PURPOSE

Acanthamoeba keratitis (AK) and fungal keratitis (FK) are two microbial keratitis that cause serious damage and, without early accurate diagnosis and treatment, may lead to blindness. In vivo corneal confocal scan, as an emerging ocular diagnostic method in comparison with microbiological smears and cultures as the gold standard, may assist in accelerating appropriate diagnosis.

OBJECTIVE

To determine the diagnostic accuracy of confocal scan for the diagnosis of AK and FK.

METHODS

Data were collected via a comprehensive literature search of PubMed, Web of Science, Cochrane Library, Embase and Scopus using keywords related to diagnostic accuracy of confocal scan in AK and FK up to October 2022. Pooled data underwent meta-analysis in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall diagnostic odds ratio (DOR) of confocal scan for the diagnosis of AK and FK.

RESULTS

The final 14 relevant studies were identified, including 1950 eyes. Meta-analysis in AK group revealed 94% sensitivity, 87% specificity, 89% PPV, 92% NPV, and DOR of 143.32, and in FK group disclosed 88% sensitivity, 85% specificity, 85% PPV, 88% NPV, and DOR of 75.98.

CONCLUSION

The accuracy of confocal scan for the diagnosis of AK was significantly more than that for detecting FK; despite the limitations such as limited numbers of available retrospective studies for the detection of FK, confocal scan had an acceptable performance in detecting FK eyes. The overall performance of NCS was similar with that of HRT-RCM for the detection of both types of keratitis.

Urgent unmet needs in the care of bacterial keratitis: An evidence-based synthesis

Bacterial corneal infections, or bacterial keratitis (BK), are ophthalmic emergencies that frequently lead to irreversible visual impairment. Though increasingly recognized as a major cause of global blindness, modern paradigms of evidence-based care in BK have remained at a diagnostic and therapeutic impasse for over half a century. Current standards of management - based on the collection of corneal cultures and the application of broad-spectrum topical antibiotics - are beset by important yet widely underrecognized limitations, including approximately 30% of all patients who will develop moderate to severe vision loss in the affected eye. Though recent advances have involved a more clearly defined role for adjunctive topical corticosteroids, and novel therapies such as corneal crosslinking, overall progress to improve patient and population-based outcomes remains incommensurate to the chronic morbidity caused by this disease. Recognizing that the care of BK is guided by the clinical axiom, "time equals vision", this chapter offers an evidence-based synthesis for the clinical management of these infections, underscoring critical unmet needs in disease prevention, diagnosis, and treatment.

Modern Technologies in Diagnosis of Fungal Keratitis (Review)

Traumas and infectious diseases of the eye play a leading role in the development of corneal blindness responsible for 1.5-2 million cases of vision loss per year. To date, the issue of reducing the incidence of fungal keratitis is acute and needs to be solved worldwide. Trauma as a risk factor for corneal fungal disease is thought to be prevalent in developing countries due to agricultural involvement, while in developed countries the onset of the disease is predisposed by medical advances such as contact vision correction and modern ophthalmic surgery. Thorough analysis of the pathogenesis gives the possibility to describe the action of fungal enzymes, biofilm formation, and the resistance mechanism, which on the one hand explains the aggressive course of the disease and difficulties in its diagnosis, and on the other hand, it encourages searching for new methods of diagnosis and treatment. The non-specific clinical picture of fungal keratitis, the variety and availability of antibiotics nowadays become an obstacle for rapid detection of this pathology. Low public awareness and late visit to an ophthalmologist are also a barrier to successful combating the increasing incidence of fungal keratitis. Belated diagnosis, increasing resistance of fungi to antibiotics, and lack of registered antifungal ophthalmic drugs justify poor treatment efficacy resulting in decreased visual acuity or vision loss. Existing diagnostic methods need systematization and detailed comparison, identifying the advantages and disadvantages of each. This review considers causative agents and their influence on pathogenesis of the disease, describes difficulties of fungal keratitis diagnosis and possible ways of overcoming these problems using new developments, and also outlines further prospects of research in this direction.

Clinical differentiation of keratitis due to fungus and Pythium: A photographic survey

Purpose

To evaluate the knowledge and skills of clinicians in differentiating cases of Pythium from fungal keratitis based on their clinical appearance.

Methods

Thirty slit-lamp photographs from culture-proven cases of Pythium and fungal keratitis were selected for conducting a prospective online-based photographic survey. The participants were asked to identify the causative organism and enlist the specific clinical features which helped in reaching the diagnosis. Both cornea specialists and clinical fellows/trainees participated in the survey. Sensitivity, specificity, and predictive values were estimated, and the factors associated with these, including the identification rates of clinical signs, were evaluated using bivariate and multivariate linear regression analyses. The identification rates between the consultants and fellows were compared.

Results

A total of 42 cornea specialists (28 consultants and 14 clinical fellows) participated in the survey. Clinicians were able to differentiate Pythium from fungal etiology in only 56% of cases. The differentiating ability between the consultants and fellows was statistically insignificant. Average sensitivity, specificity, and positive and negative predictive values for differentiating Pythium from fungus were 56%, 65%, 56%, and 66%, respectively. The specific clinical features enlisted by the participants to differentiate Pythium from fungus were the presence of tentacles, peripheral guttering, dot-like infiltrates, and elevated plaques.

