Detección antenatal de cardiopatías congénitas mediante inteligencia artificial

Objetivo: analizar la precisión diagnóstica de una arquitectura basada en “Vision Transformer” para la detección prenatal de cardiopatías congénitas (CC) de acuerdo con cada trimestre del embarazo en un país de medianos ingresos. Métodos: se realizó un estudio retrospectivo, observacional y analític...

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Autores:: Ramírez Mateus, Nataly Alejandra
Echeverry Bermúdez, Lina Marcela

Tipo de recurso:: https://purl.org/coar/resource_type/c_7a1f

Fecha de publicación:: 2025

Institución:: Universidad El Bosque

Repositorio:: Repositorio U. El Bosque

Idioma:: spa

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dc.title.none.fl_str_mv	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
dc.title.translated.none.fl_str_mv	Prenatal detection of congenital heart defects using artificial intelligence
title	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
spellingShingle	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial Ultrasonografía Inteligencia Artificial Cardiopatía Congénita Diagnóstico Prenatal Ultrasonography Artificial Intelligence Congenital Heart Disease Prenatal Diagnosis WP 100
title_short	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
title_full	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
title_fullStr	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
title_full_unstemmed	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
title_sort	Detección antenatal de cardiopatías congénitas mediante inteligencia artificial
dc.creator.fl_str_mv	Ramírez Mateus, Nataly Alejandra Echeverry Bermúdez, Lina Marcela
dc.contributor.advisor.none.fl_str_mv	Rodríguez Acosta, Nadiezhda Báez Camacho, Martha Lucía
dc.contributor.author.none.fl_str_mv	Ramírez Mateus, Nataly Alejandra Echeverry Bermúdez, Lina Marcela
dc.contributor.orcid.none.fl_str_mv	Echeverry Bermúdez, Lina Marcela [0000-0001-9102-1664]
dc.subject.none.fl_str_mv	Ultrasonografía Inteligencia Artificial Cardiopatía Congénita Diagnóstico Prenatal
topic	Ultrasonografía Inteligencia Artificial Cardiopatía Congénita Diagnóstico Prenatal Ultrasonography Artificial Intelligence Congenital Heart Disease Prenatal Diagnosis WP 100
dc.subject.keywords.none.fl_str_mv	Ultrasonography Artificial Intelligence Congenital Heart Disease Prenatal Diagnosis
dc.subject.nlm.none.fl_str_mv	WP 100
description	Objetivo: analizar la precisión diagnóstica de una arquitectura basada en “Vision Transformer” para la detección prenatal de cardiopatías congénitas (CC) de acuerdo con cada trimestre del embarazo en un país de medianos ingresos. Métodos: se realizó un estudio retrospectivo, observacional y analítico en las pacientes con ecografía obstétrica realizada en la Fundación Santa Fe de Bogotá entre el 2019 y el 2023. Se incluyeron imágenes de corazón fetal con los cortes 4C, 3VT, SVD, SVI. Se entrenó una arquitectura basada en “Vision Transformer” en donde se evaluó el rendimiento en la detección de CC por trimestre de gestación. Resultados: se revisaron 12.402 ecografías y se incluyeron 3.260. Se identificaron 181 ecografías de fetos con CC. La mayoría de las CC se detectaron en el segundo (44,6%) y tercer trimestre (52,4%), siendo la comunicación interventricular la más común. Se utilizaron 2.966 imágenes para el entrenamiento del modelo y 294 para la validación, logrando el mejor rendimiento en el segundo trimestre con una F-medida de 0,61, sensibilidad del 97%, especificidad del 88%, valor predictivo positivo del 90% y valor predictivo negativo del 97%. Conclusiones: la prevalencia de CC anteparto fue de 6,4%, superior a otros estudios, posiblemente por tratarse de un hospital de referencia y remisión dada su alta complejidad. El modelo mostró un buen rendimiento en la detección de CC, con una F1-medida de 61%, destacando el potencial de la inteligencia artificial en regiones con acceso limitado a especialistas para mejorar la precisión en el diagnóstico de CC.
