ArticleNPJ digital medicine2025
Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Corneal nerve regeneration: treatments and monitoring approaches.Annals of medicine · 2026Review
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Corneal Innervation Research at a Crossroads: A Tool-Driven Roadmap for the Future.Investigative ophthalmology & visual science · 2026Review
- An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization.Scientific data · 2026Article
- Applications of Artificial Intelligence in Corneal Nerve Images in Ophthalmology.Diagnostics (Basel, Switzerland) · 2026Review
- Electrotherapy in the management of neuropathic corneal pain: narrative review.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- Porcine Corneal Models as Translational Platforms for Innovative Therapies: Current Insights and Future Directions.Journal of functional biomaterials · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, microneuromas (aberrant growth and swelling of the corneal nerve endings) detected using in vivo confocal microscopy (IVCM) have emerged as a promising biomarker for NCP. However, this process is time-intensive and error-prone, limiting its clinical use and availability. In this work, we present a new NCP screening system based on a deep learning model trained to detect microneuromas using a multisite dataset with a combined total of 103,168 IVCM images. Our model showed excellent discriminative ability detecting microneuromas (AuROC: 0.97) and the ability to generalize to data from a new institution (AuROC: 0.90). Additionally, our pipeline provides an uncertainty quantification mechanism that allows it to communicate when its predictions are reliable, further increasing its clinical relevance.
Identifiers
What OpenQuestion holds
Registered trials
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.