1
|
Ying JN, Li H, Zhang YY, Li WD, Yi QY. Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research. Int J Ophthalmol 2024; 17:1138-1143. [PMID: 38895690 PMCID: PMC11144766 DOI: 10.18240/ijo.2024.06.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 06/21/2024] Open
Abstract
With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.
Collapse
Affiliation(s)
- Jia-Ning Ying
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
| | - Hu Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
| | - Yan-Yan Zhang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
| | - Wen-Die Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
| | - Quan-Yong Yi
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315042, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China
| |
Collapse
|
2
|
Zhang YH, Feng J, Yi CY, Deng XY, Zhou YJ, Tian L, Jie Y. Dynamic tear meniscus parameters in complete blinking: insights into dry eye assessment. Int J Ophthalmol 2023; 16:1911-1918. [PMID: 38111923 PMCID: PMC10700063 DOI: 10.18240/ijo.2023.12.01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 09/11/2023] [Indexed: 12/20/2023] Open
Abstract
AIM To investigate the relationship between dynamic tear meniscus parameters and dry eye using an automated tear meniscus segmentation method. METHODS The analysis of tear meniscus videos captured within 5s after a complete blink includes data from 38 participates. By processing video data, several key parameters including the average height of the tear meniscus at different lengths, the curvature of the tear meniscus's upper boundary, and the total area of the tear meniscus in each frame were calculated. The effective values of these dynamic parameters were then linearly fitted to explore the relationship between their changing trends and dry eye disease. RESULTS In 94.74% of the samples, the average height of central tear meniscus increased over time. Moreover, 97.37% of the samples exhibited an increase in the overall tear meniscus height (TMH) and area from the nasal to temporal side. Notably, the central TMH increased at a faster rate compared to the nasal side with the temporal side showing the slowest ascent. Statistical analysis indicates that the upper boundary curvature of the whole tear meniscus as well as the tear meniscus of the nasal side (2, 3, and 4 mm) aid in identifying the presence of dry eye and assessing its severity. CONCLUSION This study contributes to the understanding of tear meniscus dynamics as potential markers for dry eye, utilizing an automated and non-invasive approach that has implications for clinical assessment.
Collapse
Affiliation(s)
- Ying-Huai Zhang
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, Guangdong Province, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen 518055, Guangdong Province, China
| | - Jun Feng
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing 100730, China
| | - Chen-Yuan Yi
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, Guangdong Province, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen 518055, Guangdong Province, China
| | - Xian-Yu Deng
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, Guangdong Province, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen 518055, Guangdong Province, China
| | - Yong-Jin Zhou
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, Guangdong Province, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen 518055, Guangdong Province, China
| | - Lei Tian
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing 100730, China
| | - Ying Jie
- Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing 100730, China
| |
Collapse
|
3
|
Chen X, Yu Y, Nie H, Qin X, Bai W, Ren J, Yao J, Li J, Jiang Q. Insights into adeno-associated virus-based ocular gene therapy: A bibliometric and visual analysis. Medicine (Baltimore) 2023; 102:e34043. [PMID: 37327269 PMCID: PMC10270495 DOI: 10.1097/md.0000000000034043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/30/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Adeno-associated virus (AAV) plays a vital role in ocular gene therapy and has been widely studied since 1996. This study summarizes and explores the publication outputs and future research trends of AAV-based ocular gene therapy. METHODS Publications and data about AAV-based ocular gene therapy were downloaded from the Web of Science Core Collection or ClinicalTrials.gov database. The publications and data were analyzed by Microsoft Excel, CiteSpace, VOS viewer, and a free online platform (http://bibliometric.com). RESULTS Totally 832 publications from the Web of Science Core Collection relevant to AAV-based ocular gene therapy were published from 1996 to 2022. These publications were contributed by research institutes from 42 countries or regions. The US contributed the most publications among these countries or regions, notably the University of Florida. Hauswirth WW was the most productive author. "Efficacy" and "safety" are the main focus areas for future research according to the references and keywords analysis. Eighty clinical trials examined AAV-based ocular gene therapy were registered on ClinicalTrials.Gov. Institutes from the US and European did the dominant number or the large proportion of the trials. CONCLUSIONS The research focus of the AAV-based ocular gene therapy has transitioned from the study in biological theory to clinical trialing. The AAV-based gene therapy is not limited to inherited retinal diseases but various ocular diseases.