Conclusion

Clinical knowledge regarding the signs and symptoms and the microbiological identification of features for Pythium are still lacking among clinicians. As the treatment protocols are distinctly different for both, it is imperative to enhance the knowledge and diagnosing ability to tackle this emerging microorganism which causes high morbidity.

Utility of investigation for suspected microbial keratitis: a diagnostic accuracy study

PURPOSE

The true disease status of a population with suspected microbial keratitis (MK) cannot be verified. There is not an accurate (gold) reference standard to confirm infection and inter-test comparisons of sensitivity and specificity therefore lead to bias with questionable estimates of test utility. We present an alternative method to report results.

METHODS

We used a decision to treat as the definition for MK. We retrospectively compared the results of corneal culture and polymerase chain reaction (PCR) as these are objective tests available for the three principal groups of pathogens. We then estimated the potential contribution of positive results, either alone or in combination, to support the working diagnosis.

RESULTS

We included 2021 (77.4%) eyes with suspected bacterial keratitis, 365 (14.0%) with suspected acanthamoeba keratitis, and 226 (8.6%) with suspected fungal keratitis, all treated between July 2013 and December 2019. In these groups, there were 51.6% positive culture and 6.5% positive PCR results for bacteria, 19.0% and 40.5% for acanthamoeba, and 28.3% and 15.0% for fungi. Between groups the differences in the proportions of positive results from culture and PCR was statistically significant (P < 0.001). The added benefit of PCR to the result of culture in identifying a potential pathogen was 1.4% for bacteria (P = 0.6292), 24.4% for acanthamoeba (P = 0.0001), and 5.8% for fungi (P = 0.3853).

CONCLUSIONS

For suspected MK a comparison of the test positivity rate is an easily comprehensible outcome measure of test utility.

Fungal keratitis in Iran: Risk factors, clinical features, and mycological profile

Introduction

This study was intended to investigate the clinical features and predisposing factors of fungal keratitis (FK), as well as molecular identification and antifungal susceptibility of causative agents in Tehran, Iran.

Methods

This cross-sectional study was carried out from April 2019 to May 2021. All fungi isolates were identified using conventional methods and were confirmed by DNA-PCR-based molecular assays. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) was used to identify yeast species. Minimum inhibitory concentrations (MIC) of eight antifungal agents were assessed according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) microbroth dilution reference method.

Results

Fungal etiology was confirmed in 86 (7.23%) of 1189 corneal ulcers. A significant predisposing factor for FK was ocular trauma caused by plant materials. Therapeutic penetrating keratoplasty (PKP) was required in 60.4% of cases. The predominant fungal species isolated was Fusarium spp. (39.5%) followed by Aspergillus spp. (32.5%) and Candida spp. (16.2%).

Discussion

The MIC results indicate that amphotericin B may be appropriate for treating FK caused by Fusarium species. FK caused by Candida spp. can be treated with flucytosine, voriconazole, posaconazole, miconazole, and caspofungin. In developing countries such as Iran, corneal infection due to filamentous fungi is a common cause of corneal damage. In this region, fungal keratitis is observed primarily within the context of agricultural activity and subsequent ocular trauma. Fungal keratitis can be managed better with understanding the "local" etiologies and antifungal susceptibility patterns.

Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis

This investigation aimed to explore deep learning (DL) models' potential for diagnosing Pseudomonas keratitis using external eye images. In the retrospective research, the images of bacterial keratitis (BK, n = 929), classified as Pseudomonas (n = 618) and non-Pseudomonas (n = 311) keratitis, were collected. Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. Five-fold cross-validation was used to determine the ability of single and ensemble models to diagnose Pseudomonas keratitis. The EfficientNet B2 model had the highest accuracy (71.2%) of the eight single-DL models, while the best ensemble 4-DL model showed the highest accuracy (72.1%) among the ensemble models. However, no statistical difference was shown in the area under the receiver operating characteristic curve and diagnostic accuracy among these single-DL models and among the four best ensemble models. As a proof of concept, the DL approach, via external eye photos, could assist in identifying Pseudomonas keratitis from BK patients. All the best ensemble models can enhance the performance of constituent DL models in diagnosing Pseudomonas keratitis, but the enhancement effect appears to be limited.

Deep learning-based classification of infectious keratitis on slit-lamp images

BACKGROUND

Infectious keratitis (IK) is an ocular emergency caused by a variety of microorganisms, including bacteria, fungi, viruses, and parasites. Culture-based methods were the gold standard for diagnosing IK, but difficult biopsy, delaying report, and low positive rate limited their clinical application.

OBJECTIVES

This study aims to construct a deep-learning-based auxiliary diagnostic model for early IK diagnosis.

DESIGN

A retrospective study.

METHODS

IK patients with pathological diagnosis were enrolled and their slit-lamp photos were collected. Image augmentation, normalization, and histogram equalization were applied, and five image classification networks were implemented and compared. Model blending technique was used to combine the advantages of single model. The performance of combined model was validated by 10-fold cross-validation, receiver operating characteristic curves (ROC), confusion matrix, Gradient-wright class activation mapping (Grad-CAM) visualization, and t-distributed Stochastic Neighbor Embedding (t-SNE). Three experienced cornea specialists were invited and competed with the combined model on making clinical decisions.

