Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab

Introducción: El COVID-19, causado por el SARS-CoV-2, tiene un espectro clínico que varía desde asintomático hasta grave, con diagnóstico principalmente a través de RT-PCR, aunque esta prueba no siempre es accesible ni rápida. Por ello, la tomografía computarizada (TC) de tórax se ha convertido en u...

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Autores:: Santacruz Carmen, Sebastian

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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network_acronym_str	UNAB2
network_name_str	Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
dc.title.translated.spa.fl_str_mv	Clinical validation of the deepsars artificial intelligence system at the Santander Ophthalmological Foundation - Foscal and Fosunab Foundation
title	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
spellingShingle	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab Artificial intelligence Medical sciences Radiology Diagnostic imaging Development scientific and technology Medical X-ray Imaging systems in medicine Public health Ciencias médicas Radiología Diagnóstico para imágenes Desarrollo científico y tecnológico Radiografía médica Sistemas de imágenes en medicina Salud pública Inteligencia artificial Covid-19
title_short	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
title_full	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
title_fullStr	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
title_full_unstemmed	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
title_sort	Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación Fosunab
dc.creator.fl_str_mv	Santacruz Carmen, Sebastian
dc.contributor.advisor.none.fl_str_mv	Mantilla García, Daniel Eduardo Valenzuela Santos, Diana María Vásquez Cardona, Lina María
dc.contributor.author.none.fl_str_mv	Santacruz Carmen, Sebastian
dc.contributor.cvlac.spa.fl_str_mv	Mantilla García, Daniel Eduardo [0001437130] Valenzuela Santos, Diana María [0001764194] Vásquez Cardona, Lina María [0001764229]
dc.contributor.googlescholar.spa.fl_str_mv	Mantilla García, Daniel Eduardo [es&oi=ao] Valenzuela Santos, Diana María [es&oi=ao] Vásquez Cardona, Lina María [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv	Mantilla García, Daniel Eduardo [0000-0003-1532-2101] Valenzuela Santos, Diana María [0000-0002-5664-9154] Vásquez Cardona, Lina María [0000-0002-4809-5825]
dc.contributor.apolounab.spa.fl_str_mv	Mantilla García, Daniel Eduardo [daniel-eduardo-mantilla-garcía]
dc.subject.keywords.spa.fl_str_mv	Artificial intelligence Medical sciences Radiology Diagnostic imaging Development scientific and technology Medical X-ray Imaging systems in medicine Public health
topic	Artificial intelligence Medical sciences Radiology Diagnostic imaging Development scientific and technology Medical X-ray Imaging systems in medicine Public health Ciencias médicas Radiología Diagnóstico para imágenes Desarrollo científico y tecnológico Radiografía médica Sistemas de imágenes en medicina Salud pública Inteligencia artificial Covid-19
dc.subject.lemb.spa.fl_str_mv	Ciencias médicas Radiología Diagnóstico para imágenes Desarrollo científico y tecnológico Radiografía médica Sistemas de imágenes en medicina Salud pública
dc.subject.proposal.spa.fl_str_mv	Inteligencia artificial Covid-19
description	Introducción: El COVID-19, causado por el SARS-CoV-2, tiene un espectro clínico que varía desde asintomático hasta grave, con diagnóstico principalmente a través de RT-PCR, aunque esta prueba no siempre es accesible ni rápida. Por ello, la tomografía computarizada (TC) de tórax se ha convertido en una herramienta importante para detectar el virus debido a su afectación del parénquima pulmonar. Con el avance de la inteligencia artificial (IA), se han desarrollado modelos para analizar imágenes radiológicas, como DeepSARS, un sistema diseñado en 2020 para identificar y monitorear casos de COVID-19 y riesgo de síndrome de dificultad respiratoria aguda. Este estudio tiene como objetivo validar la eficacia de DeepSARS en la identificación de estas condiciones