ArticleFrontiers in cellular and infection microbiology2022
Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network.
Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 24 citations in OpenAlex.
- Profiling of ocular surface microbiome and its ocular types in children and adolescents.Communications medicine · 2026Article
- Crosstalk between gut microbiota and RNA N6-methyladenosine modification in diabetic retinopathy.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- The Ocular Surface Microbiome in Homeostasis and Dysbiosis.Microorganisms · 2025Review
- Microbiologic analysis of 140 patients undergoing revision total knee arthroplasty due to periprosthetic joint infection and aseptic loosening: droplet digital PCR analysis.Journal of orthopaedic surgery and research · 2025Article
- Achromobacter in the Conjunctival Sac Microbiota: Potential Association With Acanthamoeba Keratitis Related to Orthokeratology Lenses.Investigative ophthalmology & visual science · 2025Article
- Alterations in ocular fungal microbiota in patients with fungal keratitis: a comparative study from coastal regions of Eastern China.BMC ophthalmology · 2025Article
- Multi-cohort analysis identifying core ocular surface microbiome and bacterial alterations in eye diseases.Eye (London, England) · 2025Article
- A Tear-Based Approach for Rapid Identification of Bacterial Pathogens in Corneal Ulcers Using Nanopore Sequencing.Translational vision science & technology · 2025Observational
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Living with your biome: how the bacterial microbiome impacts ocular surface health and disease.Expert review of ophthalmology · 2024Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Metagenome Investigation of Ocular Microbiota of Cataract Patients With and Without Type 2 Diabetes.Translational vision science & technology · 2023Article
- Evaluation ofMicroorganisms · 2023Article
- Subretinal Drusenoid Deposits and Lower Serum High-Density Lipoprotein Cholesterol Levels Possess Latent Relation to Cardiovascular Disease and Can Be a Feasible Predictor.Computational and mathematical methods in medicine · 2022Article
- The species-level microbiota of healthy eyes revealed by the integration of metataxonomics with culturomics and genome analysis.Frontiers in microbiology · 2022Article
- Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy Images.Healthcare technology lettersArticle
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5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Both healthy and diseased human ocular surfaces possess their own microbiota. If allowed, opportunistic pathogens within the ocular microbiota may cause microbial keratitis (MK). However, the nonpathogenic component of the ocular microbiota has been proven to undermine the performance of culture, the gold standard of the etiological diagnosis for MK. As the conjunctival bacterial microbiota generates unique alterations with various oculopathies, this study aimed to evaluate the feasibility of distinguishing MK using machine learning based on the characteristics of the conjunctival bacterial microbiome associated with various types of MK. This study also aimed to reveal which bacterial genera constitute the core of the interaction network of the conjunctival bacterial microbiome. Conjunctival swabs collected from the diseased eyes of MK patients and the randomly chosen normal eyes of healthy volunteers were subjected for high-throughput 16S rDNA sequencing. The relative content of each bacterial genus and the composition of bacterial gene functions in every sample were used to establish identification models with the random forest algorithm. Tenfold cross validation was adopted. Accuracy was 96.25% using the bacterial microbiota structure and 93.75% using the bacterial gene functional composition. Therefore, machine learning with the conjunctival bacterial microbiome characteristics might be used for differentiation of MKs as a noninvasive supplementary approach. In addition, this study found that
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