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Wang CY, Nguyen HT, Fan WS, Lue JH, Saenprasarn P, Chen MM, Huang SY, Lin FC, Wang HC. Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration. Diagnostics (Basel) 2024; 14:1285. [PMID: 38928700 PMCID: PMC11202918 DOI: 10.3390/diagnostics14121285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/12/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.
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Affiliation(s)
- Ching-Yu Wang
- Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan; (C.-Y.W.); (W.-S.F.)
| | - Hong-Thai Nguyen
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
| | - Wen-Shuang Fan
- Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan; (C.-Y.W.); (W.-S.F.)
| | - Jiann-Hwa Lue
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
| | - Penchun Saenprasarn
- School of Nursing, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160, Thailand;
| | - Meei-Maan Chen
- Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan;
| | - Shuan-Yu Huang
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
| | - Fen-Chi Lin
- Department of Ophthalmology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Kaohsiung City 80284, Taiwan
| | - Hsiang-Chen Wang
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
- Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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Qian X, Xian S, Yifei S, Wei G, Liu H, Xiaoming X, Chu C, Yilong Y, Shuang Y, Kai M, Mei C, Yi Q. External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study. Eye (Lond) 2023; 37:3813-3818. [PMID: 37322379 PMCID: PMC10698045 DOI: 10.1038/s41433-023-02622-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 05/17/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
OBJECTIVES To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort. METHODS We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated. RESULTS In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves (AUC) of 95.17% and 96.64%, and significantly higher sensitivity of 91.75% and 91.41%, respectively, compared to manual graders. On the subsets complicated with retinal comorbidities, such as diabetic retinopathy or age-related macular degeneration, in validation datasets 1 and 2, the algorithm achieved accuracy of 87.54% and 93.81%, and AUC of 97.02% and 97.46%, respectively. In validation dataset 3, the algorithm achieved comparable accuracy of 81.98% and AUC of 87.49%, with a sensitivity of 83.61% and specificity of 81.76% on GON recognition specifically in the HM population. CONCLUSIONS With acceptable generalization capability across varying levels of image quality, different clinical centres, or certain retinal comorbidities, such as HM, the automatic AI diagnostic system had the potential to provide expert-level glaucoma detection.
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Affiliation(s)
- Xu Qian
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Song Xian
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Su Yifei
- Global Health Research Center, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu Province, 215316, China
| | - Guo Wei
- Lunan Eye Hospital, Linyi, 276000, China
| | - Hanruo Liu
- Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100730, China
| | - Xi Xiaoming
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | | | - Yin Yilong
- School of Software, Shandong University, Jinan, 250101, China
| | - Yu Shuang
- Tencent Healthcare, Shenzhen, 51800, China
| | - Ma Kai
- Tencent Healthcare, Shenzhen, 51800, China
| | - Cheng Mei
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Qu Yi
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China.
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China.
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China.
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