ArticleTranslational vision science & technology2021
White Paper on Ophthalmic Imaging for Choroidal Nevus Identification and Transformation into Melanoma.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 36 citations in OpenAlex.
- Choroidal Melanocytic Lesion Detection Using Patch Vectors With a Foundational Vision Transformer.Translational vision science & technology · 2026Article
- Review
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.Translational vision science & technology · 2025Article
- The Small Choroidal Melanoma: Diagnosis, Treatment Outcomes, and Unending Controversy.Seminars in ophthalmology · 2025Review
- Factors associated with lost to follow-up and delayed follow-up in patients with choroidal nevus.Eye (London, England) · 2025Article
- Case Series: Retinal Pigment Epithelial Detachments Over Choroidal Nevi - What Is the Clinical Significance?Ocular oncology and pathology · 2025Article
- Automated segmentation for early detection of uveal melanoma.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2024Article
- Advances in multimodal imaging for diagnosis of pigmented ocular fundus lesions.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2024Review
- Using Deep Learning to Distinguish Highly Malignant Uveal Melanoma from Benign Choroidal Nevi.Journal of clinical medicine · 2024Article
- Multispectral Fundus Photography of Choroidal Nevi With Trans-Palpebral Illumination.Translational vision science & technology · 2024Article
- Multispectral Fundus Photography of Choroidal Nevi with Trans-Palpebral Illumination.medRxiv : the preprint server for health sciences · 2024Article
- Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning.Research square · 2023Article
- Impact of Choroidal Melanoma Characteristics on Progression-Free Survival in Patients Undergoing Hypofractionated Stereotactic Photon Radiotherapy.Ophthalmology and therapy · 2023Article
- Choroidal Nevus Features Associated with Subspecialty Referral.Ophthalmology. Retina · 2023Article
- Optical Density Ratio of Subretinal Fluid in Choroidal Melanomas Versus Choroidal Naevi Assessed by Optical Coherence Tomography.Investigative ophthalmology & visual science · 2023Article
- [Transformation of a choroidal nevus into a choroidal melanoma after decades].Die Ophthalmologie · 2023Article
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
- Can the MOLES acronym and scoring system improve the management of patients with melanocytic choroidal tumours?Eye (London, England) · 2023Review
- Clinical and Multimodal Imaging Features of Choroidal Nevi in the Korean Population.Journal of clinical medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 13 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To discuss the evolution of noninvasive diagnostic methods in the identification of choroidal nevus and determination of risk factors for malignant transformation as well as introduce the novel role that artificial intelligence (AI) can play in the diagnostic process. Methods: White paper. Results: Longstanding diagnostic methods to stratify benign choroidal nevus from choroidal melanoma and to further determine the risk for nevus transformation into melanoma have been dependent on recognition of key clinical features by ophthalmic examination. These risk factors have been derived from multiple large cohort research studies over the past several decades and have garnered widespread use throughout the world. More recent publications have applied ocular diagnostic testing (fundus photography, ultrasound examination, autofluorescence, and optical coherence tomography) to identify risk factors for the malignant transformation of choroidal nevus based on multimodal imaging features. The widespread usage of ophthalmic imaging systems to identify and follow choroidal nevus, in conjunction with the characterization of malignant transformation risk factors via diagnostic imaging, presents a novel path to apply AI. Conclusions: AI applied to existing ophthalmic imaging systems could be used for both identification of choroidal nevus and as a tool to aid in earlier detection of transformation to malignant melanoma. Translational Relevance: Advances in AI models applied to ophthalmic imaging systems have the potential to improve patient care, because earlier detection and treatment of melanoma has been proven to improve long-term clinical outcomes.
Indexed as
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.