ArticleNPJ digital medicine2022
Artificial intelligence to detect malignant eyelid tumors from photographic images.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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
25 citing papers in PubMed, 57 citations in OpenAlex.
- Smartphone-Based Proactive Self-Screening for Ocular Surface Malignancies: A Nonrandomized Clinical Trial.JAMA ophthalmology · 2026Trial
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Review
- The Use of Deep Learning in Distinguishing Chalazion and Eyelid Mass.Journal of ophthalmology · 2026Article
- Deep learning-based non-invasive differential diagnosis of eyelid basal cell and sebaceous gland carcinomas using photographic images.International ophthalmology · 2025Article
- Oculoplastics and Augmented Intelligence: A Literature Review.Journal of clinical medicine · 2025Article
- Deep Learning-Based Detection of Ocular Surface Squamous Neoplasia from Ocular Surface Images.Ocular oncology and pathology · 2025Article
- Advancing predictive, preventive, and personalized medicine in eyelid diseases: a concerns-based and expandable screening system through structural dissection.The EPMA journal · 2025Article
- Deep learning-based mobile application for efficient eyelid tumor recognition in clinical images.NPJ digital medicine · 2025Article
- Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective.Cureus · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics.Cureus · 2024Review
- Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.Heliyon · 2024Article
- HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images.Biomedical engineering online · 2024Article
- DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones.Frontiers in cell and developmental biology · 2024Article
- Artificial intelligence and machine learning a new frontier in the diagnosis of ocular adnexal tumors: A review.SAGE open medicine · 2024Review
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Development and Validation of a Joint Attention-Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder.JAMA network open · 2023Article
- Facilitating deep learning through preprocessing of optical coherence tomography images.BMC ophthalmology · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
Malignant eyelid tumors can invade adjacent structures and pose a threat to vision and even life. Early identification of malignant eyelid tumors is crucial to avoiding substantial morbidity and mortality. However, differentiating malignant eyelid tumors from benign ones can be challenging for primary care physicians and even some ophthalmologists. Here, based on 1,417 photographic images from 851 patients across three hospitals, we developed an artificial intelligence system using a faster region-based convolutional neural network and deep learning classification networks to automatically locate eyelid tumors and then distinguish between malignant and benign eyelid tumors. The system performed well in both internal and external test sets (AUCs ranged from 0.899 to 0.955). The performance of the system is comparable to that of a senior ophthalmologist, indicating that this system has the potential to be used at the screening stage for promoting the early detection and treatment of malignant eyelid tumors.
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.