mediante TC de tórax y resultados de RT-PCR. Metodología: Este estudio de evaluación de prueba diagnóstica analizó la base de datos de DeepSARS, recopilando datos de tomografías de tórax y resultados de RT-PCR de pacientes sospechosos de COVID-19 atendidos en FOSCAL y FOSUNAB entre octubre de 2020 y agosto de 2021. Se incluyeron tanto pacientes con resultados positivos como aquellos sin COVID-19 confirmados por al menos dos pruebas RT-PCR negativas. Se excluyeron las TC que no pudieron ser evaluadas en DeepSARS. Dos radiólogos revisaron las tomografías de manera independiente, clasificando la presencia de COVID-19 y la severidad pulmonar. El software DeepSARS se utilizó para determinar la presencia y gravedad de COVID-19, así como la probabilidad de síndrome de dificultad respiratoria aguda. El análisis estadístico evaluó el desempeño del software mediante medidas como la sensibilidad, especificidad y la concordancia entre hallazgos clínicos e imagenológicos. Resultados: Se incluyeron 57 pacientes sospechosos de COVID-19, de los cuales el 50.8% eran hombres, con una edad promedio de 67.7 años. Las comorbilidades más comunes fueron hipertensión (53.5%) y diabetes (26.7%). La mitad de los pacientes tuvo una prueba positiva para COVID-19. Los hallazgos radiológicos más frecuentes incluyeron opacidades en vidrio esmerilado (74.14%) y consolidaciones (62%). En cuanto a la evaluación con la plataforma DeepSARS, se detectaron imágenes sugestivas de COVID-19 en el 50% de los pacientes, siendo los hallazgos moderados y avanzados los más comunes. Los análisis estadísticos mostraron una buena concordancia entre las radiólogas en la mayoría de los hallazgos imagenológicos, aunque el puntaje CT score difería significativamente entre ellas. No se encontraron diferencias significativas en la capacidad de DeepSARS para discriminar entre pacientes con y sin COVID-19. Discusión: Durante la pandemia de COVID-19, la inteligencia artificial emergió como una herramienta prometedora para la detección temprana y clasificación de neumonía por COVID-19 mediante imágenes radiológicas. Este estudio validó la herramienta de inteligencia artificial DeepSARS para la detección de COVID-19 mediante tomografía computarizada, usando una muestra de 57 pacientes. Aunque la herramienta mostró una adecuada concordancia en hallazgos típicos de COVID-19, como opacidades en vidrio esmerilado y consolidación, su capacidad discriminatoria fue limitada, con un AUC de 0.538. Los hallazgos imagenológicos fueron consistentes con estudios previos en algunos aspectos, pero también revelaron diferencias. Las discrepancias podrían deberse a la necesidad de bases de datos más grandes y a problemas en la validación y reporte de modelos de IA en la literatura. Conclusión: La implementación de la inteligencia artificial en el diagnóstico de COVID-19 debe ser acompañada por una validación interna y externa rigurosa y ajuste continuo para garantizar su efectividad clínica. Los resultados de este estudio subrayan la necesidad de integrar datos más amplios y variados, para mejorar la detección temprana y la gestión de la enfermedad, que siempre deben ir acompañadas de un seguimiento médico.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-09-18T20:01:13Z
dc.date.available.none.fl_str_mv	2024-09-18T20:01:13Z
dc.date.issued.none.fl_str_mv	2024-09-18
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv	Tesis
dc.type.hasversion.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/26615
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/26615
identifier_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co
dc.language.iso.spa.fl_str_mv	spa
language	spa