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-02-13T15:45:50Z
dc.date.available.none.fl_str_mv	2025-02-13T15:45:50Z
dc.date.issued.none.fl_str_mv	2025-01
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dc.type.local.spa.fl_str_mv	Tesis/Trabajo de grado - Monografía - Especialización
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dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
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dc.identifier.instname.spa.fl_str_mv	instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad El Bosque
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url	https://hdl.handle.net/20.500.12495/13943
identifier_str_mv	instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque repourl:https://repositorio.unbosque.edu.co
dc.language.iso.fl_str_mv	spa
language	spa
dc.relation.references.none.fl_str_mv	Karim JN, Bradburn E, Roberts N, Papageorghiou AT. First-trimester ultrasound detection of fetal heart anomalies: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2022 Jan;59(1):11–25. Carvalho JS, Allan LD, Chaoui R, Copel JA, DeVore GR, Hecher K, et al. ISUOG Practice Guidelines (updated): sonographic screening examination of the fetal heart. 2013. Liu Y, Chen S, Zühlke L, Black GC, Choy MK, Li N, et al. Global birth prevalence of congenital heart defects 1970-2017: updated systematic review and meta-analysis of 260 studies. Int J Epidemiol. 2019 Apr;48(2):455–63. Bravo-Valenzuela NJ, Peixoto AB, Araujo Júnior E. Prenatal diagnosis of congenital heart disease: A review of current knowledge. Indian Heart J. 2018;70(1):150–64. Zimmerman M, Sable C. Congenital heart disease in low-and-middle-income countries: Focus on sub-Saharan Africa. Am J Med Genet C Semin Med Genet. 2020 Mar;184(1):36–46. Hopkins MK, Dugoff L, Kuller JA. Congenital Heart Disease: Prenatal Diagnosis and Genetic Associations. Obstet Gynecol Surv. 2019 Aug;74(8):497–503. Carvalho JS, Axt-Fliedner R, Chaoui R, Copel JA, Cuneo BF, Goff D, et al. ISUOG Practice Guidelines (updated): fetal cardiac screening. Ultrasound Obstet Gynecol. 2023 Jun;61(6):788–803. Yu D, Sui L, Zhang N. Performance of First-Trimester Fetal Echocardiography in Diagnosing Fetal Heart Defects: Meta-analysis and Systematic Review. J Ultrasound Med. 2020 Mar;39(3):471–80. Alexey D. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 201011929. 2020. Horgan R, Nehme L, Abuhamad A. Artificial intelligence in obstetric ultrasound: A scoping review. Prenat Diagn. 2023 Aug;43(9):1176–219. Qiao S, Pang S, Luo G, Sun Y, Yin W, Pan S, et al. DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography. Complex & Intelligent Systems. 2023;9(4):4503–19. Shen YT, Chen L, Yue WW, Xu HX. Artificial intelligence in ultrasound. Eur J Radiol. 2021 Jun;139:109717. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical Image Analysis using Convolutional Neural Networks: A Review. J Med Syst. 2018 Oct;42(11):226. Arafati A, Hu P, Finn JP, Rickers C, Cheng AL, Jafarkhani H, et al. Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need. Cardiovasc Diagn Ther. 2019 Oct;9(Suppl 2):S310–25. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016. 770–778 p. Superior EP. Funciones de activación ruidosas en redes neuronales recurrentes. 2020. Drukker L. Real-time identification of fetal anomalies on ultrasound using artificial intelligence: what’s next? Vol. 59, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. England; 2022. p. 285–7. Timmerman D, Planchamp F, Bourne T, Landolfo C, du Bois A, Chiva L, et al. ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors. Ultrasound Obstet Gynecol. 2021 Jul;58(1):148–68. Courant R, Edberg M, Dufour N, Kalogeiton V. Transformers and visual Transformers. Machine Learning for Brain Disorders. 2023;193–229. Vaswani A. Attention is all you need. Adv Neural Inf Process Syst. 2017. Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, et al. Transformers in medical imaging: A survey. Med Image Anal. 2023;88:102802. Al-Hammuri K, Gebali F, Kanan A, Chelvan IT. Vision transformer architecture and applications in digital health: a tutorial and survey. Vis Comput Ind Biomed Art. 2023;6(1):14. Liu Z, Lv Q, Yang Z, Li Y, Lee CH, Shen L. Recent progress in transformer-based medical image analysis. Comput Biol Med. 2023;107268. Park T, Liu MY, Wang TC, Zhu JY. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019. p. 2337–46. Yang C, Liao S, Yang Z, Guo J, Zhang Z, Yang Y, et al. Rdhcformer: Fusing resdcn and transformers for fetal head circumference automatic measurement in 2d ultrasound images. Front Med (Lausanne). 2022;9:848904. Zhao L, Tan G, Pu B, Wu Q, Ren H, Li K. TransFSM: Fetal anatomy segmentation and biometric measurement in ultrasound images using a hybrid transformer. IEEE J Biomed Health Inform. 2023. Cai L, Li Q, Zhang J, Zhang Z, Yang R, Zhang L. Ultrasound image segmentation based on Transformer and U-Net with joint loss. PeerJ Comput Sci. 2023;9:e1638. Khan U, Nawaz U, Khan M, El Saddik A, Gueaieb W. FETR: A Weakly Self-Supervised Approach for Fetal Ultrasound Anatomical Detection. In: 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE; 2024. p. 1–6. Sarker MMK, Singh VK, Alsharid M, Hernandez-Cruz N, Papageorghiou AT, Noble JA. COMFormer: classification of maternal-fetal and brain anatomy using a residual cross-covariance attention guided transformer in ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control. 2023. Arora U, Sengupta D, Kumar M, Tirupathi K, Sai MK, Hareesh A, et al. Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism. Placenta. 2023;140:109–16. Qiao S, Pang S, Sun Y, Luo G, Yin W, Zhao Y, et al. SPReCHD: Four-chamber semantic parsing network for recognizing fetal congenital heart disease in medical metaverse. IEEE J Biomed Health Inform. 2022. Płotka S, Grzeszczyk MK, Brawura-Biskupski-Samaha R, Gutaj P, Lipa M, Trzciński T, et al. BabyNet: residual transformer module for birth weight prediction on fetal ultrasound video. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2022. p. 350–9. Płotka SS, Grzeszczyk MK, Szenejko PI, Żebrowska K, Szymecka-Samaha NA, Łęgowik T, et al. Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound. Am J Obstet Gynecol MFM. 2023;5(12):101182. Płotka S, Grzeszczyk MK, Brawura-Biskupski-Samaha R, Gutaj P, Lipa M, Trzciński T, et al. BabyNet++: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery. Comput Biol Med. 2023;167:107602. Zhao C, Droste R, Drukker L, Papageorghiou AT, Noble JA. Visual-assisted probe movement guidance for obstetric ultrasound scanning using landmark retrieval. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VIII 24. Springer; 2021. p. 670–9. Thorpe S, Fize D, Marlot C. Speed of processing in the human visual system. Nature. 1996;381(6582):520–2. Sandoval N. Congenital Heart Disease in Colombia and Worldwide. Revista Colombiana de Cardiología. 2015;22(1):e1–2. Abu-Rustum RS, Ziade MF, Abu-Rustum SE. Learning curve and factors influencing the feasibility of performing fetal echocardiography at the time of the first-trimester scan. J Ultrasound Med. 2011 May;30(5):695–700. Reyes-Hernández MU, Bermúdez-Rentería LG, Cifuentes-Fernández EF, Hinojosa-Cruz JC. Desenlaces perinatales adversos en fetos con diagnóstico prenatal de cardiopatía congénita. Ginecol Obstet Mex. 2021;89(07):516–23. Di Cicco V, Abdala D. Diagnóstico prenatal de cardiopatías congénitas. Salud Militar. 2020;39(1):14–9. Sainz JA, Zurita MJ, Guillen I, Borrero C, García-Mejido J, Almeida C, et al. Cribado prenatal de cardiopatías congénitas en población de bajo riesgo de defectos congénitos. Una realidad en la actualidad. In: Anales de Pediatría. Elsevier; 2015. p. 27–34. Mendieta-Alcántara GG, Santiago-Alcántara E, Mendieta-Zerón H, Dorantes-Piña R, Ortiz de Zárate-Alarcón G, Otero-Ojeda GA. Incidencia de las cardiopatías congénitas y los factores asociados a la letalidad en niños nacidos en dos hospitales del Estado de México. Gac Med Mex. 2013;149(6):617–23. Prats P, Ferrer Q, Rodríguez MA, Comas C. Diagnóstico prenatal y evolución de cardiopatías congénitas. Diagnóstico Prenatal. 2011;22(4):128–35. Quiroz L, Siebald E, Belmar C, Urcelay G, Carvajal J. El diagnóstico prenatal de cardiopatías congénitas mejora el pronóstico neonatal. Rev Chil Obstet Ginecol. 2006;71(4):267–73. The Belmont Report \| https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html Declaración de HELSINKI de la AMM. Ruan Y, Xie Z, Liu X, He Y. Associated factors for prenatally diagnosed fetal congenital heart diseases. BMC Cardiovasc Disord. 2023 Jan;23(1):52. Ibáñez-Correa LM, Victoria S, Hurtado-Villa P. Prevalencia de cardiopatías congénitas en una cohorte de 54.193 nacimientos entre 2011-2017. Revista Colombiana de Cardiología. 2021;28(1):53–9. Best KE, Rankin J. Is advanced maternal age a risk factor for congenital heart disease? Birth Defects Res A Clin Mol Teratol. 2016 Jun;106(6):461–7. Wu L, Li N, Liu Y. Association Between Maternal Factors and Risk of Congenital Heart Disease in Offspring: A Systematic Review and Meta-Analysis. Matern Child Health J. 2023 Jan;27(1):29–48. Schulkey CE, Regmi SD, Magnan RA, Danzo MT, Luther H, Hutchinson AK, et al. The maternal-age-associated risk of congenital heart disease is modifiable. Nature. 