RESULTS

Overall, 4830 slit-lamp images were collected from patients diagnosed with IK between June 2010 and May 2021, including 1490 (30.8%) bacterial keratitis (BK), 1670 (34.6%) fungal keratitis (FK), 600 (12.4%) herpes simplex keratitis (HSK), and 1070 (22.2%) Acanthamoeba keratitis (AK). KeratitisNet, the combination of ResNext101_32x16d and DenseNet169, reached the highest accuracy 77.08%. The accuracy of KeratitisNet for diagnosing BK, FK, AK, and HSK was 70.27%, 77.71%, 83.81%, and 79.31%, and AUC was 0.86, 0.91, 0.96, and 0.98, respectively. KeratitisNet was mainly confused in distinguishing BK and FK. There were 20% of BK cases mispredicted into FK and 16% of FK cases mispredicted into BK. In diagnosing each type of IK, the accuracy of model was significantly higher than that of human ophthalmologists (p < 0.001).

CONCLUSION

KeratitisNet demonstrates a good performance on clinical IK diagnosis and classification. Deep learning could provide an auxiliary diagnostic method to help clinicians suspect IK using different corneal manifestations.

Clinical differentiation of Pythium keratitis from fungal keratitis and development of a scoring system

Purpose

To differentiate Pythium keratitis from fungal keratitis using clinical signs, to explore usefulness of various signs as diagnostic prognosticators, and develop a clinical scoring system.

Methods

A retrospective review of medical records and archived clinical photographs of patients with culture-positive Pythium keratitis and hyaline filamentous fungal keratitis was conducted at a tertiary eye institute to explore characteristics of ulcers that may aid diagnosis.

Results

Full-thickness corneal stromal keratitis (P = 0.055), a dry ulcer surface (P = 0.010), tentacles (P < 0.0001), intrastromal dots (P < 0.0001), ring infiltrates (P = 0.024), reticular patterns (P < 0.0001), and peripheral furrows (P < 0.0001) were clinical signs associated with Pythium keratitis. Multiple regression analysis identified tentacles (odds ratio: 24.1, 95% confidence interval (CI): 3.8-158.1, P = 0.001) and peripheral furrows (odds ratio: 60.6, 95% CI: 5.1-712.3, P = 0.001) as independent diagnostic prognosticators for Pythium keratitis. The positive and negative likelihood ratios of a dry ulcer surface, tentacles, intrastromal dots, ring infiltrates, reticular patterns, and peripheral furrows predicting Pythium keratitis were 1.6, 13.6, 17.9, 4.3, 30.7, 15.3 and 0.4, 0.4, 0.7, 0.9, 0.6 and 0.8, respectively. The presence of two or more of these clinical signs (excluding a dry ulcer surface) had a sensitivity of 55.6% and a false positive rate of 1.4%.

Conclusion

Tentacles, intrastromal dots, ring infiltrates, reticular patterns, and peripheral furrows are clinical signs to be considered for the diagnosis of Pythium keratitis and the presence of two or more signs has a very low false positive rate.

Fungal Keratitis: Clinical Features, Risk Factors, Treatment, and Outcomes

Fungal keratitis (FK) can be challenging to diagnose and treat. In this retrospective case series, FK cases presenting at the University of Pittsburgh Medical Center, Pennsylvania, USA, from 2015 to 2021 were reviewed for ocular risk factors, clinical presentation, management, and outcomes. Twenty-eight cases of FK were included. The median presenting age was 58.5 (18.5) years, and the median symptom duration prior to presentation was 10 (35.8) days. Predisposing ocular risk factors included contact lens use (67.9%), recent ocular trauma/abrasion (42.9%), and history of ocular surgery (42.9%). The median presenting visual acuity (VA) was 1.35 (1.72) LogMAR. About half presented with a central ulcer (42.9%), large infiltrate (6.7 (6.3) mm2), corneal thinning (50.0%), and hypopyon (32.1%). The majority of isolated fungal species were filamentous (75.0%). Most common antifungal medications included topical voriconazole (71.4%) and natamycin (53.6%) drops and oral voriconazole (64.3%). Surgical management was necessary in 32.1% of cases and enucleation in one case. Defect resolution occurred in 42.5 (47.0) days, and median final VA was 0.5 (1.84) LogMAR. Features associated with poor final visual outcomes included poor initial VA (p < 0.001) and larger defect size (p = 0.002). In conclusion, unlike prior studies in the northeast region of the USA, FK was commonly caused by filamentous fungi, and antifungal management most often consisted of topical and oral voriconazole.

