Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion
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摘要
AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models. RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P<0.001). CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.
AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models.RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P<0.001).CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.
AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models.RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P<0.001).CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.
引文
1 Rogers S,McIntosh RL,Cheung N,Lim L,Wang JJ,Mitchell P,Kowalski JW,Nguyen H,Wong TY;International Eye Disease Consortium.The prevalence of retinal vein occlusion:pooled data from population studies from the United States,Europe,Asia,and Australia.Ophthalmology 2010;117(2):313-319.
2 Bunce C,Xing W,Wormald R.Causes of blind and partial sight certifications in England and Wales:April 2007-March 2008.Eye(Lond)2010;24(11):1692-1699.
3 Kornhauser T,Schwartz R,Goldstein M,Neudorfer M,Loewenstein A,Barak A.Bevacizumab treatment of macular edema in CRVO and BRVO:long-term follow-up.(BERVOLT study:Bevacizumab for RVO long-term follow-up).Graefes Arch Clin Exp Ophthalmol 2016;254(5):835-844.
4 Ehlers JP,Kim SJ,Yeh S,Thorne JE,Mruthyunjaya P,Schoenberger SD,Bakri SJ.Therapies for macular edema associated with branch retinal vein occlusion:a report by the American Academy of Ophthalmology.Ophthalmology 2017;124(9):1412-1423.
5 Clark WL,Boyer DS,Heier JS,Brown DM,Haller JA,Vitti R,Kazmi H,Berliner AJ,Erickson K,Chu KW,Soo Y,Cheng Y,Campochiaro PA.Intravitreal aflibercept for macular edema following branch retinal vein occlusion:52-week results of the VIBRANT study.Ophthalmology2016;123(2):330-336.
6 Brown DM,Campochiaro PA,Bhisitkul RB,Ho AC,Gray S,Saroj N,Adamis AP,Rubio RG,Murahashi WY.Sustained benefits from ranibizumab for macular edema following branch retinal vein occlusion:12-month outcomes of a phase III study.Ophthalmology 2011;118(8):1594-1602.
7 Mrsnik M.Global aging 2013:rising to the challenge.Standard&Poor’s ratings services Network.Available at:https://www.nact.org/resources/2013_NACT_Global_Aging.pdf 2013.
8 Nagiel A,Lalane RA,Sadda SR,Schwartz SD.Ultra-wide field fundus imaging:a review of clinical applications and future trends.Retina2016;36(4):660-678.
9 LeCun Y,Bengio Y,Hinton G.Deep learning.Nature 2015;521(7553):436-444.
10 Liu S,Liu S,Cai W,Che H,Pujol S,Kikinis R,Feng D,Fulham MJ,ADNI.Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease.IEEE Trans Biomed Eng 2015;62(4):1132-1140.
11 Litjens G,Sánchez CI,Timofeeva N,Hermsen M,Nagtegaal I,Kovacs I,Hulsbergen-van de Kaa C,Bult P,van Ginneken B,van der Laak J.Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.Sci Rep 2016;6:26286.
12 Gulshan V,Peng L,Coram M,Stumpe MC,Wu D,Narayanaswamy A,Venugopalan S,Widner K,Madams T,Cuadros J,Kim R,Raman R,Nelson PC,Mega JL,Webster DR.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA 2016;316(22):2402.
13 Pinaya WHL,Gadelha A,Doyle OM,Noto C,Zugman A,Cordeiro Q,Jackowski AP,Bressan RA,Sato JR.Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.Sci Rep 2016;6:38897.
14 Gargeya R,Leng T.Automated identification of diabetic retinopathy using deep learning.Ophthalmology 2017;124(7):962-969.
15 Ohsugi H,Tabuchi H,Enno H,Ishitobi N.Accuracy of deep learning,a machine-learning technology,using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.Sci Rep 2017;7:9425.
16 Mosteller F,Tukey JW.Data analysis,including statistics.Lindzey G,Aronson E,eds,Handbook of Social Psychology,Vol 2,Addison:Wesley;1968.
17 Kohavi R.A study of cross-validation and bootstrap for accuracy estimation and model selection.Proceedings of International Joint Conference on AI.1995;1137-1145.
18 Maslove DM,Podchiyska T,Lowe HJ.Discretization of continuous features in clinical datasets.J Am Med Inform Assoc 2013;20(3):544-553.
