目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集...目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记:1)识别采集模式;2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼;3)从病因和严重程度检测需转诊的白内障患者。此外,将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果:通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能:1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%);2)白内障诊断对正常晶体眼、白内障或术后眼,在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%,其他采集模式的AUC均>99%;3)需转诊白内障的检测(在所有测试中AUC>91%)。在真实世界的三级转诊模式中,该系统建议30.3%的人转诊,与传统模式相比,眼科医生与人群服务比率大幅提高了10.2倍。结论:通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。展开更多
Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,...Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.展开更多
Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit simila...Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit similar phenotypes,posing significant challenges for accurately assessing the severity of visual impairment.To address this issue,we propose a dense convolution combined with attention mechanism and multi-level classifier(DAMC_Net)for visual impairment grading.First,the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions.Then,a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment,while maintaining a better screening rate for normal samples.In addition,a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset.Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121(DenseNet121)models,with sensitivity improvements of 6.0%and 3.4%on the category of mild visual impairment caused by cataracts(MVICC),and 2.1%and 4.3%on the category of moderate to severe visual impairment caused by cataracts(MSVICC),respectively.The comparable performance on two external test datasets was achieved,further verifying the effectiveness and generalizability of the DAMC_Net.展开更多
Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions,and are the basis for detecting and treating ophthalmic diseases as well as other important diseases.This stud...Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions,and are the basis for detecting and treating ophthalmic diseases as well as other important diseases.This study proposes an automatic diagnosis method for multiple fundus lesions based on a deep graph neural network(GNN).2083 fundus images were collected and annotated to develop and evaluate the performance of the algorithm.First,high-level semantic features of fundus images are extracted using deep convolutional neural networks(CNNs).Then the features are input into the GNN to model the correlation between different lesions by mining and learning the correlation between lesions.Finally,the input and output features of the GNN are fused,and a multi-label classifier is used to complete the automatic diagnosis of fundus lesions.Experimental results show that the method proposed in this study can learn the correlations between lesions to improve the diagnostic performance of the algorithm,achieving better performance than the original Res Net and Dense Net models in both qualitative and quantitative evaluation.展开更多
文摘目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记:1)识别采集模式;2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼;3)从病因和严重程度检测需转诊的白内障患者。此外,将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果:通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能:1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%);2)白内障诊断对正常晶体眼、白内障或术后眼,在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%,其他采集模式的AUC均>99%;3)需转诊白内障的检测(在所有测试中AUC>91%)。在真实世界的三级转诊模式中,该系统建议30.3%的人转诊,与传统模式相比,眼科医生与人群服务比率大幅提高了10.2倍。结论:通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。
基金the National Natural Science Foundation of China(No.62276210,82201148,61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)+3 种基金the Shaanxi Province College Students'Innovation and Entrepreneurship Training Program(No.S202311664128X)the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(No.2022RC069,2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)。
文摘Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)。
文摘Recent advancements in artificial intelligence(AI)have shown promising potential for the automated screening and grading of cataracts.However,the different types of visual impairment caused by cataracts exhibit similar phenotypes,posing significant challenges for accurately assessing the severity of visual impairment.To address this issue,we propose a dense convolution combined with attention mechanism and multi-level classifier(DAMC_Net)for visual impairment grading.First,the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions.Then,a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment,while maintaining a better screening rate for normal samples.In addition,a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset.Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121(DenseNet121)models,with sensitivity improvements of 6.0%and 3.4%on the category of mild visual impairment caused by cataracts(MVICC),and 2.1%and 4.3%on the category of moderate to severe visual impairment caused by cataracts(MSVICC),respectively.The comparable performance on two external test datasets was achieved,further verifying the effectiveness and generalizability of the DAMC_Net.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)+2 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Xi’an University of Posts and Telecommunications Postgraduate Innovation and Entrepreneurship Fund Project(No.CXJJTL2021009)。
文摘Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions,and are the basis for detecting and treating ophthalmic diseases as well as other important diseases.This study proposes an automatic diagnosis method for multiple fundus lesions based on a deep graph neural network(GNN).2083 fundus images were collected and annotated to develop and evaluate the performance of the algorithm.First,high-level semantic features of fundus images are extracted using deep convolutional neural networks(CNNs).Then the features are input into the GNN to model the correlation between different lesions by mining and learning the correlation between lesions.Finally,the input and output features of the GNN are fused,and a multi-label classifier is used to complete the automatic diagnosis of fundus lesions.Experimental results show that the method proposed in this study can learn the correlations between lesions to improve the diagnostic performance of the algorithm,achieving better performance than the original Res Net and Dense Net models in both qualitative and quantitative evaluation.
