Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients

SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Danjuan Yang [verfasserIn] Meiyan Li [verfasserIn] Weizhen Li [verfasserIn] Yunzhe Wang [verfasserIn] Lingling Niu [verfasserIn] Yang Shen [verfasserIn] Xiaoyu Zhang [verfasserIn] Bo Fu [verfasserIn] Xingtao Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3

Übergeordnetes Werk:	In: Frontiers in Medicine - Frontiers Media S.A., 2014, 9(2022)
Übergeordnetes Werk:	volume:9 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmed.2022.834281

Katalog-ID:	DOAJ064318281

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245	1	0	\|a Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
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520			\|a SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.
650		4	\|a refractive error prediction
650		4	\|a myopia
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700	0		\|a Meiyan Li \|e verfasserin \|4 aut
700	0		\|a Weizhen Li \|e verfasserin \|4 aut
700	0		\|a Yunzhe Wang \|e verfasserin \|4 aut
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allfields	10.3389/fmed.2022.834281 doi (DE-627)DOAJ064318281 (DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e DE-627 ger DE-627 rakwb eng R5-920 Danjuan Yang verfasserin aut Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General) Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Weizhen Li verfasserin aut Yunzhe Wang verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Bo Fu verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 9(2022) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:9 year:2022 https://doi.org/10.3389/fmed.2022.834281 kostenfrei https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
spelling	10.3389/fmed.2022.834281 doi (DE-627)DOAJ064318281 (DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e DE-627 ger DE-627 rakwb eng R5-920 Danjuan Yang verfasserin aut Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General) Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Weizhen Li verfasserin aut Yunzhe Wang verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Bo Fu verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 9(2022) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:9 year:2022 https://doi.org/10.3389/fmed.2022.834281 kostenfrei https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfields_unstemmed	10.3389/fmed.2022.834281 doi (DE-627)DOAJ064318281 (DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e DE-627 ger DE-627 rakwb eng R5-920 Danjuan Yang verfasserin aut Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General) Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Weizhen Li verfasserin aut Yunzhe Wang verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Bo Fu verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 9(2022) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:9 year:2022 https://doi.org/10.3389/fmed.2022.834281 kostenfrei https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsGer	10.3389/fmed.2022.834281 doi (DE-627)DOAJ064318281 (DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e DE-627 ger DE-627 rakwb eng R5-920 Danjuan Yang verfasserin aut Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General) Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Weizhen Li verfasserin aut Yunzhe Wang verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Bo Fu verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 9(2022) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:9 year:2022 https://doi.org/10.3389/fmed.2022.834281 kostenfrei https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsSound	10.3389/fmed.2022.834281 doi (DE-627)DOAJ064318281 (DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e DE-627 ger DE-627 rakwb eng R5-920 Danjuan Yang verfasserin aut Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved. refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General) Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Danjuan Yang verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Meiyan Li verfasserin aut Weizhen Li verfasserin aut Yunzhe Wang verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Lingling Niu verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Yang Shen verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Xiaoyu Zhang verfasserin aut Bo Fu verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut Xingtao Zhou verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 9(2022) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:9 year:2022 https://doi.org/10.3389/fmed.2022.834281 kostenfrei https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
language	English
source	In Frontiers in Medicine 9(2022) volume:9 year:2022
sourceStr	In Frontiers in Medicine 9(2022) volume:9 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3 Medicine (General)
isfreeaccess_bool	true
container_title	Frontiers in Medicine
authorswithroles_txt_mv	Danjuan Yang @@aut@@ Meiyan Li @@aut@@ Weizhen Li @@aut@@ Yunzhe Wang @@aut@@ Lingling Niu @@aut@@ Yang Shen @@aut@@ Xiaoyu Zhang @@aut@@ Bo Fu @@aut@@ Xingtao Zhou @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	789482991
id	DOAJ064318281
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ064318281</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309035331.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fmed.2022.834281</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ064318281</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R5-920</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Danjuan Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P &lt; 0.01) and greater spherical power(P &lt; 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">refractive error prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">myopia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrawide field imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ResNet-50</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inception-V3</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield 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callnumber-first	R - Medicine
author	Danjuan Yang
spellingShingle	Danjuan Yang misc R5-920 misc refractive error prediction misc myopia misc deep learning misc ultrawide field imaging misc ResNet-50 misc Inception-V3 misc Medicine (General) Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
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topic_title	R5-920 Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients refractive error prediction myopia deep learning ultrawide field imaging ResNet-50 Inception-V3
topic	misc R5-920 misc refractive error prediction misc myopia misc deep learning misc ultrawide field imaging misc ResNet-50 misc Inception-V3 misc Medicine (General)
topic_unstemmed	misc R5-920 misc refractive error prediction misc myopia misc deep learning misc ultrawide field imaging misc ResNet-50 misc Inception-V3 misc Medicine (General)
topic_browse	misc R5-920 misc refractive error prediction misc myopia misc deep learning misc ultrawide field imaging misc ResNet-50 misc Inception-V3 misc Medicine (General)
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title	Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
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title_full	Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
author_sort	Danjuan Yang
journal	Frontiers in Medicine
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author_browse	Danjuan Yang Meiyan Li Weizhen Li Yunzhe Wang Lingling Niu Yang Shen Xiaoyu Zhang Bo Fu Xingtao Zhou
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doi_str_mv	10.3389/fmed.2022.834281
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title_sort	prediction of refractive error based on ultrawide field images with deep learning models in myopia patients
callnumber	R5-920
title_auth	Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
abstract	SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.
abstractGer	SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.
abstract_unstemmed	SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.
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title_short	Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients
url	https://doi.org/10.3389/fmed.2022.834281 https://doaj.org/article/b6f36567d3034f13a7f106a1f5f0046e https://www.frontiersin.org/articles/10.3389/fmed.2022.834281/full https://doaj.org/toc/2296-858X
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up_date	2024-07-03T22:17:35.582Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ064318281</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309035331.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fmed.2022.834281</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ064318281</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJb6f36567d3034f13a7f106a1f5f0046e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">R5-920</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Danjuan Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P &lt; 0.01) and greater spherical power(P &lt; 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">refractive error prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">myopia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrawide field imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ResNet-50</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inception-V3</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield 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Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients

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