Neural Network-Based Pipeline With High-Resolution Feature Enhancement and Low-Resolution Feature Preservation for Automated Treatment Decision of Graves’ Orbitopathy Patients-Reference-Cited by-同舟云学术

Neural Network-Based Pipeline With High-Resolution Feature Enhancement and Low-Resolution Feature Preservation for Automated Treatment Decision of Graves’ Orbitopathy Patients

Published:2024 Issue: Volume:12 Page:98426-98435
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Sanghyuck¹^ORCID,Asyraf Zulkifley Mohd²^ORCID,Kyu Lee Jeong³^ORCID,Lee Jaesung¹^ORCID

Affiliation:

1. Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea

2. Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan, Bangi, Malaysia

3. Department of Ophthalmology, Chung-Ang University College of Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea

Funder

Chung-Ang University Research Grants in 2023

National Research Foundation of Korea (NRF) through the Korean Government

Institute of Information and communications Technology Planning and Evaluation (IITP) through the Korean Government (MSIT), Artificial Intelligence Graduate School Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10599260.pdf?arnumber=10599260

Reference36 articles.

1. Current concepts regarding Graves’ orbitopathy

2. Facial expression of patients with Graves’ orbitopathy

3. Effects of block-replace regimen in patients with autoimmune hypothyroidism converted to Graves’ disease

4. Proposal for Standardization of Primary and Secondary Outcomes in Patients with Active, Moderate-to-Severe Graves’ Orbitopathy

5. The 2021 European Group on Graves’ orbitopathy (EUGOGO) clinical practice guidelines for the medical management of Graves’ orbitopathy