ArticleFrontiers in medicine2021
The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis Using
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Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal.BMJ health & care informatics · 2023Pooled it
- In Vivo Confocal Microscopy and Anterior Segment Optical Coherence Tomography in Optimizing Diagnosis and Therapeutic Management in Fungal Keratitis: Case Reports and Literature Review.Journal of clinical medicine · 2025Article
- An explainable unsupervised learning approach for anomaly detection on cornealFrontiers in bioengineering and biotechnology · 2025Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images.Translational vision science & technology · 2023Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.Translational vision science & technology · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.Scientific reports · 2022Article
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data.Ophthalmology scienceArticle
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Fan XuGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Li JiangGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Wenjing HeGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Guangyi HuangGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Yiyi HongGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Fen TangGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Jian LvGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Yunru LinGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Yikun QinChina-ASEAN Information Harbor Co., Ltd., Nanning, China.
Rushi LanGuangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China.
Xipeng PanGuangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China.
Siming ZengGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Min LiGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Qi ChenGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Ningning TangGuangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
artificial intelligencedeep learningexplainabilityfungal keratitisin vivo confocal microscopy
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.