19 Sturges HA.The choice of a class interval.Journal of the American Statistical Association 1926;21(153):65-66.
20 Akobeng AK.Understanding diagnostic tests 3:receiver operating characteristic curves.Acta Paediatrica 2007;96(5):644-647.
21 Deng J,Dong W,Socher R,Li LJ,Li K,Li FF.Imagenet:a large-scale hierarchical image database.Computer Vision and Pattern Recognition2009;248-255.
22 Russakovsky O,Deng J,Su H,Krause J,Satheesh S,Ma S,Huang Z,Karpathy A,Khosla A,Bernstein M,Berg AC,Fei-Fei L.Imagenet large scale visual recognition challenge.Int J Comput Vision 2015;115(3):211-252.
23 Lee CY,Xie S,Gallagher P,Zhang Z,Tu Z.Deeply-supervised nets.In AISTATS 2015;2:5.
24 Scherer D,Andreas M,Sven B.Evaluation of pooling operations in convolutional architectures for object recognition.Artificial Neural Networks 2010;92-101.
25 Redmon J,Divvala S,Girshick R,Farhadi A.You only look once:unified,real-time object detection.2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)27-30 June 2016,Las Vegas,NV,USA,2016:779-788.
26 Agrawal P,Girshick R,Malik J.Analyzing the performance of multilayer neural networks for object recognition.European Conference on Computer Vision 2014;329-344.
27 Qian N.On the momentum term in gradient descent learning algorithms.Neural Networks 1999;12(1):145-151.
28 Nesterov Y.A method for unconstrained convex minimization problem with the rate of convergence O(1/k^2).Doklady AN USSR1983;269(27):543-547.
29 Brereton RG,Lloyd GR.Support Vector Machines for classification and regression.The Analyst 2010;135(2):230-267.
30 Akobeng AK.Understanding diagnostic tests 3:receiver operating characteristic curves.Acta Paediatrica 2007;96(5):644-647.
31 Schisterman EF,Faraggi D,Reiser B,Hu J.Youden Index and the optimal threshold for markers with mass at zero.Stat Med 2008;27(2):297-315.
32 Wang HK,Zhou ZW,Li YC,Chen ZH,Lu PO,Wang WZ,Liu WY,Yu LJ.Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.EJNMMI Research 2017;7:11.
33 Maa AY,Wojciechowski B,Hunt KJ,Dismuke C,Shyu J,Janjua R,Lu XQ,Medert CM,Lynch MG.Early experience with technology-based eye care services(TECS)a novel ophthalmologic Telemedicine initiative.Ophthalmology 2017;124(4):539-546.
2 Bunce C,Xing W,Wormald R.Causes of blind and partial sight certifications in England and Wales:April 2007-March 2008.Eye(Lond)2010;24(11):1692-1699.
3 Kornhauser T,Schwartz R,Goldstein M,Neudorfer M,Loewenstein A,Barak A.Bevacizumab treatment of macular edema in CRVO and BRVO:long-term follow-up.(BERVOLT study:Bevacizumab for RVO long-term follow-up).Graefes Arch Clin Exp Ophthalmol 2016;254(5):835-844.
4 Ehlers JP,Kim SJ,Yeh S,Thorne JE,Mruthyunjaya P,Schoenberger SD,Bakri SJ.Therapies for macular edema associated with branch retinal vein occlusion:a report by the American Academy of Ophthalmology.Ophthalmology 2017;124(9):1412-1423.
5 Clark WL,Boyer DS,Heier JS,Brown DM,Haller JA,Vitti R,Kazmi H,Berliner AJ,Erickson K,Chu KW,Soo Y,Cheng Y,Campochiaro PA.Intravitreal aflibercept for macular edema following branch retinal vein occlusion:52-week results of the VIBRANT study.Ophthalmology2016;123(2):330-336.
6 Brown DM,Campochiaro PA,Bhisitkul RB,Ho AC,Gray S,Saroj N,Adamis AP,Rubio RG,Murahashi WY.Sustained benefits from ranibizumab for macular edema following branch retinal vein occlusion:12-month outcomes of a phase III study.Ophthalmology 2011;118(8):1594-1602.
7 Mrsnik M.Global aging 2013:rising to the challenge.Standard&Poor’s ratings services Network.Available at:https://www.nact.org/resources/2013_NACT_Global_Aging.pdf 2013.
8 Nagiel A,Lalane RA,Sadda SR,Schwartz SD.Ultra-wide field fundus imaging:a review of clinical applications and future trends.Retina2016;36(4):660-678.
