ArticleNPJ digital medicine2020
Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Nonadherence to anti-VEGF intravitreal injections in patients with diabetic macular edema : The names of the authors.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Neural Network-Based Prediction of Post-Operative Visual Outcomes Following Secondary Pediatric Intraocular Lens Implantation.Children (Basel, Switzerland) · 2025Article
- A qualitative study of the family caregiver burden for caregivers of children with congenital cataracts from a social ecosystem theory perspective.BMC nursing · 2025Article
- Stakeholder-driven multi-stage adaptive real-world theme-oriented (SMART) telehealth evaluation framework: a scoping review.Lancet regional health. Americas · 2025Review
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
- Artificial intelligence technology in ophthalmology public health: current applications and future directions.Frontiers in cell and developmental biology · 2025Review
- Ocular Pathology and Genetics: Transformative Role of Artificial Intelligence (AI) in Anterior Segment Diseases.Cureus · 2024Review
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).International journal of ophthalmology · 2023Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Application of artificial intelligence in cataract management: current and future directions.Eye and vision (London, England) · 2022Review
- Artificial Intelligence: Review of Current and Future Applications in Medicine.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2021Review
- A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2021Review
- Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets.Frontiers in medicine · 2021Article
- Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
41 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.
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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.