Diagnosis of Fungal Keratitis in Low-Income Countries: Evaluation of Smear Microscopy, Culture, and In Vivo Confocal Microscopy in Nepal

Clinically diagnosing fungal keratitis (FK) is challenging; diagnosis can be assisted by investigations including in vivo confocal microscopy (IVCM), smear microscopy, and culture. The aim of this study was to estimate the sensitivity in detecting fungal keratitis (FK) using IVCM, smear microscopy, and culture in a setting with a high prevalence of FK. In this cross-sectional study nested within a prospective cohort study, consecutive microbial keratitis (MK) patients attending a tertiary-referral eye hospital in south-eastern Nepal between June 2019 and November 2020 were recruited. IVCM and corneal scrapes for smear microscopy and culture were performed using a standardised protocol. Smear microscopy was performed using potassium hydroxide (KOH), Gram stain, and calcofluor white. The primary outcomes were sensitivities with 95% confidence intervals [95% CI] for IVCM, smear microscopy and culture, and for each different microscopy stain independently, to detect FK compared to a composite referent. We enrolled 642 patients with MK; 468/642 (72.9%) were filamentous FK, 32/642 (5.0%) were bacterial keratitis and 64/642 (10.0%) were mixed bacterial-filamentous FK, with one yeast infection (0.16%). No organism was identified in 77/642 (12.0%). Smear microscopy had the highest sensitivity (90.7% [87.9-93.1%]), followed by IVCM (89.8% [86.9-92.3%]) and culture (75.7% [71.8-79.3%]). Of the three smear microscopy stains, KOH had the highest sensitivity (85.3% [81.9-88.4%]), followed by Gram stain (83.2% [79.7-86.4%]) and calcofluor white (79.1% [75.4-82.5%]). Smear microscopy and IVCM were the most sensitive tools for identifying FK in our cohort. In low-resource settings we recommend clinicians perform corneal scrapes for microscopy using KOH and Gram staining. Culture remains an important tool to diagnose bacterial infection, identify causative fungi and enable antimicrobial susceptibility testing.

Potential new fluoroquinolone treatments for suspected bacterial keratitis

Topical fluoroquinolones (FQs) are an established treatment for suspected microbial keratitis. An increased FQ resistance in some classes of bacterial pathogens is a concern. Some recently developed FQs have an extended spectrum of activity, making them a suitable alternative for topical ophthalmic use. For example, the new generation FQs, avarofloxacin, delafloxacin, finafloxacin, lascufloxacin, nadifloxacin, levonadifloxacin, nemonoxacin and zabofloxacin have good activity against the common ophthalmic pathogens such as Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pneumoniae and several of the Enterobacteriaceae. However, because there are no published ophthalmic break-point concentrations, the susceptibility of an isolated micro-organism to a topical FQ is extrapolated from systemic break-point data and wild type susceptibility. The purpose of this review is to compare the pharmacokinetics and pharmacodynamics of the FQs licensed for topical ophthalmic use with the same parameters for new generation FQs. We performed a literature review of the FQs approved for topical treatment and the new generation FQs licensed to treat systemic infections. We then compared the minimum inhibitory concentrations (MIC) of bacterial isolates and the published concentrations that FQs achieved in the cornea and aqueous. We also considered the potential suitability of new generation FQs for topical use based on their medicinal properties. Notably, we found significant variation in the reported corneal and aqueous FQ concentrations so that reliance on the reported mean concentration may not be appropriate, and the first quartile concentration may be more clinically relevant. The provision of the MIC for the microorganism together with the achieved lower (first) quartile concentration of a FQ in the cornea could inform management decisions such as whether to continue with the prescribed antimicrobial, increase the frequency of application, use a combination of antimicrobials or change treatment.

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks

Purpose

Develop computer vision models for image-based differentiation of bacterial and fungal corneal ulcers and compare their performance against human experts.

Design

Cross-sectional comparison of diagnostic performance.

Participants

Patients with acute, culture-proven bacterial or fungal keratitis from 4 centers in South India.

Methods

Five convolutional neural networks (CNNs) were trained using images from handheld cameras collected from patients with culture-proven corneal ulcers in South India recruited as part of clinical trials conducted between 2006 and 2015. Their performance was evaluated on 2 hold-out test sets (1 single center and 1 multicenter) from South India. Twelve local expert cornea specialists performed remote interpretation of the images in the multicenter test set to enable direct comparison against CNN performance.

Main Outcome Measures

Area under the receiver operating characteristic curve (AUC) individually and for each group collectively (i.e., CNN ensemble and human ensemble).

Results

The best-performing CNN architecture was MobileNet, which attained an AUC of 0.86 on the single-center test set (other CNNs range, 0.68-0.84) and 0.83 on the multicenter test set (other CNNs range, 0.75-0.83). Expert human AUCs on the multicenter test set ranged from 0.42 to 0.79. The CNN ensemble achieved a statistically significantly higher AUC (0.84) than the human ensemble (0.76; P < 0.01). CNNs showed relatively higher accuracy for fungal (81%) versus bacterial (75%) ulcers, whereas humans showed relatively higher accuracy for bacterial (88%) versus fungal (56%) ulcers. An ensemble of the best-performing CNN and best-performing human achieved the highest AUC of 0.87, although this was not statistically significantly higher than the best CNN (0.83; P = 0.17) or best human (0.79; P = 0.09).

Conclusions

Computer vision models achieved superhuman performance in identifying the underlying infectious cause of corneal ulcers compared with cornea specialists. The best-performing model, MobileNet, attained an AUC of 0.83 to 0.86 without any additional clinical or historical information. These findings suggest the potential for future implementation of these models to enable earlier directed antimicrobial therapy in the management of infectious keratitis, which may improve visual outcomes. Additional studies are ongoing to incorporate clinical history and expert opinion into predictive models.

Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis

PURPOSE

Microbial keratitis is an urgent condition in ophthalmology that requires prompt treatment. This study aimed to apply deep learning algorithms for rapidly discriminating between fungal keratitis (FK) and bacterial keratitis (BK).

METHODS

A total of 2167 anterior segment images retrospectively acquired from 194 patients with 128 patients with BK (1388 images, 64.1%) and 66 patients with FK (779 images, 35.9%) were used to develop the model. The images were split into training, validation, and test sets. Three convolutional neural networks consisting of VGG19, ResNet50, and DenseNet121 were trained to classify images. Performance of each model was evaluated using precision (positive predictive value), sensitivity (recall), F1 score (test's accuracy), and area under the precision-recall curve (AUPRC). Ensemble learning was then applied to improve classification performance.

RESULTS

The classification performance in F1 score (95% confident interval) of VGG19, DenseNet121, and RestNet50 was 0.78 (0.72-0.84), 0.71 (0.64-0.78), and 0.68 (0.61-0.75), respectively. VGG19 also demonstrated the highest AUPRC of 0.86 followed by RestNet50 (0.73) and DenseNet (0.60). The ensemble learning could improve performance with the sensitivity and F1 score of 0.77 (0.81-0.83) and 0.83 (0.77-0.89) with an AUPRC of 0.904.

CONCLUSIONS

Convolutional neural network with ensemble learning showed the best performance in discriminating FK from BK compared with single architecture models. Our model can potentially be considered as an adjunctive tool for providing rapid provisional diagnosis in patients with microbial keratitis.

Microbial Keratitis in Nepal: Predicting the Microbial Aetiology from Clinical Features

Fungal corneal infection (keratitis) is a common clinical problem in South Asia. However, it is often challenging to distinguish this from other aetiologies, such as bacteria or acanthamoeba. In this prospective study, we investigated clinical and epidemiological features that can predict the microbial aetiology of microbial keratitis in Nepal. We recruited patients presenting with keratitis to a tertiary eye hospital in lowland eastern Nepal between June 2019 and November 2020. A structured assessment, including demographics, history, and clinical signs, was carried out. The aetiology was investigated with in vivo confocal microscopy and corneal scrape for microscopy and culture. A predictor score was developed using odds ratios calculated to predict aetiology from features. A fungal cause was identified in 482/642 (75.1%) of cases, which increased to 532/642 (82.9%) when including mixed infections. Unusually, dematiaceous fungi accounted for half of the culture-positive cases (50.6%). Serrated infiltrate margins, patent nasolacrimal duct, raised corneal slough, and organic trauma were independently associated with fungal keratitis (p < 0.01). These four features were combined in a predictor score. The probability of fungal keratitis was 30.1% if one feature was present, increasing to 96.3% if all four were present. Whilst microbiological diagnosis is the "gold standard" to determine the aetiology of an infection, certain clinical signs can help direct the clinician to find a presumptive infectious cause, allowing appropriate treatment to be started without delay. Additionally, this study identified dematiaceous fungi, specifically Curvularia spp., as the main causative agent for fungal keratitis in this region. This novel finding warrants further research to understand potential implications and any trends over time.

Expert Performance in Visual Differentiation of Bacterial and Fungal Keratitis

This study quantifies the performance of an international cohort of cornea specialists in image-based differentiation of bacterial and fungal keratitis, identifying significant regional variation and establishing a reference standard for comparison against machine learning models.

Diagnosing Fungal Keratitis and Simultaneously Identifying Fusarium and Aspergillus Keratitis with a Dot Hybridization Array

Fungal keratitis (FK) is one of the most common microbial keratitis, which often leads to poor prognosis as a result of delayed diagnosis. Several studies implied that early differentiation of the two major FK, Fusarium and Aspergillus keratitis, could be helpful in selecting effective anti-fungal regimens. Therefore, a novel dot hybridization array (DHA) was developed to diagnose FK and differentiate Fusarium and Aspergillus keratitis in this study. One hundred forty-six corneal scrapes obtained from one hundred forty-six subjects impressed with clinically suspected FK were used to evaluate the performance of the DHA. Among these patients, 107 (73.3%) patients had actual FK confirmed by culture and DNA sequencing. We found that the DHA had 93.5% sensitivity and 97.4% specificity in diagnosing FK. In addition, this array had 93.2% sensitivity and 93.8% specificity in diagnosing Fusarium keratitis, as well as 83.3% sensitivity and 100% specificity in diagnosing Aspergillus keratitis. Furthermore, it had 83.9% sensitivity and 100% specificity in identifying Fusarium solani keratitis. Thus, this newly developed DHA will be beneficial to earlier diagnosis, more precise treatment, and improve prognosis of FK, by minimizing medical refractory events and surgical needs.

Clinical factors associated with positive corneal culture in suspected microbial keratitis

AIMS

Determine demographic and clinical characteristics associated with positive culture in suspected microbial keratitis.

METHODS

Retrospective audit of patients that had corneal scrapings between October 1999-September 2004 at Princess Alexandra Hospital. Clinical information was gathered from medical records, smear and culture results from the local microbiology database. Univariate and multivariate analyses of variables associated with positive cultures and calculation of population attributable risk percentage (PAR).

