8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

          Related collections

          Most cited references65

          • Record: found
          • Abstract: found
          • Article: not found

          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Deep learning in neural networks: An overview

            In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

              Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                24 January 2023
                2023
                : 11
                : 1124775
                Affiliations
                Department of Ophthalmology , Changzheng Hospital of Naval Medicine University , Shanghai, China
                Author notes

                Edited by: Yanwu Xu, Baidu (China), China

                Reviewed by: Kai Jin, Zhejiang University, China

                Huasheng Yang, Sun Yat-sen University, China

                Ming Lin, Shanghai Jiao Tong University, China

                *Correspondence: Ruili Wei, ruiliwei@ 123456smmu.edu.cn
                [ † ]

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Molecular and Cellular Pathology, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                1124775
                10.3389/fcell.2023.1124775
                9903073
                36760363
                fbca32e5-6dba-4b75-8771-f4e7261835d9
                Copyright © 2023 Diao, Chen, Shen, Li, Chen, He, Chen, Mou, Ma and Wei.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 December 2022
                : 12 January 2023
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81770959 81570885
                This study was funded by the National Natural Science Foundation of China (grant No. 81770959 and No. 81570885).
                Categories
                Cell and Developmental Biology
                Review

                thyroid-associated ophthalmopathy,artificial intelligence,deep learning,automated diagnosis,facial images

                Comments

                Comment on this article