A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triagi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qin, Zhi Zhen [verfasserIn] Naheyan, Tasneem [verfasserIn] Ruhwald, Morten [verfasserIn] Denkinger, Claudia M. - 1978- [verfasserIn] Gelaw, Sifrash [verfasserIn] Nash, Madlen [verfasserIn] Creswell, Jacob [verfasserIn] Kik, Sandra Vivian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
4 January 2021 |
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| Schlagwörter: |
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| Anmerkung: |
Gesehen am 28.04.2021 |
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| Umfang: |
6 |
| Übergeordnetes Werk: |
Enthalten in: Tuberculosis - Edinburgh : Churchill Livingstone, 2001, 127(2021) vom: März, Artikel-ID 102049, Seite 1-6 |
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| Übergeordnetes Werk: |
volume:127 ; year:2021 ; month:03 ; elocationid:102049 ; pages:1-6 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.tube.2020.102049 |
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| Katalog-ID: |
1756022704 |
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| 245 | 1 | 2 | |a A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers |c Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik |
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| 520 | |a Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Chest X-ray | |
| 650 | 4 | |a Computer automated detection | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Diagnostic | |
| 650 | 4 | |a Tuberculosis | |
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| 700 | 1 | |a Ruhwald, Morten |e verfasserin |4 aut | |
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| 700 | 1 | |a Gelaw, Sifrash |e verfasserin |4 aut | |
| 700 | 1 | |a Nash, Madlen |e verfasserin |4 aut | |
| 700 | 1 | |a Creswell, Jacob |e verfasserin |4 aut | |
| 700 | 1 | |a Kik, Sandra Vivian |e verfasserin |4 aut | |
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10.1016/j.tube.2020.102049 doi (DE-627)1756022704 (DE-599)KXP1756022704 (OCoLC)1341406324 DE-627 ger DE-627 rda eng Qin, Zhi Zhen verfasserin (DE-588)1232353582 (DE-627)1756269947 aut A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik 4 January 2021 6 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 28.04.2021 Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Artificial intelligence Chest X-ray Computer automated detection Deep learning Diagnostic Tuberculosis Naheyan, Tasneem verfasserin aut Ruhwald, Morten verfasserin aut Denkinger, Claudia M. 1978- verfasserin (DE-588)131597701 (DE-627)511279469 (DE-576)298613727 aut Gelaw, Sifrash verfasserin aut Nash, Madlen verfasserin aut Creswell, Jacob verfasserin aut Kik, Sandra Vivian verfasserin aut Enthalten in Tuberculosis Edinburgh : Churchill Livingstone, 2001 127(2021) vom: März, Artikel-ID 102049, Seite 1-6 Online-Ressource (DE-627)33492720X (DE-600)2058561-5 (DE-576)272349887 1873-281X nnns volume:127 year:2021 month:03 elocationid:102049 pages:1-6 extent:6 https://doi.org/10.1016/j.tube.2020.102049 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S147297922030216X Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_168 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 127 2021 3 102049 1-6 6 2013 01 DE-16-250 3918173674 00 --%%-- --%%-- --%%-- --%%-- l01 27-04-21 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_8 2013 01 DE-16-250 03 s s_6 2013 01 DE-16-250 04 p (DE-627)1668020653 Denkinger, Claudia M. 2013 01 DE-16-250 04 k (DE-627)1495544966 Zentrum für Infektiologie 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_4 |
| spelling |
10.1016/j.tube.2020.102049 doi (DE-627)1756022704 (DE-599)KXP1756022704 (OCoLC)1341406324 DE-627 ger DE-627 rda eng Qin, Zhi Zhen verfasserin (DE-588)1232353582 (DE-627)1756269947 aut A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik 4 January 2021 6 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 28.04.2021 Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Artificial intelligence Chest X-ray Computer automated detection Deep learning Diagnostic Tuberculosis Naheyan, Tasneem verfasserin aut Ruhwald, Morten verfasserin aut Denkinger, Claudia M. 1978- verfasserin (DE-588)131597701 (DE-627)511279469 (DE-576)298613727 aut Gelaw, Sifrash verfasserin aut Nash, Madlen verfasserin aut Creswell, Jacob verfasserin aut Kik, Sandra Vivian verfasserin aut Enthalten in Tuberculosis Edinburgh : Churchill Livingstone, 2001 127(2021) vom: März, Artikel-ID 102049, Seite 1-6 Online-Ressource (DE-627)33492720X (DE-600)2058561-5 (DE-576)272349887 1873-281X nnns volume:127 year:2021 month:03 elocationid:102049 pages:1-6 extent:6 https://doi.org/10.1016/j.tube.2020.102049 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S147297922030216X Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_168 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 127 2021 3 102049 1-6 6 2013 01 DE-16-250 3918173674 00 --%%-- --%%-- --%%-- --%%-- l01 27-04-21 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_8 2013 01 DE-16-250 03 s s_6 2013 01 DE-16-250 04 p (DE-627)1668020653 Denkinger, Claudia M. 2013 01 DE-16-250 04 k (DE-627)1495544966 Zentrum für Infektiologie 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_4 |
