[Retracted] Design of Intelligent Diagnosis and Treatment System for Ophthalmic Diseases Based on Deep Neural Network Model

Abstract

Artificial intelligence (AI) has developed rapidly in the field of ophthalmology. Fundus images have become a research hotspot because they are easy to obtain and rich in biological information. The application of fundus image analysis (AI) in background image analysis has been deepened and expanded. At present, a variety of AI studies have been carried out in the clinical screening, diagnosis, and prognosis of eye diseases, and the research results have been gradually applied to clinical practice. The application of AI in fundus image analysis will improve the situation of lack of medical resources and low diagnosis efficiency. In the future, the research of AI eye images should focus on the comprehensive intelligent diagnosis of various ophthalmic diseases and complex diseases. The focus is to integrate standardized and high-quality data resources, improve algorithm efficiency, and formulate corresponding clinical research plans.

Citation trends in ophthalmology articles and keywords in mainland China, Hong Kong, and Taiwan since 2013 using temporal bar graphs (TBGs): Bibliometric analysis

“…[37,38] For example, fundus images have become a research hotspot due to their ease of acquisition and rich biological information. [39] AI research in health care, specifically machine learning and deep learning, has definite clinical relevance in ophthalmology. [40,41] The yearly growth of AI in ophthalmology publications has been 18.89% over the last 10 years [43] and was verified over the last 4 years in this study (at the bottom of Fig.…”

Computer vision for eye diseases detection using pre‐trained deep learning techniques and raspberry Pi

“…Using the Madrid and Zaragoza test suite for the dataset, the highest accuracy achieved was 0.92 with VGG19, while MobileNet had the lowest accuracy of 0.77. Another study by Zhou (2022) [20] used the M-ResNet model to develop an intelligent system for diagnosing cataracts, subconjunctival haemorrhage, keratitis, and pterygium. The results demonstrated the system's effectiveness, with the best accuracy reaching 0.875.…”

Retracted: Design of Intelligent Diagnosis and Treatment System for Ophthalmic Diseases Based on Deep Neural Network Model

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”