“A framework of biomarkers for visual system aging:a consensus statement by the Aging Biomarker Consortium”是由衰老标志物研究联合体(Aging Biomarker Consortium,ABC)制定的首个系统性评估视觉衰老的多维度指标体系。该体...“A framework of biomarkers for visual system aging:a consensus statement by the Aging Biomarker Consortium”是由衰老标志物研究联合体(Aging Biomarker Consortium,ABC)制定的首个系统性评估视觉衰老的多维度指标体系。该体系整合功能、结构和分子层面的生物标志物,旨在建立标准化工具,以早期识别衰老相关眼病风险、揭示潜在机制并指导精准干预。功能标志物反映临床表型,结构标志物提供影像学依据,分子标志物则阐释生物学机制。该框架的提出填补了视觉衰老量化评估的空白,为个体化眼健康管理和抗衰老研究提供了重要基础,但其临床应用仍需进一步验证,检测技术仍需优化。展开更多
Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with co...Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.展开更多
Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility...Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.展开更多
AIM:To investigate the association between anti-DFS70 antibody positivity and ocular parameters,specifically,the choroidal vascularity index(CVI)and other optical coherence tomography(OCT)metrics,in a healthy populati...AIM:To investigate the association between anti-DFS70 antibody positivity and ocular parameters,specifically,the choroidal vascularity index(CVI)and other optical coherence tomography(OCT)metrics,in a healthy population.METHODS:This age-and sex-matched case-control study enrolled 84 healthy individuals with positive anti-DFS70 antibody findings and 84 healthy negative controls.All participants underwent detailed ophthalmological examinations,including biometry and OCT imaging.Anti-DFS70 positivity was determined by indirect immunofluorescence and scored semi-quantitatively(1+to 3+).CVI was calculated from OCT images using a standardized protocol with Image J software.Statistical analyses,including Student’s t-test,Mann-Whitney U test,Spearman correlation,and logistic regression,were used to compare groups and identify predictive factors.RESULTS:The individuals who tested positive and negative for anti-DFS70 included in the study were matched for age(median age=47y)and sex(F:M=7:1).CVI was significantly lower in the anti-DFS70-positive group compared to the negative group.A higher anti-DFS70 antibody titer was significantly associated with decreased subfoveal and nasal choroidal thickness(P=0.016 and P=0.014,respectively).In univariate regression analysis,CVI was the only significant predictor of anti-DFS70 positivity[odds ratio(OR)=0.02,P=0.025].Multivariate analysis revealed a positive correlation between macular thinning outside the subfoveal area and anti-DFS70 status(P<0.05).CONCLUSION:Our study demonstrates a novel association between anti-DFS70 antibody positivity and reduced choroidal vascularity in healthy individuals.These findings suggest that anti-DFS70 antibodies may be associated with subtle choroidal vascular changes detectable by OCT,even in asymptomatic individuals.Further longitudinal research is warranted to clarify the underlying mechanisms and long-term clinical significance of these ocular changes.展开更多
AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angi...AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angiography(OCTA)images.METHODS:Two swept-source OCT imaging systems,VG200I and Topcon DRI OCT Triton,were used to capture OCT and OCTA images in triplicate.The first and third images were taken by one operator,and the second image was taken by another operator.The built-in software was used to calculate the CVI from the OCTA images(CVI-OCTA),and a custom-designed algorithm was used to calculate the CVI from the OCT images(CVI-OCT).Repeatability and reproducibility were assessed with the intraclass correlation coefficient(ICC),and agreement between devices and between OCT and OCTA were evaluated with Bland-Altman analysis.RESULTS:Sixty-eight eyes from 35 adults(17 females)were included in the analysis.The average age of the participants was 23.6±2.3y,with an average spherical equivalent refraction of-3.08±2.47 D and an average AL of 25.21±1.20 mm.Both OCT devices demonstrated high repeatability and reproducibility in measuring the CVI-OCTA(all ICCs>0.894 across five choroidal regions)and CVI-OCT(all ICCs>0.838).Furthermore,the between-device agreement in measuring the CVI-OCT was good[mean difference(MD)ranging from-2.32%to-3.07%],but that in measuring the CVI-OCTA was poor(MD,1.48%to-7.43%).Additionally,the between-imaging agreement(CVI-OCTA versus CVI-OCT)was poor for both devices(Triton,MD,6.05%to 12.68%;VG200I,MD,6.67%to 12.09%).CONCLUSION:Both OCT devices and the two analytical methods demonstrate good stability.The inter-device consistency of CVI-OCT is good,while the inter-device consistency of CVI-OCTA and the consistency between the two analytical methods in the same device are both poor.展开更多
文摘“A framework of biomarkers for visual system aging:a consensus statement by the Aging Biomarker Consortium”是由衰老标志物研究联合体(Aging Biomarker Consortium,ABC)制定的首个系统性评估视觉衰老的多维度指标体系。该体系整合功能、结构和分子层面的生物标志物,旨在建立标准化工具,以早期识别衰老相关眼病风险、揭示潜在机制并指导精准干预。功能标志物反映临床表型,结构标志物提供影像学依据,分子标志物则阐释生物学机制。该框架的提出填补了视觉衰老量化评估的空白,为个体化眼健康管理和抗衰老研究提供了重要基础,但其临床应用仍需进一步验证,检测技术仍需优化。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R77)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number NBU-FFR-2026-2248-01.