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Theranostics. 2020 Apr 27;10(12):5613-5622. doi: 10.7150/thno.45985. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196293/ Agarwal, M., Saba, L., Gupta, S. K., Carriero, A., Falaschi, Z., Paschè, A., Danna, P., El-Baz, A., Naidu, S., & Suri, J. S. (2021). A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort. Journal of medical systems, 45(3), 28. https://doi.org/10.1007/s10916-021-01707-w Lessmann, N., Sánchez, C. I., Beenen, L., Boulogne, L. H., Brink, M., Calli, E., Charbonnier, J. P., Dofferhoff, T., van Everdingen, W. M., Gerke, P. K., Geurts, B., Gietema, H. A., Groeneveld, M., van Harten, L., Hendrix, N., Hendrix, W., Huisman, H. J., Išgum, I., Jacobs, C., Kluge, R., … van Ginneken, B. (2021). 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Respiratory research, 21(1), 286. https://doi.org/10.1186/s12931-020-01550-6 Zou, J. N., Sun, L., Wang, B. R., Zou, Y., Xu, S., Ding, Y. J., Shen, L. J., Huang, W. C., Jiang, X. J., & Chen, S. M. (2021). The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT. PloS one, 16(3), e0248957. https://doi.org/10.1371/journal.pone.0248957 Lessmann, N., Sánchez, C. I., Beenen, L., Boulogne, L. H., Brink, M., Calli, E., Charbonnier, J. P., Dofferhoff, T., van Everdingen, W. M., Gerke, P. K., Geurts, B., Gietema, H. A., Groeneveld, M., van Harten, L., Hendrix, N., Hendrix, W., Huisman, H. J., Išgum, I., Jacobs, C., Kluge, R., … van Ginneken, B. (2021). Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. 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Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019. Bioeng. Biotechnol., 31 July 2020 \| https://doi.org/10.3389/fbioe.2020.00898 Shuai Wang, Bo Kang, Jinlu Ma, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology Wang Q, Ma J, Zhang L, Xie L. Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis. Medicine [Internet]. 2022 Oct 21;101(42):e31346. Available from: https://doi.org/10.1097/md.0000000000031346 Rodriguez DR, Pinzón ÁM, Rubio C, Pinilla DI, Niño MJ, Díaz MA, et al. Clinical characteristics and mortality associated with COVID-19 at high altitude: a 57 cohort of 5161 patients in Bogotá, Colombia. International Journal of Emergency Medicine [Internet]. 2022 May 21;15(1). 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dc.coverage.spatial.spa.fl_str_mv	Floridablanca (Santander, Colombia)
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dc.publisher.grantor.spa.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
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spelling	Mantilla García, Daniel Eduardo02f2cc58-14df-4196-a68a-08dc2290cdc0Valenzuela Santos, Diana María62000588-b5e1-4d66-a021-e60d12a0da5aVásquez Cardona, Lina Maríaba32873c-5ff2-4de0-8c63-6eb0ddb4335cSantacruz Carmen, Sebastian978b24c7-f38b-48f0-a0cc-886d58fb6898Mantilla García, Daniel Eduardo [0001437130]Valenzuela Santos, Diana María [0001764194]Vásquez Cardona, Lina María [0001764229]Mantilla García, Daniel Eduardo [es&oi=ao]Valenzuela Santos, Diana María [es&oi=ao]Vásquez Cardona, Lina María [es&oi=ao]Mantilla García, Daniel Eduardo [0000-0003-1532-2101]Valenzuela Santos, Diana María [0000-0002-5664-9154]Vásquez Cardona, Lina María [0000-0002-4809-5825]Mantilla García, Daniel Eduardo [daniel-eduardo-mantilla-garcía]Floridablanca (Santander, Colombia)14 de octubre de 2020 al 31 de agosto de 2021UNAB Campus Bucaramanga2024-09-18T20:01:13Z2024-09-18T20:01:13Z2024-09-18http://hdl.handle.net/20.500.12749/26615instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coIntroducción: El COVID-19, causado por el SARS-CoV-2, tiene un espectro clínico que varía desde asintomático hasta grave, con diagnóstico principalmente a través de RT-PCR, aunque esta prueba no siempre es accesible ni rápida. Por ello, la tomografía computarizada (TC) de tórax se ha convertido en una herramienta importante para detectar el virus debido a su afectación del parénquima pulmonar. Con el avance de la inteligencia artificial (IA), se han desarrollado modelos para analizar imágenes radiológicas, como DeepSARS, un sistema diseñado en 2020 para identificar y monitorear casos de COVID-19 y riesgo de síndrome de dificultad respiratoria aguda. Este estudio tiene como objetivo validar la eficacia de DeepSARS en la identificación de estas condiciones mediante TC de tórax y resultados de RT-PCR. Metodología: Este estudio de evaluación de prueba diagnóstica analizó la base de datos de DeepSARS, recopilando datos de tomografías de tórax y resultados de RT-PCR de pacientes sospechosos de COVID-19 atendidos en FOSCAL