RESULTS

Univariate analysis showed that positive cultures were associated with patients over 60 years (81% vs 55%; p < 0.001), presenting visual acuity (VA) of 6/24 or worse (74% vs 57%; p = 0.012) or contact lens-related keratitis (CLK 77% vs 62%; p = 0.08). Analysis of patients' clinical presentation showed that positive culture was associated with a central epithelial defect (74% vs 57%; p = 0.012), anterior chamber reaction of 2 + cells or worse (73% vs 56%; p = 0.03), an epithelial defect of 2 mm or more in diameter (71% vs 50%; p = 0.006) or no prior treatment with antibiotics (68% vs 56%; p = 0.053). Multivariate analysis showed the independent variables associated with positive cultures were VA of 6/24 or worse on presentation, contact lens-related keratitis, age greater than 60 years, an anterior chamber reaction of 2 + cells or worse and no prior treatment with antibiotics. The factor with the highest attributable risk (PAR%) for a positive corneal scraping was VA of 6/24 or worse on presentation (21%).

CONCLUSIONS

In this series positive cultures were associated with poor presenting VA contact lens keratitis (CLK), older age, anterior chamber reaction and no prior treatment with antibiotics.

Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs

Bacterial keratitis (BK), a painful and fulminant bacterial infection of the cornea, is the most common type of vision-threatening infectious keratitis (IK). A rapid clinical diagnosis by an ophthalmologist may often help prevent BK patients from progression to corneal melting or even perforation, but many rural areas cannot afford an ophthalmologist. Thanks to the rapid development of deep learning (DL) algorithms, artificial intelligence via image could provide an immediate screening and recommendation for patients with red and painful eyes. Therefore, this study aims to elucidate the potentials of different DL algorithms for diagnosing BK via external eye photos. External eye photos of clinically suspected IK were consecutively collected from five referral centers. The candidate DL frameworks, including ResNet50, ResNeXt50, DenseNet121, SE-ResNet50, EfficientNets B0, B1, B2, and B3, were trained to recognize BK from the photo toward the target with the greatest area under the receiver operating characteristic curve (AUROC). Via five-cross validation, EfficientNet B3 showed the most excellent average AUROC, in which the average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was 74, 64, 77, and 61. There was no statistical difference in diagnostic accuracy and AUROC between any two of these DL frameworks. The diagnostic accuracy of these models (ranged from 69 to 72%) is comparable to that of the ophthalmologist (66% to 74%). Therefore, all these models are promising tools for diagnosing BK in first-line medical care units without ophthalmologists.

Bacterial keratitis: identifying the areas of clinical uncertainty

Bacterial keratitis is a common corneal infection that is treated with topical antimicrobials. By the time of presentation there may already be severe visual loss from corneal ulceration and opacity, which may persist despite treatment. There are significant differences in the associated risk factors and the bacterial isolates between high income and low- or middle-income countries, so that general management guidelines may not be appropriate. Although the diagnosis of bacterial keratitis may seem intuitive there are multiple uncertainties about the criteria that are used, which impacts the interpretation of investigations and recruitment to clinical studies. Importantly, the concept that bacterial keratitis can only be confirmed by culture ignores the approximately 50% of cases clinically consistent with bacterial keratitis in which investigations are negative. The aetiology of these culture-negative cases is unknown. Currently, the estimation of bacterial susceptibility to antimicrobials is based on data from systemic administration and achievable serum or tissue concentrations, rather than relevant corneal concentrations and biological activity in the cornea. The provision to the clinician of minimum inhibitory concentrations of the antimicrobials for the isolated bacteria would be an important step forward. An increase in the prevalence of antimicrobial resistance is a concern, but the effect this has on disease outcomes is yet unclear. Virulence factors are not routinely assessed although they may affect the pathogenicity of bacteria within species and affect outcomes. New technologies have been developed to detect and kill bacteria, and their application to bacterial keratitis is discussed. In this review we present the multiple areas of clinical uncertainty that hamper research and the clinical management of bacterial keratitis, and we address some of the assumptions and dogma that have become established in the literature.

Fungal keratitis due to Scopulariopsis brevicaulis and a potential promising therapeutic effect of antibacterial agents: A case report

INTRODUCTION

Microbial keratitis is a serious potentially blinding corneal infection. Contact lens wear remains the most common predisposing factor. Fungal keratitis represent only a small fraction of the overall number of cases of contact lens-associated microbial keratitis, however they are proportionally more severe.

PATIENT CONCERNS

An 18-year-old female, who occasionally used eye cosmetic soft contact lenses, presented with pain, redness, and blurring of vision in her left eye.

DIAGNOSIS

The left eye showed decreased visual acuity, central corneal ulcer and abscess, and severe ciliary injection. A provisional diagnosis of infectious keratitis was considered.

INTERVENTION

Corneal scrapings were aseptically collected and directly inoculated onto sterile bacterial and fungal agar plates that were immediately incubated. The patient was admitted and started on topical and systemic antibacterial agents.