文摘Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.
基金supported by the National Natural Science Foundation of China(62522119 and 62372358)the Beijing Natural Science Foundation(7242267)+2 种基金the Beijing Scholars Program([2015]160)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0719)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110453)。
文摘Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.
文摘AIM:To investigate the association between anti-DFS70 antibody positivity and ocular parameters,specifically,the choroidal vascularity index(CVI)and other optical coherence tomography(OCT)metrics,in a healthy population.METHODS:This age-and sex-matched case-control study enrolled 84 healthy individuals with positive anti-DFS70 antibody findings and 84 healthy negative controls.All participants underwent detailed ophthalmological examinations,including biometry and OCT imaging.Anti-DFS70 positivity was determined by indirect immunofluorescence and scored semi-quantitatively(1+to 3+).CVI was calculated from OCT images using a standardized protocol with Image J software.Statistical analyses,including Student’s t-test,Mann-Whitney U test,Spearman correlation,and logistic regression,were used to compare groups and identify predictive factors.RESULTS:The individuals who tested positive and negative for anti-DFS70 included in the study were matched for age(median age=47y)and sex(F:M=7:1).CVI was significantly lower in the anti-DFS70-positive group compared to the negative group.A higher anti-DFS70 antibody titer was significantly associated with decreased subfoveal and nasal choroidal thickness(P=0.016 and P=0.014,respectively).In univariate regression analysis,CVI was the only significant predictor of anti-DFS70 positivity[odds ratio(OR)=0.02,P=0.025].Multivariate analysis revealed a positive correlation between macular thinning outside the subfoveal area and anti-DFS70 status(P<0.05).CONCLUSION:Our study demonstrates a novel association between anti-DFS70 antibody positivity and reduced choroidal vascularity in healthy individuals.These findings suggest that anti-DFS70 antibodies may be associated with subtle choroidal vascular changes detectable by OCT,even in asymptomatic individuals.Further longitudinal research is warranted to clarify the underlying mechanisms and long-term clinical significance of these ocular changes.
基金Supported by the National Key Research and Development Program of China(No.2022YFC3502503)the Medical and Health Science and Technology Project of the Zhejiang Provincial Health Commission of China(No.2022PY072).
文摘AIM:To explore the repeatability,reproducibility,and agreement in the measurement of the choroidal vascularity index(CVI)for different swept-source optical coherence tomography(OCT)devices and between OCT and OCT angiography(OCTA)images.METHODS:Two swept-source OCT imaging systems,VG200I and Topcon DRI OCT Triton,were used to capture OCT and OCTA images in triplicate.The first and third images were taken by one operator,and the second image was taken by another operator.The built-in software was used to calculate the CVI from the OCTA images(CVI-OCTA),and a custom-designed algorithm was used to calculate the CVI from the OCT images(CVI-OCT).Repeatability and reproducibility were assessed with the intraclass correlation coefficient(ICC),and agreement between devices and between OCT and OCTA were evaluated with Bland-Altman analysis.RESULTS:Sixty-eight eyes from 35 adults(17 females)were included in the analysis.The average age of the participants was 23.6±2.3y,with an average spherical equivalent refraction of-3.08±2.47 D and an average AL of 25.21±1.20 mm.Both OCT devices demonstrated high repeatability and reproducibility in measuring the CVI-OCTA(all ICCs>0.894 across five choroidal regions)and CVI-OCT(all ICCs>0.838).Furthermore,the between-device agreement in measuring the CVI-OCT was good[mean difference(MD)ranging from-2.32%to-3.07%],but that in measuring the CVI-OCTA was poor(MD,1.48%to-7.43%).Additionally,the between-imaging agreement(CVI-OCTA versus CVI-OCT)was poor for both devices(Triton,MD,6.05%to 12.68%;VG200I,MD,6.67%to 12.09%).CONCLUSION:Both OCT devices and the two analytical methods demonstrate good stability.The inter-device consistency of CVI-OCT is good,while the inter-device consistency of CVI-OCTA and the consistency between the two analytical methods in the same device are both poor.
