AIM: To explore the performance in diabetic retinopathy(DR) screening of artificial intelligence(AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional table...AIM: To explore the performance in diabetic retinopathy(DR) screening of artificial intelligence(AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. METHODS: Overall, 630 eyes were included from three centers and screened by a handheld camera(Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR.RESULTS: Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range(1.47, 1.48, and 1.40) than conventional tabletop cameras(1.30, 1.28, and 1.18;P<0.001). Detection of retinal hemorrhage, hard exudation,and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1%(95%CI: 72.1%-92.2%) and 97.4%(95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory;however, Phoebus showed a high sensitivity(88.2%, 95%CI: 79.4%-97.1%) and low specificity(40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR.CONCLUSION: The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.展开更多
Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combin...Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combining two mirror-symmetric generative adversarial networks(GANs)for image enhancement.Methods:A total of 1047 retinal images were included.The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment.All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists.The quality classification and quality change of images were compared.In addition,imagedetailed reading results for the number of dubiously pathological fundi were also compared.Results:After GAN enhancement,42.9% of images increased their quality,37.5%remained stable,and 19.6%decreased.After excluding the images at the highest level(level 0)before enhancement,a large number(75.6%)of images showed an increase in quality classification,and only a minority(9.3%)showed a decrease.The GANenhanced method was superior for quality improvement over a luminosity and contrast adjustment method(P<0.001).In terms of image reading results,the consistency rate fluctuated from 86.6%to 95.6%,and for the specific disease subtypes,both discrepancy number and discrepancy rate were less than 15 and 15%,for two ophthalmologists.Conclusions:Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.展开更多
Remote sensing satellites are playing very important roles in diverse earth observation fields.However,long revisit period,high cost and dense cloud cover have been the main limitations of satellite remote sensing for...Remote sensing satellites are playing very important roles in diverse earth observation fields.However,long revisit period,high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time.This paper introduces the novel volunteered passenger aircraft remote sensing(VPARS)concept,which can partly overcome these problems.By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft,it has various advantages including low cost,high revisit,dense coverage,and partial anti-cloud,which can well complement conventional remote sensing data.This paper examines the concept of VPARS and give general data processing framework of VPARS.Several cases were given to validate this processing approach.Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data.Furthermore,we examine the issues about data maintenance,potential applications,limitations and challenges.We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.展开更多
基金Supported by the National Natural Science Foundation of China(No.81970845)European Union’s Horizon 2020 research and innovation programme under grant agreement(No.778089)。
文摘AIM: To explore the performance in diabetic retinopathy(DR) screening of artificial intelligence(AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. METHODS: Overall, 630 eyes were included from three centers and screened by a handheld camera(Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR.RESULTS: Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range(1.47, 1.48, and 1.40) than conventional tabletop cameras(1.30, 1.28, and 1.18;P<0.001). Detection of retinal hemorrhage, hard exudation,and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1%(95%CI: 72.1%-92.2%) and 97.4%(95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory;however, Phoebus showed a high sensitivity(88.2%, 95%CI: 79.4%-97.1%) and low specificity(40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR.CONCLUSION: The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.
文摘Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combining two mirror-symmetric generative adversarial networks(GANs)for image enhancement.Methods:A total of 1047 retinal images were included.The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment.All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists.The quality classification and quality change of images were compared.In addition,imagedetailed reading results for the number of dubiously pathological fundi were also compared.Results:After GAN enhancement,42.9% of images increased their quality,37.5%remained stable,and 19.6%decreased.After excluding the images at the highest level(level 0)before enhancement,a large number(75.6%)of images showed an increase in quality classification,and only a minority(9.3%)showed a decrease.The GANenhanced method was superior for quality improvement over a luminosity and contrast adjustment method(P<0.001).In terms of image reading results,the consistency rate fluctuated from 86.6%to 95.6%,and for the specific disease subtypes,both discrepancy number and discrepancy rate were less than 15 and 15%,for two ophthalmologists.Conclusions:Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.
基金supported by National Natural Science Foundation of China(41974006)Shenzhen Scientific Research and Development Funding Program(KQJSCX20180328093453763,JCYJ20180305125101282,JCYJ20170412142239369)+1 种基金Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation(KF-2018-03-004)Department of Education of Guangdong Province(2018KTSCX196).
文摘Remote sensing satellites are playing very important roles in diverse earth observation fields.However,long revisit period,high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time.This paper introduces the novel volunteered passenger aircraft remote sensing(VPARS)concept,which can partly overcome these problems.By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft,it has various advantages including low cost,high revisit,dense coverage,and partial anti-cloud,which can well complement conventional remote sensing data.This paper examines the concept of VPARS and give general data processing framework of VPARS.Several cases were given to validate this processing approach.Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data.Furthermore,we examine the issues about data maintenance,potential applications,limitations and challenges.We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.
