AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites
Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
JIANG Yang [verfasserIn] YU Huizhi [verfasserIn] GAO Ya [verfasserIn] SHEN Yu [verfasserIn] MAO Min [verfasserIn] LIU Chongmei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Zhongliu Fangzhi Yanjiu - Magazine House of Cancer Research on Prevention and Treatment, 2019, 50(2023), 4, Seite 390-396 |
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| Übergeordnetes Werk: |
volume:50 ; year:2023 ; number:4 ; pages:390-396 |
| Links: |
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| DOI / URN: |
10.3971/j.issn.1000-8578.2023.22.0762 |
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| Katalog-ID: |
DOAJ090458737 |
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| 520 | |a Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. | ||
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a liquid-based cytology | |
| 650 | 4 | |a dna quantitative analysis | |
| 650 | 4 | |a pleural and ascites effusion | |
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| 653 | 0 | |a Neoplasms. Tumors. Oncology. Including cancer and carcinogens | |
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10.3971/j.issn.1000-8578.2023.22.0762 doi (DE-627)DOAJ090458737 (DE-599)DOAJbaec98e84d124b77b5b752a5d4dec0f3 DE-627 ger DE-627 rakwb chi RC254-282 JIANG Yang verfasserin aut AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value Neoplasms. Tumors. Oncology. Including cancer and carcinogens YU Huizhi verfasserin aut GAO Ya verfasserin aut SHEN Yu verfasserin aut MAO Min verfasserin aut LIU Chongmei verfasserin aut In Zhongliu Fangzhi Yanjiu Magazine House of Cancer Research on Prevention and Treatment, 2019 50(2023), 4, Seite 390-396 (DE-627)176063638X 10008578 nnns volume:50 year:2023 number:4 pages:390-396 https://doi.org/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/article/baec98e84d124b77b5b752a5d4dec0f3 kostenfrei http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/toc/1000-8578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 390-396 |
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10.3971/j.issn.1000-8578.2023.22.0762 doi (DE-627)DOAJ090458737 (DE-599)DOAJbaec98e84d124b77b5b752a5d4dec0f3 DE-627 ger DE-627 rakwb chi RC254-282 JIANG Yang verfasserin aut AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value Neoplasms. Tumors. Oncology. Including cancer and carcinogens YU Huizhi verfasserin aut GAO Ya verfasserin aut SHEN Yu verfasserin aut MAO Min verfasserin aut LIU Chongmei verfasserin aut In Zhongliu Fangzhi Yanjiu Magazine House of Cancer Research on Prevention and Treatment, 2019 50(2023), 4, Seite 390-396 (DE-627)176063638X 10008578 nnns volume:50 year:2023 number:4 pages:390-396 https://doi.org/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/article/baec98e84d124b77b5b752a5d4dec0f3 kostenfrei http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/toc/1000-8578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 390-396 |
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10.3971/j.issn.1000-8578.2023.22.0762 doi (DE-627)DOAJ090458737 (DE-599)DOAJbaec98e84d124b77b5b752a5d4dec0f3 DE-627 ger DE-627 rakwb chi RC254-282 JIANG Yang verfasserin aut AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value Neoplasms. Tumors. Oncology. Including cancer and carcinogens YU Huizhi verfasserin aut GAO Ya verfasserin aut SHEN Yu verfasserin aut MAO Min verfasserin aut LIU Chongmei verfasserin aut In Zhongliu Fangzhi Yanjiu Magazine House of Cancer Research on Prevention and Treatment, 2019 50(2023), 4, Seite 390-396 (DE-627)176063638X 10008578 nnns volume:50 year:2023 number:4 pages:390-396 https://doi.org/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/article/baec98e84d124b77b5b752a5d4dec0f3 kostenfrei http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/toc/1000-8578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 390-396 |
