An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions

ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thy...
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Autor*in:	Dong Xu [verfasserIn] Yuan Wang [verfasserIn] Hao Wu [verfasserIn] Wenliang Lu [verfasserIn] Wanru Chang [verfasserIn] Jincao Yao [verfasserIn] Meiying Yan [verfasserIn] Chanjuan Peng [verfasserIn] Chen Yang [verfasserIn] Liping Wang [verfasserIn] Lei Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence

Übergeordnetes Werk:	In: Frontiers in Endocrinology - Frontiers Media S.A., 2011, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fendo.2022.981403

Katalog-ID:	DOAJ079614558

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520			\|a ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.
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allfields	10.3389/fendo.2022.981403 doi (DE-627)DOAJ079614558 (DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf DE-627 ger DE-627 rakwb eng RC648-665 Dong Xu verfasserin aut An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features. thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence Diseases of the endocrine glands. Clinical endocrinology Dong Xu verfasserin aut Dong Xu verfasserin aut Dong Xu verfasserin aut Yuan Wang verfasserin aut Hao Wu verfasserin aut Wenliang Lu verfasserin aut Wanru Chang verfasserin aut Jincao Yao verfasserin aut Meiying Yan verfasserin aut Chanjuan Peng verfasserin aut Chen Yang verfasserin aut Liping Wang verfasserin aut Liping Wang verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut In Frontiers in Endocrinology Frontiers Media S.A., 2011 13(2022) (DE-627)645090948 (DE-600)2592084-4 16642392 nnns volume:13 year:2022 https://doi.org/10.3389/fendo.2022.981403 kostenfrei https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf kostenfrei https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full kostenfrei https://doaj.org/toc/1664-2392 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2022
spelling	10.3389/fendo.2022.981403 doi (DE-627)DOAJ079614558 (DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf DE-627 ger DE-627 rakwb eng RC648-665 Dong Xu verfasserin aut An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features. thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence Diseases of the endocrine glands. Clinical endocrinology Dong Xu verfasserin aut Dong Xu verfasserin aut Dong Xu verfasserin aut Yuan Wang verfasserin aut Hao Wu verfasserin aut Wenliang Lu verfasserin aut Wanru Chang verfasserin aut Jincao Yao verfasserin aut Meiying Yan verfasserin aut Chanjuan Peng verfasserin aut Chen Yang verfasserin aut Liping Wang verfasserin aut Liping Wang verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut In Frontiers in Endocrinology Frontiers Media S.A., 2011 13(2022) (DE-627)645090948 (DE-600)2592084-4 16642392 nnns volume:13 year:2022 https://doi.org/10.3389/fendo.2022.981403 kostenfrei https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf kostenfrei https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full kostenfrei https://doaj.org/toc/1664-2392 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2022
allfields_unstemmed	10.3389/fendo.2022.981403 doi (DE-627)DOAJ079614558 (DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf DE-627 ger DE-627 rakwb eng RC648-665 Dong Xu verfasserin aut An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features. thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence Diseases of the endocrine glands. Clinical endocrinology Dong Xu verfasserin aut Dong Xu verfasserin aut Dong Xu verfasserin aut Yuan Wang verfasserin aut Hao Wu verfasserin aut Wenliang Lu verfasserin aut Wanru Chang verfasserin aut Jincao Yao verfasserin aut Meiying Yan verfasserin aut Chanjuan Peng verfasserin aut Chen Yang verfasserin aut Liping Wang verfasserin aut Liping Wang verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut In Frontiers in Endocrinology Frontiers Media S.A., 2011 13(2022) (DE-627)645090948 (DE-600)2592084-4 16642392 nnns volume:13 year:2022 https://doi.org/10.3389/fendo.2022.981403 kostenfrei https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf kostenfrei https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full kostenfrei https://doaj.org/toc/1664-2392 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2022
allfieldsGer	10.3389/fendo.2022.981403 doi (DE-627)DOAJ079614558 (DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf DE-627 ger DE-627 rakwb eng RC648-665 Dong Xu verfasserin aut An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features. thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence Diseases of the endocrine glands. Clinical endocrinology Dong Xu verfasserin aut Dong Xu verfasserin aut Dong Xu verfasserin aut Yuan Wang verfasserin aut Hao Wu verfasserin aut Wenliang Lu verfasserin aut Wanru Chang verfasserin aut Jincao Yao verfasserin aut Meiying Yan verfasserin aut Chanjuan Peng verfasserin aut Chen Yang verfasserin aut Liping Wang verfasserin aut Liping Wang verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut In Frontiers in Endocrinology Frontiers Media S.A., 2011 13(2022) (DE-627)645090948 (DE-600)2592084-4 16642392 nnns volume:13 year:2022 https://doi.org/10.3389/fendo.2022.981403 kostenfrei https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf kostenfrei https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full kostenfrei https://doaj.org/toc/1664-2392 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2022
