ArticleJournal of personalized medicine2022
An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 22 citations in OpenAlex.
- Accuracy of ChatGPT, Gemini, Copilot, and Claude to Blepharoplasty-Related Questions.Aesthetic plastic surgery · 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
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Ethical guidance for reporting and evaluating claims of AI outperforming human doctors.NPJ digital medicine · 2024Article
- Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos.JAMA network open · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Development and validation of a convolutional neural network to identify blepharoptosis.Scientific reports · 2023Article
- The Challenges and Therapeutic Prospects in Eye Disease.Journal of personalized medicine · 2023Article
- Orbital and eyelid diseases: The next breakthrough in artificial intelligence?Frontiers in cell and developmental biology · 2022Review
- From pixels to precision: Imaging technologies shaping oculofacial plastic surgery practice.Taiwan journal of ophthalmologyReview
- Facial artificial intelligence in ophthalmology and medicine: fundamental and transformative applications.Therapeutic advances in ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
The aim of this study is to develop an AI model that accurately identifies referable blepharoptosis automatically and to compare the AI model's performance to a group of non-ophthalmic physicians. In total, 1000 retrospective single-eye images from tertiary oculoplastic clinics were labeled by three oculoplastic surgeons as having either ptosis, including true and pseudoptosis, or a healthy eyelid. A convolutional neural network (CNN) was trained for binary classification. The same dataset was used in testing three non-ophthalmic physicians. The CNN model achieved a sensitivity of 92% and a specificity of 88%, compared with the non-ophthalmic physician group, which achieved a mean sensitivity of 72% and a mean specificity of 82.67%. The AI model showed better performance than the non-ophthalmic physician group in identifying referable blepharoptosis, including true and pseudoptosis, correctly. Therefore, artificial intelligence-aided tools have the potential to assist in the diagnosis and referral of blepharoptosis for general practitioners.
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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.