OUTCOMES

The infection showed signs of satisfactory clinical resolution. However, the mold Scopulariopsis brevicaulis was isolated in pure colonies 5 days after presentation.

CONCLUSION

We report the first case from Jordan of fungal keratitis caused by the mold S brevicaulis. A high index of suspicion is required for fungal keratitis caused by S brevicaulis in immunocompetent patients who wear contact lenses despite its rarity. This fungal infection was successfully treated using antibacterial agents. However, larger studies are recommended to investigate the clinical effectiveness of antimicrobial agents that have both antibacterial and antifungal effects and to assess their role as empirical therapeutic modalities for infectious keratitis.

Diagnostic armamentarium of infectious keratitis: A comprehensive review

Infectious keratitis (IK) represents the leading cause of corneal blindness worldwide, particularly in developing countries. A good outcome of IK is contingent upon timely and accurate diagnosis followed by appropriate interventions. Currently, IK is primarily diagnosed on clinical grounds supplemented by microbiological investigations such as microscopic examination with stains, and culture and sensitivity testing. Although this is the most widely accepted practice adopted in most regions, such an approach is challenged by several factors, including indistinguishable clinical features shared among different causative organisms, polymicrobial infection, long diagnostic turnaround time, and variably low culture positivity rate. In this review, we aim to provide a comprehensive overview of the current diagnostic armamentarium of IK, encompassing conventional microbiological investigations, molecular diagnostics (including polymerase chain reaction and mass spectrometry), and imaging modalities (including anterior segment optical coherence tomography and in vivo confocal microscopy). We also highlight the potential roles of emerging technologies such as next-generation sequencing, artificial intelligence-assisted platforms. and tele-medicine in shaping the future diagnostic landscape of IK.

Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.

Distribution, Prevalence, and Causative Agents of Fungal Keratitis: A Systematic Review and Meta-Analysis (1990 to 2020)

Objectives

This study aims to provide an overview of the prevalence, distribution, and causative agents of fungal keratitis.

Methods

All the articles with data on the prevalence of fungal keratitis among various patient groups from January 1, 1990 to May 27, 2020 were retrieved through a systematic search in PubMed, Scopus, Web of Science, and Google Scholar. Data were extracted, and the pooled estimated prevalence of fungal keratitis, yeast/mold infection, the spectrum and frequency of various causative agents, and the pooled estimated prevalence of mixed infections were calculated in general and in various countries (wherever possible) using meta-analysis.

Results

From 11,235 articles retrieved in the primary search step, 169 met the inclusion criteria. The 169 eligible articles were divided into six groups and analyzed separately. The pooled prevalence of fungal keratitis was variable with values ranging from 0.05% among postkeratoplasty patients to 43.01% among patients with a clinical suspicion of fungal keratitis. There was also a country-dependent variation in the prevalence (Paraguay: 50.1% (95% CI, 35.11, 65.00); Ireland: 1.1% (95% CI, 0.03, 6.04)). Except for postkeratoplasty cases (yeast: 51.80%), in all patient groups, molds were more common than yeasts. Although more than 50 distinct species of fungi have been found to cause fungal keratitis, Fusarium species followed by Aspergillus species were the most common causes of the disease. In general, 9.29% (95% CI, 6.52, 12.38) of fungal keratitis cases were mixed with bacterial agents.

Conclusion

The prevalence of fungal keratitis can vary dramatically depending on the patient groups and geographical origin; however, the dominant causative agents are generally similar.

Fungal keratitis: Mechanisms of infection and management strategies

Fungal corneal ulcers are an uncommon, yet challenging, cause of vision loss. In the United States, geographic location appears to dictate not only the incidence of fungal ulcers, but also the fungal genera most encountered. These patterns of infection can be linked to environmental factors and individual characteristics of fungal organisms. Successful management of fungal ulcers is dependent on an early diagnosis. New diagnostic modalities like confocal microscopy and polymerase chain reaction are being increasingly used to detect and identify infectious organisms. Several novel therapies, including crosslinking and light therapy, are currently being tested as alternatives to conventional antifungal medications. We explore the biology of Candida, Fusarium, and Aspergillus, the three most common genera of fungi causing corneal ulcers in the United States and discuss current treatment regimens for the management of fungal keratitis.

Foundational concepts in the biology of bacterial keratitis

Bacterial infections of the cornea, or bacterial keratitis (BK), are notorious for causing rapidly fulminant disease and permanent vision loss, even among treated patients. In the last sixty years, dramatic upward trajectories in the frequency of BK have been observed internationally, driven in large part by the commercialization of hydrogel contact lenses in the late 1960s. Despite this worsening burden of disease, current evidence-based therapies for BK - including broad-spectrum topical antibiotics and, if indicated, topical corticosteroids - fail to salvage vision in a substantial proportion of affected patients. Amid growing concerns of rapidly diminishing antibiotic utility, there has been renewed interest in urgently needed novel treatments that may improve clinical outcomes on an individual and public health level. Bridging the translational gap in the care of BK requires the identification of new therapeutic targets and rational treatment design, but neither of these aims can be achieved without understanding the complex biological processes that determine how bacterial corneal infections arise, progress, and resolve. In this chapter, we synthesize the current wealth of human and animal experimental data that now inform our understanding of basic BK pathophysiology, in context with modern concepts in ocular immunology and microbiology. By identifying the key molecular determinants of clinical disease, we explore how novel treatments can be developed and translated into routine patient care.

