Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation

Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colono...
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Autor*in:	Yamada, Masayoshi [verfasserIn] Shino, Ryosaku Kondo, Hiroko Yamada, Shigemi Takamaru, Hiroyuki Sakamoto, Taku Bhandari, Pradeep Imaoka, Hitoshi Kuchiba, Aya Shibata, Taro Saito, Yutaka Hamamoto, Ryuji

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Deep learning Artificial intelligence Colonoscopy Multi-class classification External validation

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Journal of gastroenterology - Tokyo : Springer, 1994, 57(2022), 11 vom: 16. Aug., Seite 879-889
Übergeordnetes Werk:	volume:57 ; year:2022 ; number:11 ; day:16 ; month:08 ; pages:879-889

Links:	Volltext

DOI / URN:	10.1007/s00535-022-01908-1

Katalog-ID:	SPR048445959

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100	1		\|a Yamada, Masayoshi \|e verfasserin \|0 (orcid)0000-0003-3979-5560 \|4 aut
245	1	0	\|a Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
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520			\|a Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words).
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650		4	\|a Multi-class classification \|7 (dpeaa)DE-He213
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700	1		\|a Saito, Yutaka \|4 aut
700	1		\|a Hamamoto, Ryuji \|4 aut
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allfields	10.1007/s00535-022-01908-1 doi (DE-627)SPR048445959 (SPR)s00535-022-01908-1-e DE-627 ger DE-627 rakwb eng Yamada, Masayoshi verfasserin (orcid)0000-0003-3979-5560 aut Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213 Shino, Ryosaku aut Kondo, Hiroko aut Yamada, Shigemi aut Takamaru, Hiroyuki aut Sakamoto, Taku aut Bhandari, Pradeep aut Imaoka, Hitoshi aut Kuchiba, Aya aut Shibata, Taro aut Saito, Yutaka aut Hamamoto, Ryuji aut Enthalten in Journal of gastroenterology Tokyo : Springer, 1994 57(2022), 11 vom: 16. Aug., Seite 879-889 (DE-627)268761671 (DE-600)1473159-9 1435-5922 nnns volume:57 year:2022 number:11 day:16 month:08 pages:879-889 https://dx.doi.org/10.1007/s00535-022-01908-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2022 11 16 08 879-889
spelling	10.1007/s00535-022-01908-1 doi (DE-627)SPR048445959 (SPR)s00535-022-01908-1-e DE-627 ger DE-627 rakwb eng Yamada, Masayoshi verfasserin (orcid)0000-0003-3979-5560 aut Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213 Shino, Ryosaku aut Kondo, Hiroko aut Yamada, Shigemi aut Takamaru, Hiroyuki aut Sakamoto, Taku aut Bhandari, Pradeep aut Imaoka, Hitoshi aut Kuchiba, Aya aut Shibata, Taro aut Saito, Yutaka aut Hamamoto, Ryuji aut Enthalten in Journal of gastroenterology Tokyo : Springer, 1994 57(2022), 11 vom: 16. Aug., Seite 879-889 (DE-627)268761671 (DE-600)1473159-9 1435-5922 nnns volume:57 year:2022 number:11 day:16 month:08 pages:879-889 https://dx.doi.org/10.1007/s00535-022-01908-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2022 11 16 08 879-889
allfields_unstemmed	10.1007/s00535-022-01908-1 doi (DE-627)SPR048445959 (SPR)s00535-022-01908-1-e DE-627 ger DE-627 rakwb eng Yamada, Masayoshi verfasserin (orcid)0000-0003-3979-5560 aut Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213 Shino, Ryosaku aut Kondo, Hiroko aut Yamada, Shigemi aut Takamaru, Hiroyuki aut Sakamoto, Taku aut Bhandari, Pradeep aut Imaoka, Hitoshi aut Kuchiba, Aya aut Shibata, Taro aut Saito, Yutaka aut Hamamoto, Ryuji aut Enthalten in Journal of gastroenterology Tokyo : Springer, 1994 57(2022), 11 vom: 16. Aug., Seite 879-889 (DE-627)268761671 (DE-600)1473159-9 1435-5922 nnns volume:57 year:2022 number:11 day:16 month:08 pages:879-889 https://dx.doi.org/10.1007/s00535-022-01908-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2022 11 16 08 879-889
allfieldsGer	10.1007/s00535-022-01908-1 doi (DE-627)SPR048445959 (SPR)s00535-022-01908-1-e DE-627 ger DE-627 rakwb eng Yamada, Masayoshi verfasserin (orcid)0000-0003-3979-5560 aut Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213 Shino, Ryosaku aut Kondo, Hiroko aut Yamada, Shigemi aut Takamaru, Hiroyuki aut Sakamoto, Taku aut Bhandari, Pradeep aut Imaoka, Hitoshi aut Kuchiba, Aya aut Shibata, Taro aut Saito, Yutaka aut Hamamoto, Ryuji aut Enthalten in Journal of gastroenterology Tokyo : Springer, 1994 57(2022), 11 vom: 16. Aug., Seite 879-889 (DE-627)268761671 (DE-600)1473159-9 1435-5922 nnns volume:57 year:2022 number:11 day:16 month:08 pages:879-889 https://dx.doi.org/10.1007/s00535-022-01908-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2022 11 16 08 879-889
allfieldsSound	10.1007/s00535-022-01908-1 doi (DE-627)SPR048445959 (SPR)s00535-022-01908-1-e DE-627 ger DE-627 rakwb eng Yamada, Masayoshi verfasserin (orcid)0000-0003-3979-5560 aut Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213 Shino, Ryosaku aut Kondo, Hiroko aut Yamada, Shigemi aut Takamaru, Hiroyuki aut Sakamoto, Taku aut Bhandari, Pradeep aut Imaoka, Hitoshi aut Kuchiba, Aya aut Shibata, Taro aut Saito, Yutaka aut Hamamoto, Ryuji aut Enthalten in Journal of gastroenterology Tokyo : Springer, 1994 57(2022), 11 vom: 16. Aug., Seite 879-889 (DE-627)268761671 (DE-600)1473159-9 1435-5922 nnns volume:57 year:2022 number:11 day:16 month:08 pages:879-889 https://dx.doi.org/10.1007/s00535-022-01908-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2022 11 16 08 879-889
language	English
source	Enthalten in Journal of gastroenterology 57(2022), 11 vom: 16. Aug., Seite 879-889 volume:57 year:2022 number:11 day:16 month:08 pages:879-889
sourceStr	Enthalten in Journal of gastroenterology 57(2022), 11 vom: 16. Aug., Seite 879-889 volume:57 year:2022 number:11 day:16 month:08 pages:879-889