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10.3971/j.issn.1000-8578.2023.22.0762 doi (DE-627)DOAJ090458737 (DE-599)DOAJbaec98e84d124b77b5b752a5d4dec0f3 DE-627 ger DE-627 rakwb chi RC254-282 JIANG Yang verfasserin aut AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value Neoplasms. Tumors. Oncology. Including cancer and carcinogens YU Huizhi verfasserin aut GAO Ya verfasserin aut SHEN Yu verfasserin aut MAO Min verfasserin aut LIU Chongmei verfasserin aut In Zhongliu Fangzhi Yanjiu Magazine House of Cancer Research on Prevention and Treatment, 2019 50(2023), 4, Seite 390-396 (DE-627)176063638X 10008578 nnns volume:50 year:2023 number:4 pages:390-396 https://doi.org/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/article/baec98e84d124b77b5b752a5d4dec0f3 kostenfrei http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/toc/1000-8578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 390-396 |
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10.3971/j.issn.1000-8578.2023.22.0762 doi (DE-627)DOAJ090458737 (DE-599)DOAJbaec98e84d124b77b5b752a5d4dec0f3 DE-627 ger DE-627 rakwb chi RC254-282 JIANG Yang verfasserin aut AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value Neoplasms. Tumors. Oncology. Including cancer and carcinogens YU Huizhi verfasserin aut GAO Ya verfasserin aut SHEN Yu verfasserin aut MAO Min verfasserin aut LIU Chongmei verfasserin aut In Zhongliu Fangzhi Yanjiu Magazine House of Cancer Research on Prevention and Treatment, 2019 50(2023), 4, Seite 390-396 (DE-627)176063638X 10008578 nnns volume:50 year:2023 number:4 pages:390-396 https://doi.org/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/article/baec98e84d124b77b5b752a5d4dec0f3 kostenfrei http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2023.22.0762 kostenfrei https://doaj.org/toc/1000-8578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 390-396 |
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Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. 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RC254-282 AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites artificial intelligence liquid-based cytology dna quantitative analysis pleural and ascites effusion diagnostic value |
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ai cytomorphology combined with dna-image cytometry for identifying benign and malignant pleural effusion and ascites |
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AI Cytomorphology Combined with DNA-image Cytometry for Identifying Benign and Malignant Pleural Effusion and Ascites |
| abstract |
Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. |
| abstractGer |
Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. |
| abstract_unstemmed |
Objective To explore the diagnostic value of artificial intelligence (AI) cytology combined with DNA-image cytometry (DNA-ICM) auxiliary diagnostic system for the identification of benign and malignant pleural effusion and ascites. Methods Liquid-based cytology technology (LCT), DNA-ICM, AI, and AI combined with DNA-ICM were used to identify benign and malignant pleural effusion and ascites specimens in 360 cases, and their sensitivity, specificity, accuracy, Kappa value, Youden index and AUC were statistically analyzed. Results The sensitivity, specificity, and accuracy of AI combined with DNA-ICM in detecting benign and malignant pleural effusion and ascites were 95.23%, 94.12%, and 94.44%, respectively, which were higher than those of the three other separate detection methods (all P < 0.05). The kappa values of LCT, DNA-ICM, and AI were 0.646, 0.642, and 0.586; their Youden index values were 0.693, 0.687, and 0.676, and their AUC values were 0.846, 0.843, and 0.838, respectively. The Kappa value of AI combined with DNA-ICM was 0.869, the Youden index was 0.893, and AUC was 0.947, which were all higher than those of the three detection methods alone. Conclusion Among the three separate detection methods, LCT has the highest reliability, authenticity, and diagnostic value, and it can be used as a common method for the clinical identification of benign and malignant pleural effusion and ascites. The diagnostic performance of AI combined with DNA-ICM auxiliary diagnosis system in identifying benign and malignant pleural effusion and ascites is better than those of the three separate detection methods and can be used as a reliable method for the clinical identification of benign and malignant pleural effusion and ascites. |
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