2015 Apr;520(7546):230–3. Devine PC, Simpson LL. Nuchal translucency and its relationship to congenital heart disease. In: Seminars in Perinatology. Elsevier; 2000. p. 343–51. Bahado-Singh RO, Wapner R, Thom E, Zachary J, Platt L, Mahoney MJ, et al. Elevated first-trimester nuchal translucency increases the risk of congenital heart defects. Am J Obstet Gynecol. 2005;192(5):1357–61. Gonçalves ASL. Nuchal Translucency and Congenital Heart Defects. PQDT-Global. 2023. Jelliffe-Pawlowski LL, Norton ME, Shaw GM, Baer RJ, Flessel MC, Goldman S, et al. Risk of critical congenital heart defects by nuchal translucency norms. Am J Obstet Gynecol. 2015;212(4):518-e1. Simpson LL, Malone F, Bianchi D, Ball R, Nyberg D, Comstock CH, et al. Nuchal translucency and the risk of congenital heart disease—A population-based screening study (the FASTER trial). Am J Obstet Gynecol. 2004;191(6):S3–4. Karadzov Orlic N, Egic A, Damnjanovic‐Pazin B, Lukic R, Joksic I, Mikovic Z. Screening performance of congenital heart defects in first trimester using simple cardiac scan, nuchal translucency, abnormal ductus venosus blood flow and tricuspid regurgitation. Congenit Heart Dis. 2019;14(6):1094–101. Ghanchi A, Rahshenas M, Bonnet D, Derridj N, LeLong N, Salomon LJ, et al. Prevalence of Growth Restriction at Birth for Newborns With Congenital Heart Defects: A Population-Based Prospective Cohort Study EPICARD. Front Pediatr. 2021;9:676994. Wallenstein MB, Harper LM, Odibo AO, Roehl KA, Longman RE, Macones GA, et al. Fetal congenital heart disease and intrauterine growth restriction: a retrospective cohort study. J Matern Fetal Neonatal Med. 2012 Jun;25(6):662–5. Weichert A, Gembicki M, Weichert J, Weber SC, Koenigbauer J. Semi-automatic measurement of fetal cardiac axis in fetuses with congenital heart disease (CHD) with fetal intelligent navigation echocardiography (FINE). J Clin Med. 2023;12(19):6371 Solano M AF, García-Perdomo HA. Incidence of congenital heart disease in fetuses diagnosed with single isolated umbilical artery. Systematic review and meta-analysis. Birth Defects Res. 2024 Jan;116(1):e2296. Vafaeezadeh M, Behnam H, Gifani P. Ultrasound Image Analysis with Vision Transformers. Diagnostics. 2024;14(5):542. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 4510–20. Simonyan K. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770–8. Huhta JC. First-trimester screening for congenital heart disease. Curr Opin Cardiol. 2016 Jan;31(1):72–7.
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spelling	Rodríguez Acosta, NadiezhdaBáez Camacho, Martha LucíaRamírez Mateus, Nataly AlejandraEcheverry Bermúdez, Lina MarcelaEcheverry Bermúdez, Lina Marcela [0000-0001-9102-1664]2025-02-13T15:45:50Z2025-02-13T15:45:50Z2025-01https://hdl.handle.net/20.500.12495/13943instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coObjetivo: analizar la precisión diagnóstica de una arquitectura basada en “Vision Transformer” para la detección prenatal de cardiopatías congénitas (CC) de acuerdo con cada trimestre del embarazo en un país de medianos ingresos. Métodos: se realizó un estudio retrospectivo, observacional