Collapse
Affiliation(s)
- Xi Chen
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Ophthalmology, Northern Jiangsu People’s Hospital, Yangzhou, China
| | - Yang Yu
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huiling Nie
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xun Qin
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wen Bai
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junsong Ren
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jin Yao
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Juxue Li
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, Jiangsu, China
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qin Jiang
- Affiliated Eye Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| |
Collapse
|
4
|
Su H, Gao L, Lu Y, Jing H, Hong J, Huang L, Chen Z. Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation. Front Cell Dev Biol 2023; 11:1196191. [PMID: 37228648 PMCID: PMC10203622 DOI: 10.3389/fcell.2023.1196191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Accurate retinal vessel segmentation from fundus images is essential for eye disease diagnosis. Many deep learning methods have shown great performance in this task but still struggle with limited annotated data. To alleviate this issue, we propose an Attention-Guided Cascaded Network (AGC-Net) that learns more valuable vessel features from a few fundus images. Attention-guided cascaded network consists of two stages: the coarse stage produces a rough vessel prediction map from the fundus image, and the fine stage refines the missing vessel details from this map. In attention-guided cascaded network, we incorporate an inter-stage attention module (ISAM) to cascade the backbone of these two stages, which helps the fine stage focus on vessel regions for better refinement. We also propose Pixel-Importance-Balance Loss (PIB Loss) to train the model, which avoids gradient domination by non-vascular pixels during backpropagation. We evaluate our methods on two mainstream fundus image datasets (i.e., DRIVE and CHASE-DB1) and achieve AUCs of 0.9882 and 0.9914, respectively. Experimental results show that our method outperforms other state-of-the-art methods in performance.
Collapse
Affiliation(s)
- Hexing Su
- Faculty of Intelligent Manufacturing, Wu Yi University, Jiangmen, China
| | - Le Gao
- Faculty of Intelligent Manufacturing, Wu Yi University, Jiangmen, China
| | - Yichao Lu
- Faculty of Intelligent Manufacturing, Wu Yi University, Jiangmen, China
| | - Han Jing
- Faculty of Intelligent Manufacturing, Wu Yi University, Jiangmen, China
| | - Jin Hong
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Li Huang
- Faculty of Intelligent Manufacturing, Wu Yi University, Jiangmen, China
| | - Zequn Chen
- Faculty of Social Sciences, Lingnan University, Hongkong, China
| |
Collapse
|
5
|
Zhang Y, Rao J, Wu X, Zhou Y, Liu G, Zhang H. Automatic measurement of exophthalmos based orbital CT images using deep learning. Front Cell Dev Biol 2023; 11:1135959. [PMID: 36910161 PMCID: PMC9998665 DOI: 10.3389/fcell.2023.1135959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
Introduction: Objective, accurate, and efficient measurement of exophthalmos is imperative for diagnosing orbital diseases that cause abnormal degrees of exophthalmos (such as thyroid-related eye diseases) and for quantifying treatment effects. Methods: To address the limitations of existing clinical methods for measuring exophthalmos, such as poor reproducibility, low reliability, and subjectivity, we propose a method that uses deep learning and image processing techniques to measure the exophthalmos. The proposed method calculates two vertical distances; the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the outer edge of the orbit in axial CT images and the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the upper and lower outer edges of the orbit in sagittal CT images. Results: Based on the dataset used, the results of the present method are in good agreement with those measured manually by clinicians, achieving a concordance correlation coefficient (CCC) of 0.9895 and an intraclass correlation coefficient (ICC) of 0.9698 on axial CT images while achieving a CCC of 0.9902 and an ICC of 0.9773 on sagittal CT images. Discussion: In summary, our method can provide a fully automated measurement of the exophthalmos based on orbital CT images. The proposed method is reproducible, shows high accuracy and objectivity, aids in the diagnosis of relevant orbital diseases, and can quantify treatment effects.
Collapse
Affiliation(s)
- Yinghuai Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen, China
| | - Jing Rao
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China.,Shenzhen Eye Institute, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China.,Shenzhen Eye Institute, Shenzhen, China
| | - Yongjin Zhou
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen, China
| | - Guiqin Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China.,Shenzhen Eye Institute, Shenzhen, China
| | - Hua Zhang
- Shenzhen Overseas Chinese Town Hospital, Shenzhen, China
| |
Collapse
|
6
|
Wang R, Zuo G, Li K, Li W, Xuan Z, Han Y, Yang W. Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy. Front Endocrinol (Lausanne) 2022; 13:1036426. [PMID: 36387891 PMCID: PMC9659570 DOI: 10.3389/fendo.2022.1036426] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR. METHODS Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software. RESULTS Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning. CONCLUSION Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.
Collapse
Affiliation(s)
- Ruoyu Wang
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guangxi Zuo
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Wangting Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Zhiqiang Xuan
- Institute of Occupational Health and Radiation Protection, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
| | - Yongzhao Han
- Affiliated Jiangning Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
| | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
| |
Collapse
|