y FOSUNAB entre octubre de 2020 y agosto de 2021. Se incluyeron tanto pacientes con resultados positivos como aquellos sin COVID-19 confirmados por al menos dos pruebas RT-PCR negativas. Se excluyeron las TC que no pudieron ser evaluadas en DeepSARS. Dos radiólogos revisaron las tomografías de manera independiente, clasificando la presencia de COVID-19 y la severidad pulmonar. El software DeepSARS se utilizó para determinar la presencia y gravedad de COVID-19, así como la probabilidad de síndrome de dificultad respiratoria aguda. El análisis estadístico evaluó el desempeño del software mediante medidas como la sensibilidad, especificidad y la concordancia entre hallazgos clínicos e imagenológicos. Resultados: Se incluyeron 57 pacientes sospechosos de COVID-19, de los cuales el 50.8% eran hombres, con una edad promedio de 67.7 años. Las comorbilidades más comunes fueron hipertensión (53.5%) y diabetes (26.7%). La mitad de los pacientes tuvo una prueba positiva para COVID-19. Los hallazgos radiológicos más frecuentes incluyeron opacidades en vidrio esmerilado (74.14%) y consolidaciones (62%). En cuanto a la evaluación con la plataforma DeepSARS, se detectaron imágenes sugestivas de COVID-19 en el 50% de los pacientes, siendo los hallazgos moderados y avanzados los más comunes. Los análisis estadísticos mostraron una buena concordancia entre las radiólogas en la mayoría de los hallazgos imagenológicos, aunque el puntaje CT score difería significativamente entre ellas. No se encontraron diferencias significativas en la capacidad de DeepSARS para discriminar entre pacientes con y sin COVID-19. Discusión: Durante la pandemia de COVID-19, la inteligencia artificial emergió como una herramienta prometedora para la detección temprana y clasificación de neumonía por COVID-19 mediante imágenes radiológicas. Este estudio validó la herramienta de inteligencia artificial DeepSARS para la detección de COVID-19 mediante tomografía computarizada, usando una muestra de 57 pacientes. Aunque la herramienta mostró una adecuada concordancia en hallazgos típicos de COVID-19, como opacidades en vidrio esmerilado y consolidación, su capacidad discriminatoria fue limitada, con un AUC de 0.538. Los hallazgos imagenológicos fueron consistentes con estudios previos en algunos aspectos, pero también revelaron diferencias. Las discrepancias podrían deberse a la necesidad de bases de datos más grandes y a problemas en la validación y reporte de modelos de IA en la literatura. Conclusión: La implementación de la inteligencia artificial en el diagnóstico de COVID-19 debe ser acompañada por una validación interna y externa rigurosa y ajuste continuo para garantizar su efectividad clínica. Los resultados de este estudio subrayan la necesidad de integrar datos más amplios y variados, para mejorar la detección temprana y la gestión de la enfermedad, que siempre deben ir acompañadas de un seguimiento médico.RESUMEN DEL PROYECTO 3 1. PLANTEAMIENTO DEL PROBLEMA 5 2.MARCO TEÓRICO 6 3. ESTADO DEL ARTE15 4. OBJETIVOS 4.1. Objetivo General 4.2. Objetivos específicos 19 5. METODOLOGÍA 5.1. Diseño del estudio 5.2. Población 5.3. Criterios de elegibilidad 5.4. Variables20 6. DESCRIPCIÓN DE LOS PROCEDIMIENTOS22 7.RESULTADOS ESPERADOS Y POTENCIALES BENEFICIARIOS 7.1.Relacionados con la generación de conocimiento y/o nuevos desarrollos tecnológicos e innovación 7.2.Conducentes al fortalecimiento de la capacidad científica institucional 7.3.Dirigidos a la apropiación social del conocimiento24 8.IMPACTO AMBIENTAL DEL PROYECTO26 9.CONSIDERACIONES ÉTICAS27 10.CRONOGRAMA DE ACTIVIDADES28 11.PRESUPUESTO29 12.RESULTADOS30 13.DISCUSIÓN44 14.CONCLUSIÓN48 15.REFERENCIAS BIBLIOGRÁFICAS49 16.ANEXOSEspecializaciónIntroduction: COVID-19, caused by SARS-CoV-2, has a clinical spectrum that varies from asymptomatic to severe, with diagnosis mainly through RT-PCR, although this test is not always accessible or rapid. Therefore, chest computed tomography (CT) has become an important tool to detect the virus due to its involvement of the lung parenchyma. With the advancement of artificial intelligence (AI), models have been developed