Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (781 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A microscope with a light source that can be projected into a linear beam. It allows cross-sectional viewing of the aqueous humor; conjuncteiva; cornea; eyelids; IRIS; and lens (see LENS, EYE) of the eye.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Lamp, Slit; Lamps, Slit; Slit Lamps; Slit Lamp Biomicroscope; Biomicroscope, Slit Lamp; Biomicroscopes, Slit Lamp; Lamp Biomicroscope, Slit; Lamp Biomicroscopes, Slit.
Where it fits in the MeSH classification
- Broader topics
- Ophthalmoscopes
- See also
- Slit Lamp Microscopy
Related concepts
- Aqueous Humor
- Conjuncteiva
- Cornea
- Eyelids
Research landscape
PubMed indexes 501 records tagged with the MeSH heading “Slit Lamp” (counts retrieved 2026-11-04). 124 of them (24.8%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 269 records.
Reviews account for 15 records (3.0%) and meta-analyses for 0. Randomized controlled trials number 8 (1.6%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 108 |
| 2000-2009 | 0 |
| 2010-2019 | 269 |
| 2020 onward | 124 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 15 | 3.0% |
| Meta-analyses | 0 | 0.0% |
| Randomized controlled trials | 8 | 1.6% |
Method: PubMed query "Slit Lamp"[MeSH Terms] with publication-type and date filters, retrieved 2026-11-04.
Reading the evidence in diagnostics and medical technology
A test or device is judged by how well it detects a condition, how often it gives false alarms or misses cases, and whether using it improves outcomes for people. Accuracy depends on who is tested and how common the condition is, so a test that performs well in a specialist hospital can perform differently in the community. Check the reference standard, the population studied, the sensitivity and specificity and whether results were confirmed in a separate group.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis.
- Closing excerpt
- We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (198 words)
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the "face" of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34811468 · DOI · PMC8608802
Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Ocular images play an essential role in ophthalmology.
- Closing excerpt
- Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (222 words)
Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs and LSTM (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model's performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths. The quantitative results demonstrated that our proposed model offers exceptional performance with mean accuracy (92.22), sensitivity (88.55), specificity (94.31) and AUC (97.18). Moreover, the model achieves real-time prediction with only 27.6ms for single sequence, and simultaneously predicts sequence data with lengths of 3-5. Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 30063738 · DOI · PMC6067742
Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device [3]
Abstract reading summary — source excerpts
- Opening excerpt
- There is currently no objective portable screening modality for narrow angles in the community.
- Closing excerpt
- This study suggests the possibility of estimating central ACD using slit lamp images taken from portable devices.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (178 words)
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to predict the central anterior chamber depth (ACD) of phakic patients with undilated pupils. Patients 60 years or older with no history of laser or intraocular surgery were recruited. Slit lamp images were taken with MIDAS, followed by anterior segment optical coherence tomography (ASOCT; Casia SS-1000, Tomey, Nagoya, Japan). After manual annotation of the anatomical landmarks of the slit lamp photos, machine learning was applied after image processing and feature extraction to predict the ACD. These values were then compared with those acquired from the ASOCT. Sixty-six eyes (right = 39, 59.1%) were included for analysis. The predicted ACD values formed a strong positive correlation with the measured ACD values from ASOCT (R2 = 0.91 for training data and R2 = 0.73 for test data). This study suggests the possibility of estimating central ACD using slit lamp images taken from portable devices.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34198935 · DOI · PMC8227501
Evaluation of the KOWA SL-19 Plus Portable Slit Lamp With Integrated Digital Camera for Veterinary Ophthalmic Imaging: A Comparative Study With Smartphone-Based Systems [4]
Abstract reading summary — source excerpts
- Opening excerpt
- ObjectiveThis study compares the performance of the KOWA SL-19 Plus portable slit lamp, equipped with an integrated digital camera, and a smartphone-based imaging system adapted to fit on the slit lamp eyepiece for veterinary ophthalmic imaging.