y analítico en las pacientes con ecografía obstétrica realizada en la Fundación Santa Fe de Bogotá entre el 2019 y el 2023. Se incluyeron imágenes de corazón fetal con los cortes 4C, 3VT, SVD, SVI. Se entrenó una arquitectura basada en “Vision Transformer” en donde se evaluó el rendimiento en la detección de CC por trimestre de gestación. Resultados: se revisaron 12.402 ecografías y se incluyeron 3.260. Se identificaron 181 ecografías de fetos con CC. La mayoría de las CC se detectaron en el segundo (44,6%) y tercer trimestre (52,4%), siendo la comunicación interventricular la más común. Se utilizaron 2.966 imágenes para el entrenamiento del modelo y 294 para la validación, logrando el mejor rendimiento en el segundo trimestre con una F-medida de 0,61, sensibilidad del 97%, especificidad del 88%, valor predictivo positivo del 90% y valor predictivo negativo del 97%. Conclusiones: la prevalencia de CC anteparto fue de 6,4%, superior a otros estudios, posiblemente por tratarse de un hospital de referencia y remisión dada su alta complejidad. El modelo mostró un buen rendimiento en la detección de CC, con una F1-medida de 61%, destacando el potencial de la inteligencia artificial en regiones con acceso limitado a especialistas para mejorar la precisión en el diagnóstico de CC.Fundación Santa Fe de BogotáUniversidad de Los AndesEspecialista en Ginecología y ObstetriciaEspecializaciónObjectives: to analyze the diagnostic accuracy of a Vision Transformer architecture for prenatal detection of congenital heart diseases (CHD) according to each trimester of pregnancy in a middle-income country. Methods: an observational, retrospective, and analytical study was conducted in patients with obstetric ultrasound performed at the Fundación Santa Fe de Bogotá from 2019 to 2023. Fetal heart images captured in 4C, 3VT, SVD, SVI views were included. A Vision Transformer-based architecture was trained and its performance in the detection of CHD was evaluated by trimester of pregnancy. Results: 12,402 ultrasounds were reviewed and 3,260 were included. 181 ultrasounds of fetuses with CHD were identified. Most of CHDs were detected in the second (44.6%) and third trimesters (52.4%), with ventricular septal defect being the most common. The model was trained on 2,966 images and validated with 294 images, achieving the best performance in the second trimester, with an F-score of 0.61, sensitivity of 97%, specificity of 88%, positive predictive value of 90% and negative predictive value of 97%. Conclusions: the prevalence of antepartum CHD was 6.4%, higher than other studies, possibly because it was a referral hospital given its high complexity. The model demonstrated good performance in CHD detection rate, with a F1-score of 61%, highlighting the potential of artificial intelligence in regions with limited access to specialists to improve accuracy in CHD diagnosis.application/pdfAttribution-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-sa/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2UltrasonografíaInteligencia ArtificialCardiopatía CongénitaDiagnóstico