Development of a novel micro-bead force spectroscopy approach to measure the ability of a thermo-active polymer to remove bacteria from a corneal model

Microbial keratitis occurs from the infection of the cornea by fungi and or bacteria. It remains one of the most common global causes of irreversible blindness accounting for 3.5% (36 million) of blind people as of 2015. This paper looks at the use of a bacteria binding polymer designed to bind Staphylococcus aureus and remove it from the corneal surface. Mechanical unbinding measurements were used to probe the interactions of a thermo-active bacteria-binding polymer, highly-branched poly(N-isopropyl acrylamide), functionalised with modified vancomycin end groups (HB-PNIPAM-Van) to bacteria placed on rabbit corneal surfaces studied ex-vivo. This was conducted during sequential temperature phase transitions of HB-PNIPAM-Van-S. aureus below, above and below the lower critical solution temperature (LCST) in 3 stages, in-vitro, using a novel micro-bead force spectroscopy (MBFS) approach via atomic force microscopy (AFM). The effect of temperature on the functionality of HB-PNIPAM-Van-S. aureus showed that the polymer-bacteria complex reduced the work done in removing bacterial aggregates at T > LCST (p < 0.05), exhibiting reversibility at T < LCST (p < 0.05). At T < LCST, the breaking force, number of unbinding events, percentage fitted segments in the short and long range, and the percentage of unbinding events occurring in the long range (> 2.5 µm) increased (p < 0.05). Furthermore, the LCST phase transition temperature showed 100 × more unbinding events in the long-range z-length (> 2.5 µm) compared to S. aureus aggregates only. Here, we present the first study using AFM to assess the reversible mechanical impact of a thermo-active polymer-binding bacteria on a natural corneal surface.

Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions

ABSTRACT

Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.

Beauveria bassiana keratitis: Management of an atypical clinical presentation

We report an atypical presentation of Beauveria bassiana keratitis which unusually presented in a 85-year-old patient with a corneal ulcer with an anterior segment inflammation and hypopyon. Despite negative culture and unspecific results from panfungal PCR-based sequencing, the patient was treated for a presumed fungal infection. Following clinical deterioration an emergency surgical intervention with apposition of a corneal patch was performed. Infection resolution was achieved following the introduction of systemic voriconazole to the topical one.

Fungal keratitis at a tertiary eye care in Northern Thailand: Etiology and prognostic factors for treatment outcomes

PURPOSE

To evaluate etiology and prognostic factors for treatment outcomes of fungal keratitis (FK).

METHODS

Culture-positive FK patients between 2012 and 2017 were reviewed. Treatment outcomes were categorized into success (resolved within two weeks), slow response and medication failure (no improvement or required surgery). Etiology and risk factors for poor treatment outcomes were analyzed.

RESULTS

A total of 113 eyes of 113 patients (77% males) were recruited. Ocular trauma (69.0%) was the most common predisposing factor. Of this, 80% were exposed to organic foreign bodies. The most common pathogen was Fusarium spp. (45.2%), while dematiaceous fungi were discovered in 29.6%. Medical treatment was successful in 24.8% of eyes, while 29.2% had a slow response and 42.5% failed medication. Therapeutic keratoplasty was performed in 22.1% of eyes and 11.5% eventually required eye removal. Significant risk factors for medication failure were advanced age (P = 0.005), delayed antifungal treatment (P = 0.038) and large-size lesion (P = 0.003).

CONCLUSIONS

Ocular trauma was the major predisposing factor of FK in the Northern Thailand. Fusarium was the most common identified pathogen. Many cases were refractory to medications and required surgical intervention. Aging, delayed treatment and a large lesion were predictors for poor outcomes.

Inhibitory effect of host ocular microenvironmental factors on chlorhexidine digluconate activity

Acanthamoeba spp. are free-living protozoan that cause a serious human eye disease called Acanthamoeba keratitis (AK). Several new and effective medical therapy for AK patients remains highly debated and therefore, CHG is still considered one of the first lines of treatment for AK patients. We hypothesized that ocular microenvironmental factors are responsible for Acanthamoeba drug resistance and clinical AK treatment failure. To investigate the influence of the ocular surface on CHG treatment, we tested the effect of several ocular elements on the anti-amoeba activity of CHG. The suspected inhibitory elements, including mucin, albumin, human and amoeba cell lysates, live and heat-killed bacteria, and cornea, were added to the amoebicidal activity platform, where amoeba was incubated with CHG at varying concentrations. Mucin showed a significant inhibitory effect on CHG activity against Acanthamoeba castellanii In contrast, albumin did not affect CHG treatment. Furthermore, human and amoeba cell lysates as well as live and heat-killed bacterial suspensions also significantly inhibited CHG activity. Additionally, we found that pig corneas also reduced CHG activity. In contrast, dry eye drops and their major component, propylene glycol, which is commonly used as eyewash material, did not have an impact on CHG activity. Our results demonstrate the effect of ocular microenvironmental factors on CHG activity and suggest that these factors may play a role in the development of amoeba resistance to CHG and treatment failure.