- Closing excerpt
- It presents a promising step forward for both clinical diagnostics and educational purposes.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (183 words)
ObjectiveThis study compares the performance of the KOWA SL-19 Plus portable slit lamp, equipped with an integrated digital camera, and a smartphone-based imaging system adapted to fit on the slit lamp eyepiece for veterinary ophthalmic imaging. Both devices were tested for their efficiency, image quality, and diagnostic utility in clinical settings.Animals studiedA total of 20 eyes from 13 animals (7 dogs and 6 cats), presenting various ophthalmic conditions, were examined using both devices.Procedure(s)During the ophthalmic examination, videos were captured using both the KOWA SL-19 Plus and the smartphone-based system, then the best picture was extracted. Extraction time was measured. Thirty-nine board-certified ophthalmologists evaluated the images together, focusing on image quality and diagnostic value.ResultsThe KOWA SL-19 Plus outperformed the smartphone-based system in all categories. The mean extraction time for the KOWA SL-19 Plus was significantly faster (65.47 s) than for the smartphone system (115.50 s, p ConclusionsThe KOWA SL-19 Plus is a valuable tool for veterinary ophthalmic imaging, offering superior image quality and faster processing times compared to the smartphone-based system. It presents a promising step forward for both clinical diagnostics and educational purposes.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40222962 · DOI · PMC12765205
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article The pathophysiology, diagnosis, and treatment of dry eye disease [5]
- Research article Intraocular light scatter, reflections, fluorescence and absorption: what we see in the slit lamp [6]
- Research article Do Slit-Lamp Shields and Face Masks Protect Ophthalmologists amidst COVID-19? [7]
- Research article Slit Lamp-Based Ocular Scoring Systems in Toxicology and Drug Development: A Literature Survey [8]
- Research article [The revised newest IC³D classification of corneal dystrophies] [9]
- Research article Considerations in the use of slit lamp shields to reduce the risk of respiratory virus transmission in coronavirus disease 2019 [10]
References
- Koyama A, Miyazaki D, Nakagawa Y, Ayatsuka Y, Miyake H, Ehara F, et al.. Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images. . 2021. PMID 34811468 · DOI · PMC8608802
- Jiang J, Liu X, Liu L, Wang S, Long E, Yang H, et al.. Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. . 2018. PMID 30063738 · DOI · PMC6067742
- Chen D, Ho Y, Sasa Y, Lee J, Yen CC, Tan C. Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device. . 2021. PMID 34198935 · DOI · PMC8227501
- Michaud B. Evaluation of the KOWA SL-19 Plus Portable Slit Lamp With Integrated Digital Camera for Veterinary Ophthalmic Imaging: A Comparative Study With Smartphone-Based Systems. . 2026. PMID 40222962 · DOI · PMC12765205
- Messmer EM. The pathophysiology, diagnosis, and treatment of dry eye disease. Dtsch Arztebl Int. 2015. PMID 25686388 · DOI · PMC4335585
- van den Berg TJTP. Intraocular light scatter, reflections, fluorescence and absorption: what we see in the slit lamp. Ophthalmic Physiol Opt. 2018. PMID 29265476 · DOI
- Ong SC, Razali MAB, Shaffiee L, Yap JX, Fei TTY, Loon SC, et al.. Do Slit-Lamp Shields and Face Masks Protect Ophthalmologists amidst COVID-19?. Ophthalmology. 2020. PMID 32574762 · DOI · PMC7306107
- Eaton JS, Miller PE, Bentley E, Thomasy SM, Murphy CJ. Slit Lamp-Based Ocular Scoring Systems in Toxicology and Drug Development: A Literature Survey. J Ocul Pharmacol Ther. 2017. PMID 29111862 · DOI
- Seitz B, Lisch W, Weiss J. [The revised newest IC³D classification of corneal dystrophies]. Klin Monbl Augenheilkd. 2015. PMID 25803558 · DOI
- Ong SC, Yap JX, Tay TYF, Mo Y, Loon SC, Koh V. Considerations in the use of slit lamp shields to reduce the risk of respiratory virus transmission in coronavirus disease 2019. Curr Opin Ophthalmol. 2020. PMID 32694271 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.