PrenatalUltrasonographyArtificial IntelligenceCongenital Heart DiseasePrenatal DiagnosisWP 100Detección antenatal de cardiopatías congénitas mediante inteligencia artificialPrenatal detection of congenital heart defects using artificial intelligenceEspecialización en Ginecología y ObstetriciaUniversidad El BosqueFacultad de MedicinaTesis/Trabajo de grado - Monografía - Especializaciónhttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aaKarim JN, Bradburn E, Roberts N, Papageorghiou AT. First-trimester ultrasound detection of fetal heart anomalies: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2022 Jan;59(1):11–25.Carvalho JS, Allan LD, Chaoui R, Copel JA, DeVore GR, Hecher K, et al. ISUOG Practice Guidelines (updated): sonographic screening examination of the fetal heart. 2013.Liu Y, Chen S, Zühlke L, Black GC, Choy MK, Li N, et al. Global birth prevalence of congenital heart defects 1970-2017: updated systematic review and meta-analysis of 260 studies. Int J Epidemiol. 2019 Apr;48(2):455–63.Bravo-Valenzuela NJ, Peixoto AB, Araujo Júnior E. Prenatal diagnosis of congenital heart disease: A review of current knowledge. Indian Heart J. 2018;70(1):150–64.Zimmerman M, Sable C. Congenital heart disease in low-and-middle-income countries: Focus on sub-Saharan Africa. Am J Med Genet C Semin Med Genet. 2020 Mar;184(1):36–46.Hopkins MK, Dugoff L, Kuller JA. Congenital Heart Disease: Prenatal Diagnosis and Genetic Associations. 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Curr Opin Cardiol. 2016 Jan;31(1):72–7.spaCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81154https://repositorio.unbosque.edu.co/bitstreams/36684790-074f-4261-bc32-c432dd07099b/downloadadb7af3ef071a784ffe1b544b9a344abMD57TEXTTrabajo de grado.pdf.txtTrabajo de grado.pdf.txtExtracted texttext/plain102941https://repositorio.unbosque.edu.co/bitstreams/c92e8fd0-059e-4315-bc73-7cde46b749bf/downloadc7f0569fdef8ff6cd9b4490b80518f96MD511THUMBNAILTrabajo de grado.pdf.jpgTrabajo de grado.pdf.jpgGenerated Thumbnailimage/jpeg3240https://repositorio.unbosque.edu.co/bitstreams/6a56c7ab-041c-4d6c-9e4a-5ee2cf22d97b/download36ea18b1620f70c3355bbc6085bbc9eeMD512ORIGINALTrabajo de grado.pdfTrabajo de grado.pdfapplication/pdf2173394https://repositorio.unbosque.edu.co/bitstreams/40c02b66-6f8f-4fc8-abe2-861f8ad2f0cf/download54e2424320d5f35f35cbbac831b8a4baMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82000https://repositorio.unbosque.edu.co/bitstreams/1588da1b-6c6a-49c5-893b-e8fd71f19bdc/download17cc15b951e7cc6b3728a574117320f9MD56Carta de autorizacion.pdfapplication/pdf205852https://repositorio.unbosque.edu.co/bitstreams/2b01575e-f53d-4b83-969c-09570d38ac88/download0835149e5a81a4660b07c35fa4391fc9MD58Anexo 1 Acta de aprobacion.pdfapplication/pdf155074https://repositorio.unbosque.edu.co/bitstreams/722eef5e-f61a-4248-b459-a1753313ca7b/download9d33ab83c622258f85577b00a82b8199MD59Anexo 2.pdfapplication/pdf225132https://repositorio.unbosque.edu.co/bitstreams/d5c4f43a-3abf-40ef-9000-7e698808f4e9/downloada147289b41e9ebcc3b3011cd4ba19115MD51020.500.12495/13943oai:repositorio.unbosque.edu.co:20.500.12495/139432025-02-14 03:05:32.885http://creativecommons.org/licenses/by-sa/4.0/Attribution-ShareAlike 4.0 Internationalopen.accesshttps://repositorio.unbosque.edu.coRepositorio Institucional Universidad El Bosquebibliotecas@biteca.comTGljZW5jaWEgZGUgRGlzdHJpYnVjacOzbiBObyBFeGNsdXNpdmEKClBhcmEgcXVlIGVsIFJlcG9zaXRvcmlvIGRlIGxhIFVuaXZlcnNpZGFkIEVsIEJvc3F1ZSBhIHB1ZWRhIHJlcHJvZHVjaXIgeSBjb211bmljYXIgcMO6YmxpY2FtZW50ZSBzdSBkb2N1bWVudG8gZXMgbmVjZXNhcmlvIGxhIGFjZXB0YWNpw7NuIGRlIGxvcyBzaWd1aWVudGVzIHTDqXJtaW5vcy4gUG9yIGZhdm9yLCBsZWEgbGFzIHNpZ3VpZW50ZXMgY29uZGljaW9uZXMgZGUgbGljZW5jaWE6CgoxLiBBY2VwdGFuZG8gZXN0YSBsaWNlbmNpYSwgdXN0ZWQgKGVsIGF1dG9yL2VzIG8gZWwgcHJvcGlldGFyaW8vcyBkZSBsb3MgZGVyZWNob3MgZGUgYXV0b3IpIGdhcmFudGl6YSBhIGxhIFVuaXZlcnNpZGFkIEVsIEJvc3F1ZSBlbCBkZXJlY2hvIG5vIGV4Y2x1c2l2byBkZSBhcmNoaXZhciwgcmVwcm9kdWNpciwgY29udmVydGlyIChjb21vIHNlIGRlZmluZSBtw6FzIGFiYWpvKSwgY29tdW5pY2FyIHkvbyBkaXN0cmlidWlyIHN1IGRvY3VtZW50byBtdW5kaWFsbWVudGUgZW4gZm9ybWF0byBlbGVjdHLDs25pY28uCgoyLiBUYW1iacOpbiBlc3TDoSBkZSBhY3VlcmRvIGNvbiBxdWUgbGEgVW5pdmVyc2lkYWQgRWwgQm9zcXVlIHB1ZWRhIGNvbnNlcnZhciBtw6FzIGRlIHVuYSBjb3BpYSBkZSBlc3RlIGRvY3VtZW50byB5LCBzaW4gYWx0ZXJhciBzdSBjb250ZW5pZG8sIGNvbnZlcnRpcmxvIGEgY3VhbHF1aWVyIGZvcm1hdG8gZGUgZmljaGVybywgbWVkaW8gbyBzb3BvcnRlLCBwYXJhIHByb3DDs3NpdG9zIGRlIHNlZ3VyaWRhZCwgcHJlc2VydmFjacOzbiB5IGFjY2Vzby4KCjMuIERlY2xhcmEgcXVlIGVsIGRvY3VtZW50byBlcyB1biB0cmFiYWpvIG9yaWdpbmFsIHN1eW8geS9vIHF1ZSB0aWVuZSBlbCBkZXJlY2hvIHBhcmEgb3RvcmdhciBsb3MgZGVyZWNob3MgY29udGVuaWRvcyBlbiBlc3RhIGxpY2VuY2lhLiBUYW1iacOpbiBkZWNsYXJhIHF1ZSBzdSBkb2N1bWVudG8gbm8gaW5mcmluZ2UsIGVuIHRhbnRvIGVuIGN1YW50byBsZSBzZWEgcG9zaWJsZSBzYWJlciwgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIGRlIG5pbmd1bmEgb3RyYSBwZXJzb25hIG8gZW50aWRhZC4KCjQuIFNpIGVsIGRvY3VtZW50byBjb250aWVuZSBtYXRlcmlhbGVzIGRlIGxvcyBjdWFsZXMgbm8gdGllbmUgbG9zIGRlcmVjaG9zIGRlIGF1dG9yLCBkZWNsYXJhIHF1ZSBoYSBvYnRlbmlkbyBlbCBwZXJtaXNvIHNpbiByZXN0cmljY2nDs24gZGVsIHByb3BpZXRhcmlvIGRlIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBwYXJhIG90b3JnYXIgYSBsYSBVbml2ZXJzaWRhZCBFbCBCb3NxdWUgbG9zIGRlcmVjaG9zIHJlcXVlcmlkb3MgcG9yIGVzdGEgbGljZW5jaWEsIHkgcXVlIGVzZSBtYXRlcmlhbCBjdXlvcyBkZXJlY2hvcyBzb24gZGUgdGVyY2Vyb3MgZXN0w6EgY2xhcmFtZW50ZSBpZGVudGlmaWNhZG8geSByZWNvbm9jaWRvIGVuIGVsIHRleHRvIG8gY29udGVuaWRvIGRlbCBkb2N1bWVudG8gZW50cmVnYWRvLgoKNS4gU2kgZWwgZG9jdW1lbnRvIHNlIGJhc2EgZW4gdW5hIG9icmEgcXVlIGhhIHNpZG8gcGF0cm9jaW5hZGEgbyBhcG95YWRhIHBvciB1bmEgYWdlbmNpYSB1IG9yZ2FuaXphY2nDs24gZGlmZXJlbnRlIGRlIGxhIFVuaXZlcnNpZGFkIEVsIEJvc3F1ZSwgc2UgcHJlc3Vwb25lIHF1ZSBzZSBoYSBjdW1wbGlkbyBjb24gY3VhbHF1aWVyIGRlcmVjaG8gZGUgcmV2aXNpw7NuIHUgb3RyYXMgb2JsaWdhY2lvbmVzIHJlcXVlcmlkYXMgcG9yIGVzdGUgY29udHJhdG8gbyBhY3VlcmRvLgoKNi4gVW5pdmVyc2lkYWQgRWwgQm9zcXVlIGlkZW50aWZpY2Fyw6EgY2xhcmFtZW50ZSBzdS9zIG5vbWJyZS9zIGNvbW8gZWwvbG9zIGF1dG9yL2VzIG8gcHJvcGlldGFyaW8vcyBkZSBsb3MgZGVyZWNob3MgZGVsIGRvY3VtZW50bywgeSBubyBoYXLDoSBuaW5ndW5hIGFsdGVyYWNpw7NuIGRlIHN1IGRvY3VtZW50byBkaWZlcmVudGUgYSBsYXMgcGVybWl0aWRhcyBlbiBlc3RhIGxpY2VuY2lhLgo=

Detección antenatal de cardiopatías congénitas mediante inteligencia artificial

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