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Diagnosing Cataracts in the Digital Age: A Survey on AI, Metaverse, and Digital Twin Applications.
Jones, Aida; Vijayan, Thulasi Bai; John, Sheila.
Afiliación
  • Jones A; Department of ECE, KCG College of Technology, Chennai, India.
  • Vijayan TB; Department of ECE, KCG College of Technology, Chennai, India.
  • John S; Department of Teleophthalmology, Sankara Nethralaya, Medical Research Foundation, Chennai, India.
Semin Ophthalmol ; : 1-8, 2024 Sep 20.
Article en En | MEDLINE | ID: mdl-39300918
ABSTRACT

PURPOSE:

The study explores the evolving landscape of cataract diagnosis, focusing on both traditional methods and innovative technological integrations. It aims to address challenges with subjectivity in traditional cataract grading and to evaluate how new technologies can enhance diagnostic accuracy and accessibility.

METHODS:

The research introduces and examines the use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in automating and improving cataract screening processes. It also explores the role of the Metaverse, Digital Twins, and Teleophthalmology for immersive patient education, real-time virtual replicas of eyes, and remote access to specialized care.

RESULTS:

Various ML and DL techniques demonstrated significant accuracy in cataract detection. The integration of these technologies, along with the Metaverse, Digital Twins, and Teleophthalmology, provides a comprehensive framework for accurate and accessible cataract diagnosis.

CONCLUSION:

There is a notable paradigm shift toward individualized, predictive, and transformative eye care. The advancements in technology address existing diagnostic challenges and mitigate the shortage of ophthalmologists by extending high-quality care to underserved regions. These developments pave the way for improved cataract management and broader accessibility.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Semin Ophthalmol Asunto de la revista: OFTALMOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Semin Ophthalmol Asunto de la revista: OFTALMOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido