Development and validation of an infrared-artificial intelligence software for breast cancer detection

Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not...
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Autor*in:	Enrique Martín-Del-Campo-Mena [verfasserIn] Pedro A. Sánchez-Méndez [verfasserIn] Eva Ruvalcaba-Limon [verfasserIn] Federico M. Lazcano-Ramírez [verfasserIn] Andrés Hernández-Santiago [verfasserIn] Jorge A. Juárez-Aburto [verfasserIn] Kictzia Y. Larios-Cruz [verfasserIn] L. Enrique Hernández-Gómez [verfasserIn] J. Andrei Merino-González [verfasserIn] Yessica González-Mejía [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	breast cancer infrared thermography artificial intelligence mammography screening

Übergeordnetes Werk:	In: Exploration of Targeted Anti-tumor Therapy - Open Exploration Publishing Inc., 2021, 4(2023), 2, Seite 294-306
Übergeordnetes Werk:	volume:4 ; year:2023 ; number:2 ; pages:294-306

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.37349/etat.2023.00135

Katalog-ID:	DOAJ090135156

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520			\|a Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.
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allfields	10.37349/etat.2023.00135 doi (DE-627)DOAJ090135156 (DE-599)DOAJa6aacac1ad414898a70f9dbc2da0173e DE-627 ger DE-627 rakwb eng RC31-1245 Enrique Martín-Del-Campo-Mena verfasserin aut Development and validation of an infrared-artificial intelligence software for breast cancer detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC. breast cancer infrared thermography artificial intelligence mammography screening Internal medicine Pedro A. Sánchez-Méndez verfasserin aut Eva Ruvalcaba-Limon verfasserin aut Federico M. Lazcano-Ramírez verfasserin aut Andrés Hernández-Santiago verfasserin aut Jorge A. Juárez-Aburto verfasserin aut Kictzia Y. Larios-Cruz verfasserin aut L. Enrique Hernández-Gómez verfasserin aut J. Andrei Merino-González verfasserin aut Yessica González-Mejía verfasserin aut In Exploration of Targeted Anti-tumor Therapy Open Exploration Publishing Inc., 2021 4(2023), 2, Seite 294-306 (DE-627)174398264X 26923114 nnns volume:4 year:2023 number:2 pages:294-306 https://doi.org/10.37349/etat.2023.00135 kostenfrei https://doaj.org/article/a6aacac1ad414898a70f9dbc2da0173e kostenfrei https://www.explorationpub.com/Journals/etat/Article/1002135 kostenfrei https://doaj.org/toc/2692-3114 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 4 2023 2 294-306
spelling	10.37349/etat.2023.00135 doi (DE-627)DOAJ090135156 (DE-599)DOAJa6aacac1ad414898a70f9dbc2da0173e DE-627 ger DE-627 rakwb eng RC31-1245 Enrique Martín-Del-Campo-Mena verfasserin aut Development and validation of an infrared-artificial intelligence software for breast cancer detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC. breast cancer infrared thermography artificial intelligence mammography screening Internal medicine Pedro A. Sánchez-Méndez verfasserin aut Eva Ruvalcaba-Limon verfasserin aut Federico M. Lazcano-Ramírez verfasserin aut Andrés Hernández-Santiago verfasserin aut Jorge A. Juárez-Aburto verfasserin aut Kictzia Y. Larios-Cruz verfasserin aut L. Enrique Hernández-Gómez verfasserin aut J. Andrei Merino-González verfasserin aut Yessica González-Mejía verfasserin aut In Exploration of Targeted Anti-tumor Therapy Open Exploration Publishing Inc., 2021 4(2023), 2, Seite 294-306 (DE-627)174398264X 26923114 nnns volume:4 year:2023 number:2 pages:294-306 https://doi.org/10.37349/etat.2023.00135 kostenfrei https://doaj.org/article/a6aacac1ad414898a70f9dbc2da0173e kostenfrei https://www.explorationpub.com/Journals/etat/Article/1002135 kostenfrei https://doaj.org/toc/2692-3114 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 4 2023 2 294-306
allfields_unstemmed	10.37349/etat.2023.00135 doi (DE-627)DOAJ090135156 (DE-599)DOAJa6aacac1ad414898a70f9dbc2da0173e DE-627 ger DE-627 rakwb eng RC31-1245 Enrique Martín-Del-Campo-Mena verfasserin aut Development and validation of an infrared-artificial intelligence software for breast cancer detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC. breast cancer infrared thermography artificial intelligence mammography screening Internal medicine Pedro A. Sánchez-Méndez verfasserin aut Eva Ruvalcaba-Limon verfasserin aut Federico M. Lazcano-Ramírez verfasserin aut Andrés Hernández-Santiago verfasserin aut Jorge A. Juárez-Aburto verfasserin aut Kictzia Y. Larios-Cruz verfasserin aut L. Enrique Hernández-Gómez verfasserin aut J. Andrei Merino-González verfasserin aut Yessica González-Mejía verfasserin aut In Exploration of Targeted Anti-tumor Therapy Open Exploration Publishing Inc., 2021 4(2023), 2, Seite 294-306 (DE-627)174398264X 26923114 nnns volume:4 year:2023 number:2 pages:294-306 https://doi.org/10.37349/etat.2023.00135 kostenfrei https://doaj.org/article/a6aacac1ad414898a70f9dbc2da0173e kostenfrei https://www.explorationpub.com/Journals/etat/Article/1002135 kostenfrei https://doaj.org/toc/2692-3114 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 4 2023 2 294-306
