Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.

<h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis...
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Autor*in:	Larisa Wewetzer [verfasserIn] Linda A Held [verfasserIn] Jost Steinhäuser [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 16(2021), 8, p e0255034
Übergeordnetes Werk:	volume:16 ; year:2021 ; number:8, p e0255034

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1371/journal.pone.0255034

Katalog-ID:	DOAJ061944084

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allfields	10.1371/journal.pone.0255034 doi (DE-627)DOAJ061944084 (DE-599)DOAJbc6fd489bd594fc7aeafbd9d7a999f11 DE-627 ger DE-627 rakwb eng Larisa Wewetzer verfasserin aut Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data. Medicine R Science Q Linda A Held verfasserin aut Jost Steinhäuser verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 16(2021), 8, p e0255034 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:16 year:2021 number:8, p e0255034 https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 kostenfrei https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/toc/1932-6203 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_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2021 8, p e0255034
spelling	10.1371/journal.pone.0255034 doi (DE-627)DOAJ061944084 (DE-599)DOAJbc6fd489bd594fc7aeafbd9d7a999f11 DE-627 ger DE-627 rakwb eng Larisa Wewetzer verfasserin aut Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data. Medicine R Science Q Linda A Held verfasserin aut Jost Steinhäuser verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 16(2021), 8, p e0255034 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:16 year:2021 number:8, p e0255034 https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 kostenfrei https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/toc/1932-6203 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_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2021 8, p e0255034
allfields_unstemmed	10.1371/journal.pone.0255034 doi (DE-627)DOAJ061944084 (DE-599)DOAJbc6fd489bd594fc7aeafbd9d7a999f11 DE-627 ger DE-627 rakwb eng Larisa Wewetzer verfasserin aut Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data. Medicine R Science Q Linda A Held verfasserin aut Jost Steinhäuser verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 16(2021), 8, p e0255034 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:16 year:2021 number:8, p e0255034 https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 kostenfrei https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/toc/1932-6203 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_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2021 8, p e0255034
allfieldsGer	10.1371/journal.pone.0255034 doi (DE-627)DOAJ061944084 (DE-599)DOAJbc6fd489bd594fc7aeafbd9d7a999f11 DE-627 ger DE-627 rakwb eng Larisa Wewetzer verfasserin aut Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data. Medicine R Science Q Linda A Held verfasserin aut Jost Steinhäuser verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 16(2021), 8, p e0255034 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:16 year:2021 number:8, p e0255034 https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 kostenfrei https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/toc/1932-6203 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_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2021 8, p e0255034
allfieldsSound	10.1371/journal.pone.0255034 doi (DE-627)DOAJ061944084 (DE-599)DOAJbc6fd489bd594fc7aeafbd9d7a999f11 DE-627 ger DE-627 rakwb eng Larisa Wewetzer verfasserin aut Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data. Medicine R Science Q Linda A Held verfasserin aut Jost Steinhäuser verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 16(2021), 8, p e0255034 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:16 year:2021 number:8, p e0255034 https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/article/bc6fd489bd594fc7aeafbd9d7a999f11 kostenfrei https://doi.org/10.1371/journal.pone.0255034 kostenfrei https://doaj.org/toc/1932-6203 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_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2021 8, p e0255034
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source	In PLoS ONE 16(2021), 8, p e0255034 volume:16 year:2021 number:8, p e0255034
sourceStr	In PLoS ONE 16(2021), 8, p e0255034 volume:16 year:2021 number:8, p e0255034
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authorswithroles_txt_mv	Larisa Wewetzer @@aut@@ Linda A Held @@aut@@ Jost Steinhäuser @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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author	Larisa Wewetzer
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topic_title	Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis
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title	Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.
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title_sort	diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-a meta-analysis
title_auth	Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.
abstract	<h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data.
abstractGer	<h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data.
abstract_unstemmed	<h4<Background</h4<Diabetic retinopathy (DR) affects 10-24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis.<h4<Purpose</h4<The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC.<h4<Data sources</h4<A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies.<h4<Study selection</h4<Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects.<h4<Data extraction</h4<The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures.<h4<Data synthesis and conclusion</h4<The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%.<h4<Limitations</h4<Selected studies showed a high variation in sample size and quality and quantity of available data.
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Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis.

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