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10.1016/j.tube.2020.102049 doi (DE-627)1756022704 (DE-599)KXP1756022704 (OCoLC)1341406324 DE-627 ger DE-627 rda eng Qin, Zhi Zhen verfasserin (DE-588)1232353582 (DE-627)1756269947 aut A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik 4 January 2021 6 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 28.04.2021 Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Artificial intelligence Chest X-ray Computer automated detection Deep learning Diagnostic Tuberculosis Naheyan, Tasneem verfasserin aut Ruhwald, Morten verfasserin aut Denkinger, Claudia M. 1978- verfasserin (DE-588)131597701 (DE-627)511279469 (DE-576)298613727 aut Gelaw, Sifrash verfasserin aut Nash, Madlen verfasserin aut Creswell, Jacob verfasserin aut Kik, Sandra Vivian verfasserin aut Enthalten in Tuberculosis Edinburgh : Churchill Livingstone, 2001 127(2021) vom: März, Artikel-ID 102049, Seite 1-6 Online-Ressource (DE-627)33492720X (DE-600)2058561-5 (DE-576)272349887 1873-281X nnns volume:127 year:2021 month:03 elocationid:102049 pages:1-6 extent:6 https://doi.org/10.1016/j.tube.2020.102049 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S147297922030216X Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_168 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 127 2021 3 102049 1-6 6 2013 01 DE-16-250 3918173674 00 --%%-- --%%-- --%%-- --%%-- l01 27-04-21 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_8 2013 01 DE-16-250 03 s s_6 2013 01 DE-16-250 04 p (DE-627)1668020653 Denkinger, Claudia M. 2013 01 DE-16-250 04 k (DE-627)1495544966 Zentrum für Infektiologie 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_4 |
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10.1016/j.tube.2020.102049 doi (DE-627)1756022704 (DE-599)KXP1756022704 (OCoLC)1341406324 DE-627 ger DE-627 rda eng Qin, Zhi Zhen verfasserin (DE-588)1232353582 (DE-627)1756269947 aut A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik 4 January 2021 6 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 28.04.2021 Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Artificial intelligence Chest X-ray Computer automated detection Deep learning Diagnostic Tuberculosis Naheyan, Tasneem verfasserin aut Ruhwald, Morten verfasserin aut Denkinger, Claudia M. 1978- verfasserin (DE-588)131597701 (DE-627)511279469 (DE-576)298613727 aut Gelaw, Sifrash verfasserin aut Nash, Madlen verfasserin aut Creswell, Jacob verfasserin aut Kik, Sandra Vivian verfasserin aut Enthalten in Tuberculosis Edinburgh : Churchill Livingstone, 2001 127(2021) vom: März, Artikel-ID 102049, Seite 1-6 Online-Ressource (DE-627)33492720X (DE-600)2058561-5 (DE-576)272349887 1873-281X nnns volume:127 year:2021 month:03 elocationid:102049 pages:1-6 extent:6 https://doi.org/10.1016/j.tube.2020.102049 Verlag Resolving-System lizenzpflichtig Volltext https://www.sciencedirect.com/science/article/pii/S147297922030216X Verlag lizenzpflichtig Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_168 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 127 2021 3 102049 1-6 6 2013 01 DE-16-250 3918173674 00 --%%-- --%%-- --%%-- --%%-- l01 27-04-21 2013 01 DE-16-250 00 s hd2021 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_8 2013 01 DE-16-250 03 s s_6 2013 01 DE-16-250 04 p (DE-627)1668020653 Denkinger, Claudia M. 2013 01 DE-16-250 04 k (DE-627)1495544966 Zentrum für Infektiologie 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_4 |
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Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. 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new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers |
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A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers |
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Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Gesehen am 28.04.2021 |
| abstractGer |
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Gesehen am 28.04.2021 |
| abstract_unstemmed |
Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. Gesehen am 28.04.2021 |
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A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers |
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