Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Liu L

PROVIDER: S-EPMC9975093 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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

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

Cell reports. Medicine 20230119 2

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 ...[more]

PMID: 36669488

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Project description:BackgroundPostoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy.MethodsVideo clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands.Results152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods.ConclusionThis AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model.

Dataset Information

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

Publications

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

Similar Datasets

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