allfieldsSound	10.3389/fendo.2022.981403 doi (DE-627)DOAJ079614558 (DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf DE-627 ger DE-627 rakwb eng RC648-665 Dong Xu verfasserin aut An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features. thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence Diseases of the endocrine glands. Clinical endocrinology Dong Xu verfasserin aut Dong Xu verfasserin aut Dong Xu verfasserin aut Yuan Wang verfasserin aut Hao Wu verfasserin aut Wenliang Lu verfasserin aut Wanru Chang verfasserin aut Jincao Yao verfasserin aut Meiying Yan verfasserin aut Chanjuan Peng verfasserin aut Chen Yang verfasserin aut Liping Wang verfasserin aut Liping Wang verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut Lei Xu verfasserin aut In Frontiers in Endocrinology Frontiers Media S.A., 2011 13(2022) (DE-627)645090948 (DE-600)2592084-4 16642392 nnns volume:13 year:2022 https://doi.org/10.3389/fendo.2022.981403 kostenfrei https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf kostenfrei https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full kostenfrei https://doaj.org/toc/1664-2392 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 13 2022
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container_title	Frontiers in Endocrinology
authorswithroles_txt_mv	Dong Xu @@aut@@ Yuan Wang @@aut@@ Hao Wu @@aut@@ Wenliang Lu @@aut@@ Wanru Chang @@aut@@ Jincao Yao @@aut@@ Meiying Yan @@aut@@ Chanjuan Peng @@aut@@ Chen Yang @@aut@@ Liping Wang @@aut@@ Lei Xu @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">thyroid adenomas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adenocarcinomas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">follicular</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrasonography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the endocrine glands. 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callnumber-first	R - Medicine
author	Dong Xu
spellingShingle	Dong Xu misc RC648-665 misc thyroid adenomas misc adenocarcinomas misc follicular misc ultrasonography misc artificial intelligence misc Diseases of the endocrine glands. Clinical endocrinology An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
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topic_title	RC648-665 An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions thyroid adenomas adenocarcinomas follicular ultrasonography artificial intelligence
topic	misc RC648-665 misc thyroid adenomas misc adenocarcinomas misc follicular misc ultrasonography misc artificial intelligence misc Diseases of the endocrine glands. Clinical endocrinology
topic_unstemmed	misc RC648-665 misc thyroid adenomas misc adenocarcinomas misc follicular misc ultrasonography misc artificial intelligence misc Diseases of the endocrine glands. Clinical endocrinology
topic_browse	misc RC648-665 misc thyroid adenomas misc adenocarcinomas misc follicular misc ultrasonography misc artificial intelligence misc Diseases of the endocrine glands. Clinical endocrinology
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title	An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
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title_full	An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
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author_browse	Dong Xu Yuan Wang Hao Wu Wenliang Lu Wanru Chang Jincao Yao Meiying Yan Chanjuan Peng Chen Yang Liping Wang Lei Xu
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title_sort	artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
callnumber	RC648-665
title_auth	An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
abstract	ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.
abstractGer	ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.
abstract_unstemmed	ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.
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title_short	An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions
url	https://doi.org/10.3389/fendo.2022.981403 https://doaj.org/article/3d9722e9330c40c99408e6d82ec0f9cf https://www.frontiersin.org/articles/10.3389/fendo.2022.981403/full https://doaj.org/toc/1664-2392
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author2	Dong Xu Yuan Wang Hao Wu Wenliang Lu Wanru Chang Jincao Yao Meiying Yan Chanjuan Peng Chen Yang Liping Wang Lei Xu
author2Str	Dong Xu Yuan Wang Hao Wu Wenliang Lu Wanru Chang Jincao Yao Meiying Yan Chanjuan Peng Chen Yang Liping Wang Lei Xu
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up_date	2024-07-04T00:10:56.812Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ079614558</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307020543.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230307s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fendo.2022.981403</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ079614558</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ3d9722e9330c40c99408e6d82ec0f9cf</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">RC648-665</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Dong Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions</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">ObjectivesTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels.MethodsWe retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference.ResultsThe accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10-5. Two radiologists had higher sensitivity (0.84 and 0.78) than that of the AI system (0.69) at the cost of having much lower specificity (0.35, 0.57 versus 0.71). One senior radiologist showed balanced sensitivity and specificity (0.62 and 0.54) but both were lower than that of the AI system.ConclusionsThe generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">thyroid adenomas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adenocarcinomas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">follicular</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrasonography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the endocrine glands. 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An artificial intelligence ultrasound system’s ability to distinguish benign from malignant follicular-patterned lesions

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