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DeepFundus: A flow-cytometry-like image quality classifier for boosting the whole life cycle of medical artificial intelligence.

Lixue Liu | Xiaohang Wu | Duoru Lin | Lanqin Zhao | Mingyuan Li | Dongyuan Yun | Zhenzhe Lin | Jianyu Pang | Longhui Li | Yuxuan Wu | Weiyi Lai | Wei Xiao | Yuanjun Shang | Weibo Feng | Xiao Tan | Qiang Li | Shenzhen Liu | Xinxin Lin | Jiaxin Sun | Yiqi Zhao | Ximei Yang | Qinying Ye | Yuesi Zhong | Xi Huang | Yuan He | Ziwei Fu | Yi Xiang | Li Zhang | Mingwei Zhao | Jinfeng Qu | Fan Xu | Peng Lu | Jianqiao Li | Fabao Xu | Wenbin Wei | Li Dong | Guangzheng Dai | Xingru He | Wentao Yan | Qiaolin Zhu | Linna Lu | Jiaying Zhang | Wei Zhou | Xiangda Meng | Shiying Li | Mei Shen | Qin Jiang | Nan Chen | Xingtao Zhou | Meiyan Li | Yan Wang | Haohan Zou | Hua Zhong | Wenyan Yang | Wulin Shou | Xingwu Zhong | Zhenduo Yang | Lin Ding | Yongcheng Hu | Gang Tan | Wanji He | Xin Zhao | Yuzhong Chen | Yizhi Liu | Haotian Lin
Cell reports. Medicine | 2023

Medical artificial intelligence (AI) has been moving from the research phase to clinical implementation. However, most AI-based models are mainly built using high-quality images preprocessed in the laboratory, which is not representative of real-world settings. This dataset bias proves a major driver of AI system dysfunction. Inspired by the design of flow cytometry, DeepFundus, a deep-learning-based fundus image classifier, is developed to provide automated and multidimensional image sorting to address this data quality gap. DeepFundus achieves areas under the receiver operating characteristic curves (AUCs) over 0.9 in image classification concerning overall quality, clinical quality factors, and structural quality analysis on both the internal test and national validation datasets. Additionally, DeepFundus can be integrated into both model development and clinical application of AI diagnostics to significantly enhance model performance for detecting multiple retinopathies. DeepFundus can be used to construct a data-driven paradigm for improving the entire life cycle of medical AI practice.

Pubmed ID: 36669488

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ClinicalTrials.gov (tool)

RRID:SCR_002309

Registry and results database of federally and privately supported clinical trials conducted in United States and around world. Provides information about purpose of trial, who may participate, locations, and phone numbers for more details. This information should be used in conjunction with advice from health care professionals.Offers information for locating federally and privately supported clinical trials for wide range of diseases and conditions. Research study in human volunteers to answer specific health questions. Interventional trials determine whether experimental treatments or new ways of using known therapies are safe and effective under controlled environments. Observational trials address health issues in large groups of people or populations in natural settings. ClinicalTrials.gov contains trials sponsored by National Institutes of Health, other federal agencies, and private industry. Studies listed in database are conducted in all 50 States and in 178 countries.

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tensorflow (tool)

RRID:SCR_016345

Software as an open source machine learning framework for everyone. Library for high performance numerical computation. Allows deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.

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