allfieldsGer	10.37349/etat.2023.00135 doi (DE-627)DOAJ090135156 (DE-599)DOAJa6aacac1ad414898a70f9dbc2da0173e DE-627 ger DE-627 rakwb eng RC31-1245 Enrique Martín-Del-Campo-Mena verfasserin aut Development and validation of an infrared-artificial intelligence software for breast cancer detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC. breast cancer infrared thermography artificial intelligence mammography screening Internal medicine Pedro A. Sánchez-Méndez verfasserin aut Eva Ruvalcaba-Limon verfasserin aut Federico M. Lazcano-Ramírez verfasserin aut Andrés Hernández-Santiago verfasserin aut Jorge A. Juárez-Aburto verfasserin aut Kictzia Y. Larios-Cruz verfasserin aut L. Enrique Hernández-Gómez verfasserin aut J. Andrei Merino-González verfasserin aut Yessica González-Mejía verfasserin aut In Exploration of Targeted Anti-tumor Therapy Open Exploration Publishing Inc., 2021 4(2023), 2, Seite 294-306 (DE-627)174398264X 26923114 nnns volume:4 year:2023 number:2 pages:294-306 https://doi.org/10.37349/etat.2023.00135 kostenfrei https://doaj.org/article/a6aacac1ad414898a70f9dbc2da0173e kostenfrei https://www.explorationpub.com/Journals/etat/Article/1002135 kostenfrei https://doaj.org/toc/2692-3114 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 4 2023 2 294-306
allfieldsSound	10.37349/etat.2023.00135 doi (DE-627)DOAJ090135156 (DE-599)DOAJa6aacac1ad414898a70f9dbc2da0173e DE-627 ger DE-627 rakwb eng RC31-1245 Enrique Martín-Del-Campo-Mena verfasserin aut Development and validation of an infrared-artificial intelligence software for breast cancer detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC. breast cancer infrared thermography artificial intelligence mammography screening Internal medicine Pedro A. Sánchez-Méndez verfasserin aut Eva Ruvalcaba-Limon verfasserin aut Federico M. Lazcano-Ramírez verfasserin aut Andrés Hernández-Santiago verfasserin aut Jorge A. Juárez-Aburto verfasserin aut Kictzia Y. Larios-Cruz verfasserin aut L. Enrique Hernández-Gómez verfasserin aut J. Andrei Merino-González verfasserin aut Yessica González-Mejía verfasserin aut In Exploration of Targeted Anti-tumor Therapy Open Exploration Publishing Inc., 2021 4(2023), 2, Seite 294-306 (DE-627)174398264X 26923114 nnns volume:4 year:2023 number:2 pages:294-306 https://doi.org/10.37349/etat.2023.00135 kostenfrei https://doaj.org/article/a6aacac1ad414898a70f9dbc2da0173e kostenfrei https://www.explorationpub.com/Journals/etat/Article/1002135 kostenfrei https://doaj.org/toc/2692-3114 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 4 2023 2 294-306
language	English
source	In Exploration of Targeted Anti-tumor Therapy 4(2023), 2, Seite 294-306 volume:4 year:2023 number:2 pages:294-306
sourceStr	In Exploration of Targeted Anti-tumor Therapy 4(2023), 2, Seite 294-306 volume:4 year:2023 number:2 pages:294-306
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title	Development and validation of an infrared-artificial intelligence software for breast cancer detection
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author_browse	Enrique Martín-Del-Campo-Mena Pedro A. Sánchez-Méndez Eva Ruvalcaba-Limon Federico M. Lazcano-Ramírez Andrés Hernández-Santiago Jorge A. Juárez-Aburto Kictzia Y. Larios-Cruz L. Enrique Hernández-Gómez J. Andrei Merino-González Yessica González-Mejía
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title_sort	development and validation of an infrared-artificial intelligence software for breast cancer detection
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title_auth	Development and validation of an infrared-artificial intelligence software for breast cancer detection
abstract	Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.
abstractGer	Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.
abstract_unstemmed	Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases. Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test. Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.
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title_short	Development and validation of an infrared-artificial intelligence software for breast cancer detection
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Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV. Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). 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