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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15928 |
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| _version_ | 1866912389950078976 |
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| author | Wang, Meng Lin, Tian Hou, Qingshan Lin, Aidi Wang, Jingcheng Peng, Qingsheng Nguyen, Truong X. Fang, Danqi Zou, Ke Xu, Ting Xue, Cancan Quek, Ten Cheer Yu, Qinkai Liu, Minxin Zhou, Hui Xiao, Zixuan He, Guiqin Liang, Huiyu Shi, Tingkun Chen, Man Liu, Linna Peng, Yuanyuan Wang, Lianyu Hu, Qiuming Chen, Junhong Zhang, Zhenhua Chen, Cheng Zhao, Yitian Liu, Dianbo Wu, Jianhua Chen, Xinjian Zhang, Changqing Nguyen, Triet Thanh Meng, Yanda Zheng, Yalin Tham, Yih Chung Cheung, Carol Y. Fu, Huazhu Chen, Haoyu Cheng, Ching-Yu |
| author_facet | Wang, Meng Lin, Tian Hou, Qingshan Lin, Aidi Wang, Jingcheng Peng, Qingsheng Nguyen, Truong X. Fang, Danqi Zou, Ke Xu, Ting Xue, Cancan Quek, Ten Cheer Yu, Qinkai Liu, Minxin Zhou, Hui Xiao, Zixuan He, Guiqin Liang, Huiyu Shi, Tingkun Chen, Man Liu, Linna Peng, Yuanyuan Wang, Lianyu Hu, Qiuming Chen, Junhong Zhang, Zhenhua Chen, Cheng Zhao, Yitian Liu, Dianbo Wu, Jianhua Chen, Xinjian Zhang, Changqing Nguyen, Triet Thanh Meng, Yanda Zheng, Yalin Tham, Yih Chung Cheung, Carol Y. Fu, Huazhu Chen, Haoyu Cheng, Ching-Yu |
| contents | Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We introduce GlobeReady, a clinician-friendly AI platform that enables fundus disease diagnosis that operates without retraining, fine-tuning, or the needs for technical expertise. GlobeReady demonstrates high accuracy across imaging modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs (CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography (OCT) scans. By leveraging training-free local feature augmentation, GlobeReady platform effectively mitigates domain shifts across centers and populations, achieving accuracies of 88.9-97.4% across five centers on average in China, 86.3-96.9% in Vietnam, and 73.4-91.0% in Singapore, and 90.2-98.9% in the UK. Incorporating a bulit-in confidence-quantifiable diagnostic mechanism further enhances the platform's accuracy to 94.9-99.4% with CFPs and 88.2-96.2% with OCT, while enabling identification of out-of-distribution cases with 86.3% accuracy across 49 common and rare fundus diseases using CFPs, and 90.6% accuracy across 13 diseases using OCT. Clinicians from countries rated GlobeReady highly for usability and clinical relevance (average score 4.6/5). These findings demonstrate GlobeReady's robustness, generalizability and potential to support global ophthalmic care without technical barriers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers Wang, Meng Lin, Tian Hou, Qingshan Lin, Aidi Wang, Jingcheng Peng, Qingsheng Nguyen, Truong X. Fang, Danqi Zou, Ke Xu, Ting Xue, Cancan Quek, Ten Cheer Yu, Qinkai Liu, Minxin Zhou, Hui Xiao, Zixuan He, Guiqin Liang, Huiyu Shi, Tingkun Chen, Man Liu, Linna Peng, Yuanyuan Wang, Lianyu Hu, Qiuming Chen, Junhong Zhang, Zhenhua Chen, Cheng Zhao, Yitian Liu, Dianbo Wu, Jianhua Chen, Xinjian Zhang, Changqing Nguyen, Triet Thanh Meng, Yanda Zheng, Yalin Tham, Yih Chung Cheung, Carol Y. Fu, Huazhu Chen, Haoyu Cheng, Ching-Yu Computer Vision and Pattern Recognition Artificial Intelligence Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We introduce GlobeReady, a clinician-friendly AI platform that enables fundus disease diagnosis that operates without retraining, fine-tuning, or the needs for technical expertise. GlobeReady demonstrates high accuracy across imaging modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs (CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography (OCT) scans. By leveraging training-free local feature augmentation, GlobeReady platform effectively mitigates domain shifts across centers and populations, achieving accuracies of 88.9-97.4% across five centers on average in China, 86.3-96.9% in Vietnam, and 73.4-91.0% in Singapore, and 90.2-98.9% in the UK. Incorporating a bulit-in confidence-quantifiable diagnostic mechanism further enhances the platform's accuracy to 94.9-99.4% with CFPs and 88.2-96.2% with OCT, while enabling identification of out-of-distribution cases with 86.3% accuracy across 49 common and rare fundus diseases using CFPs, and 90.6% accuracy across 13 diseases using OCT. Clinicians from countries rated GlobeReady highly for usability and clinical relevance (average score 4.6/5). These findings demonstrate GlobeReady's robustness, generalizability and potential to support global ophthalmic care without technical barriers. |
| title | A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15928 |