9 LeCun Y,Bengio Y,Hinton G.Deep learning.Nature 2015;521(7553):436-444.
10 Liu S,Liu S,Cai W,Che H,Pujol S,Kikinis R,Feng D,Fulham MJ,ADNI.Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease.IEEE Trans Biomed Eng 2015;62(4):1132-1140.
11 Litjens G,Sánchez CI,Timofeeva N,Hermsen M,Nagtegaal I,Kovacs I,Hulsbergen-van de Kaa C,Bult P,van Ginneken B,van der Laak J.Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.Sci Rep 2016;6:26286.
12 Gulshan V,Peng L,Coram M,Stumpe MC,Wu D,Narayanaswamy A,Venugopalan S,Widner K,Madams T,Cuadros J,Kim R,Raman R,Nelson PC,Mega JL,Webster DR.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA 2016;316(22):2402.
13 Pinaya WHL,Gadelha A,Doyle OM,Noto C,Zugman A,Cordeiro Q,Jackowski AP,Bressan RA,Sato JR.Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.Sci Rep 2016;6:38897.
14 Gargeya R,Leng T.Automated identification of diabetic retinopathy using deep learning.Ophthalmology 2017;124(7):962-969.
15 Ohsugi H,Tabuchi H,Enno H,Ishitobi N.Accuracy of deep learning,a machine-learning technology,using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.Sci Rep 2017;7:9425.
16 Mosteller F,Tukey JW.Data analysis,including statistics.Lindzey G,Aronson E,eds,Handbook of Social Psychology,Vol 2,Addison:Wesley;1968.
17 Kohavi R.A study of cross-validation and bootstrap for accuracy estimation and model selection.Proceedings of International Joint Conference on AI.1995;1137-1145.
18 Maslove DM,Podchiyska T,Lowe HJ.Discretization of continuous features in clinical datasets.J Am Med Inform Assoc 2013;20(3):544-553.
19 Sturges HA.The choice of a class interval.Journal of the American Statistical Association 1926;21(153):65-66.
20 Akobeng AK.Understanding diagnostic tests 3:receiver operating characteristic curves.Acta Paediatrica 2007;96(5):644-647.
21 Deng J,Dong W,Socher R,Li LJ,Li K,Li FF.Imagenet:a large-scale hierarchical image database.Computer Vision and Pattern Recognition2009;248-255.
22 Russakovsky O,Deng J,Su H,Krause J,Satheesh S,Ma S,Huang Z,Karpathy A,Khosla A,Bernstein M,Berg AC,Fei-Fei L.Imagenet large scale visual recognition challenge.Int J Comput Vision 2015;115(3):211-252.
23 Lee CY,Xie S,Gallagher P,Zhang Z,Tu Z.Deeply-supervised nets.In AISTATS 2015;2:5.
24 Scherer D,Andreas M,Sven B.Evaluation of pooling operations in convolutional architectures for object recognition.Artificial Neural Networks 2010;92-101.
25 Redmon J,Divvala S,Girshick R,Farhadi A.You only look once:unified,real-time object detection.2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)27-30 June 2016,Las Vegas,NV,USA,2016:779-788.
26 Agrawal P,Girshick R,Malik J.Analyzing the performance of multilayer neural networks for object recognition.European Conference on Computer Vision 2014;329-344.
27 Qian N.On the momentum term in gradient descent learning algorithms.Neural Networks 1999;12(1):145-151.
28 Nesterov Y.A method for unconstrained convex minimization problem with the rate of convergence O(1/k^2).Doklady AN USSR1983;269(27):543-547.
29 Brereton RG,Lloyd GR.Support Vector Machines for classification and regression.The Analyst 2010;135(2):230-267.
30 Akobeng AK.Understanding diagnostic tests 3:receiver operating characteristic curves.Acta Paediatrica 2007;96(5):644-647.
31 Schisterman EF,Faraggi D,Reiser B,Hu J.Youden Index and the optimal threshold for markers with mass at zero.Stat Med 2008;27(2):297-315.
32 Wang HK,Zhou ZW,Li YC,Chen ZH,Lu PO,Wang WZ,Liu WY,Yu LJ.Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.EJNMMI Research 2017;7:11.
33 Maa AY,Wojciechowski B,Hunt KJ,Dismuke C,Shyu J,Janjua R,Lu XQ,Medert CM,Lynch MG.Early experience with technology-based eye care services(TECS)a novel ophthalmologic Telemedicine initiative.Ophthalmology 2017;124(4):539-546.