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep learning Artificial intelligence Colonoscopy Multi-class classification External validation
isfreeaccess_bool	true
container_title	Journal of gastroenterology
authorswithroles_txt_mv	Yamada, Masayoshi @@aut@@ Shino, Ryosaku @@aut@@ Kondo, Hiroko @@aut@@ Yamada, Shigemi @@aut@@ Takamaru, Hiroyuki @@aut@@ Sakamoto, Taku @@aut@@ Bhandari, Pradeep @@aut@@ Imaoka, Hitoshi @@aut@@ Kuchiba, Aya @@aut@@ Shibata, Taro @@aut@@ Saito, Yutaka @@aut@@ Hamamoto, Ryuji @@aut@@
publishDateDaySort_date	2022-08-16T00:00:00Z
hierarchy_top_id	268761671
id	SPR048445959
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">SPR048445959</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509114536.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221026s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00535-022-01908-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048445959</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00535-022-01908-1-e</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="100" ind1="1" ind2=" "><subfield code="a">Yamada, Masayoshi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3979-5560</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. 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author	Yamada, Masayoshi
spellingShingle	Yamada, Masayoshi misc Deep learning misc Artificial intelligence misc Colonoscopy misc Multi-class classification misc External validation Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
authorStr	Yamada, Masayoshi
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topic_title	Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation Deep learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Colonoscopy (dpeaa)DE-He213 Multi-class classification (dpeaa)DE-He213 External validation (dpeaa)DE-He213
topic	misc Deep learning misc Artificial intelligence misc Colonoscopy misc Multi-class classification misc External validation
topic_unstemmed	misc Deep learning misc Artificial intelligence misc Colonoscopy misc Multi-class classification misc External validation
topic_browse	misc Deep learning misc Artificial intelligence misc Colonoscopy misc Multi-class classification misc External validation
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title	Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
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title_full	Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
author_sort	Yamada, Masayoshi
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author_browse	Yamada, Masayoshi Shino, Ryosaku Kondo, Hiroko Yamada, Shigemi Takamaru, Hiroyuki Sakamoto, Taku Bhandari, Pradeep Imaoka, Hitoshi Kuchiba, Aya Shibata, Taro Saito, Yutaka Hamamoto, Ryuji
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author-letter	Yamada, Masayoshi
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title_sort	robust automated prediction of the revised vienna classification in colonoscopy using deep learning: development and initial external validation
title_auth	Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
abstract	Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). © The Author(s) 2022
abstractGer	Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). © The Author(s) 2022
abstract_unstemmed	Background Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. (229/250 words). © The Author(s) 2022
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container_issue	11
title_short	Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation
url	https://dx.doi.org/10.1007/s00535-022-01908-1
remote_bool	true
author2	Shino, Ryosaku Kondo, Hiroko Yamada, Shigemi Takamaru, Hiroyuki Sakamoto, Taku Bhandari, Pradeep Imaoka, Hitoshi Kuchiba, Aya Shibata, Taro Saito, Yutaka Hamamoto, Ryuji
author2Str	Shino, Ryosaku Kondo, Hiroko Yamada, Shigemi Takamaru, Hiroyuki Sakamoto, Taku Bhandari, Pradeep Imaoka, Hitoshi Kuchiba, Aya Shibata, Taro Saito, Yutaka Hamamoto, Ryuji
ppnlink	268761671
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hochschulschrift_bool	false
doi_str	10.1007/s00535-022-01908-1
up_date	2024-07-03T19:15:42.043Z
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Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images. Methods We prepared deep learning algorithms and colonoscopy images containing pathologically proven lesions (56,872 images, 6775 lesions). Four classifications were adopted: revised Vienna Classification category 1, 3, and 4/5 and normal images. The best algorithm—ResNet152—in the independent internal validation (14,048 images, 1718 lesions) was used for external validation (255 images, 128 lesions) based on neoplastic and non-neoplastic classification. Diagnostic performance of endoscopists was compared using a computer-assisted interpreting test. Results In the internal validation, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for adenoma (category 3) of 84.6% (95% CI 83.5–85.6%), 99.7% (99.5–99.8%), 90.8% (89.9–91.7%), 89.2% (88.5–99.0%), and 89.8% (89.3–90.4%), respectively. In the external validation, ResNet152’s sensitivity, specificity, PPV, NPV, and accuracy for neoplastic lesions were 88.3% (82.6–94.1%), 90.3% (83.0–97.7%), 94.6% (90.5–98.8%), 80.0% (70.6–89.4%), and 89.0% (84.5–93.6%), respectively. This diagnostic performance was superior to that of expert endoscopists. Area under the receiver-operating characteristic curve was 0.903 (0.860–0.946). Conclusions The developed AI system can help non-expert endoscopists make differential diagnoses of colorectal neoplasia on par with expert endoscopists during colonoscopy. 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Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation

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