to analyze radiological images, such as DeepSARS, a system designed in 2020 to identify and monitor cases of COVID-19 and risk of acute respiratory distress syndrome. This study aims to validate the effectiveness of DeepSARS in identifying these conditions using chest CT and RT-PCR results. Methodology: This diagnostic test evaluation study analyzed the DeepSARS database, collecting data from chest scans and RT-PCR results of suspected COVID-19 patients treated at FOSCAL and FOSUNAB between October 2020 and August 2021. They were included. both patients with positive results and those without COVID-19 confirmed by at least two negative RT-PCR tests. TCs that could not be evaluated in DeepSARS were excluded. Two radiologists independently reviewed the scans, classifying the presence of COVID-19 and lung severity. DeepSARS software was used to determine the presence and severity of COVID-19, as well as the likelihood of acute respiratory distress syndrome. The statistical analysis evaluated the performance of the software through measures such as sensitivity, specificity, and agreement between clinical and imaging findings. Results: 57 suspected COVID-19 patients were included, of which 50.8% were men, with an average age of 67.7 years. The most common comorbidities were hypertension (53.5%) and diabetes (26.7%). Half of the patients had a positive test for COVID-19. The most common radiological findings included ground glass opacities (74.14%) and consolidations (62%). Regarding the evaluation with the DeepSARS platform, images suggestive of COVID-19 were detected in 50% of the patients, with moderate and advanced findings being the most common. Statistical analyzes showed good agreement between radiologists in most of the imaging findings, although the CT score differed significantly between them. No significant differences were found in the ability of DeepSARS to discriminate between patients with and without COVID-19. Discussion: During the COVID-19 pandemic, artificial intelligence emerged as a promising tool for early detection and classification of COVID-19 pneumonia using radiological imaging. This study validated the DeepSARS artificial intelligence tool for the detection of COVID-19 using computed tomography, using a sample of 57 patients. Although the tool showed adequate agreement on typical COVID-19 findings, such as ground-glass opacities and consolidation, its discriminatory capacity was limited, with an AUC of 0.538. The imaging findings were consistent with previous studies in some aspects, but also revealed differences. The discrepancies could be due to the need for larger databases and problems in validating and reporting AI models in the literature. Conclusion: The implementation of artificial intelligence in the diagnosis of COVID-19 must be accompanied by rigorous internal and external validation and continuous adjustment to guarantee its clinical effectiveness. The results of this study underline the need to integrate broader and more varied data, to improve early detection and management of the disease, which must always be accompanied by medical follow-up.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Validación clínica del sistema de inteligencia artificial deepsars en la Fundación Oftalmológica de Santander - Foscal y Fundación FosunabClinical validation of the deepsars artificial intelligence system at the Santander Ophthalmological Foundation - Foscal and Fosunab FoundationEspecialistas en Radiología e Imágenes DiagnósticasUniversidad Autónoma de Bucaramanga UNABFacultad Ciencias de la SaludEspecialización en Radiología e Imágenes Diagnósticasinfo:eu-repo/semantics/masterThesisTesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TMArtificial intelligenceMedical sciencesRadiologyDiagnostic imagingDevelopment scientific and technologyMedical X-rayImaging systems in medicinePublic healthCiencias médicasRadiologíaDiagnóstico para imágenesDesarrollo científico y tecnológicoRadiografía médicaSistemas de imágenes en medicinaSalud públicaInteligencia artificialCovid-19Islam, K. 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