Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula

ilustraciones, gráficas, tablas

Autores:: Padilla Pantoja, Fabio Daniel

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
dc.title.translated.eng.fl_str_mv	Etiologic classification of macular edema using a Deep Learning approach in OCT scans
title	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
spellingShingle	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula 610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiología Macular Edema Artificial Intelligence Tomography, Optical Edema Macular Inteligencia Artificial Tomografía Óptica Machine Learning Artificial intelligence Macular edema Optical coherence tomography Inteligencia artificial Edema macular Tomografía de coherencia óptica
title_short	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
title_full	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
title_fullStr	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
title_full_unstemmed	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
title_sort	Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula
dc.creator.fl_str_mv	Padilla Pantoja, Fabio Daniel
dc.contributor.advisor.spa.fl_str_mv	Quijano Nieto, Bernardo alfonso
dc.contributor.author.spa.fl_str_mv	Padilla Pantoja, Fabio Daniel
dc.contributor.educationalvalidator.spa.fl_str_mv	Perdomo Charry, Oscar Julián González Osorio, Fabio Augusto
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigacion en Oftalmología Básica y Clínica
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiología
topic	610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiología Macular Edema Artificial Intelligence Tomography, Optical Edema Macular Inteligencia Artificial Tomografía Óptica Machine Learning Artificial intelligence Macular edema Optical coherence tomography Inteligencia artificial Edema macular Tomografía de coherencia óptica
dc.subject.decs.eng.fl_str_mv	Macular Edema Artificial Intelligence Tomography, Optical
dc.subject.decs.spa.fl_str_mv	Edema Macular Inteligencia Artificial Tomografía Óptica
dc.subject.proposal.eng.fl_str_mv	Machine Learning Artificial intelligence Macular edema Optical coherence tomography
dc.subject.proposal.spa.fl_str_mv	Inteligencia artificial Edema macular Tomografía de coherencia óptica
description	ilustraciones, gráficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-01-31T19:08:41Z
dc.date.available.none.fl_str_mv	2022-01-31T19:08:41Z
dc.date.issued.none.fl_str_mv	2022-01-30
dc.type.spa.fl_str_mv	Trabajo de grado - Especialidad Médica
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dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80817
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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url	https://repositorio.unal.edu.co/handle/unal/80817 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	Bireme
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Keane PA, Patel PJ, Liakopoulos S, et al. Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol. 2012;57(5):389-414. Kwan CC, Fawzi AA. Imaging and Biomarkers in Diabetic Macular Edema and Diabetic Retinopathy. Curr Diab Rep. 2019;19(10). Lee H, Jang H, Choi YA, et al. Association between soluble cd14 in the aqueous humor and hyperreflective foci on optical coherence tomography in patients with diabetic macular edema. Investig Ophthalmol Vis Sci. 2018;59(2):715-721. Panozzo G, Cicinelli MV, Augustin AJ, et al. An optical coherence tomography- based grading of diabetic maculopathy proposed by an international expert panel: The European School for Advanced Studies in Ophthalmology classification. Eur J Ophthalmol. 2020;30(1):8-18. Yiu G, Welch RJ, Wang Y, et al. Spectral-Domain OCT Predictors of Visual Outcomes after Ranibizumab Treatment for Macular Edema Resulting from Retinal Vein Occlusion. Ophthalmol Retin. 2020;4(1):67-76. Yu C, Xie S, Niu S, et al. Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks. Med Phys. 2019;46(10):4502-4519. Ozer MD, Batur M, Mesen S, et al. Evaluation of the Initial Optical Coherence Tomography Parameters in Anticipating the Final Visual Outcome of Central Retinal Vein Occlusion. J Curr Ophthalmol. 2020;32(1):46-52. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1-29. Currie G, Hawk KE, Rohren E, et al. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci. 2019;50(4):477- 487. Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA - J Am Med Assoc. 2018;320(11):1192-1193. Lakhani P, Gray DL, Pett CR, et al. Hello World Deep Learning in Medical Imaging. J Digit Imaging. 2018;31(3):283-289. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262-e273. Perdomo Charry OJ, González Osorio FA. A Systematic Review of Deep Learning Methods Applied to Ocular Images. Cienc e Ing Neogranadina. 2019;30(1):9-26. Maninis KK, Pont-Tuset J, Arbeláez P, et al. Deep retinal image understanding. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 9901 LNCS. Springer Verlag; 2016:140-148. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digit Med. 2018;1(1). Schlegl T, Waldstein SM, Bogunovic H, et al. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology. 2018;125(4):549-558. Roy AG, Conjeti S, Karri SPK, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express. 2017;8(8):3627. Alsaih K, Yusoff MZ, Tang TB, et al. Retinal Fluids Segmentation Using Volumetric Deep Neural Networks on Optical Coherence Tomography Scans. In: Institute of Electrical and Electronics Engineers (IEEE); 2020:68-72. Gao K, Niu S, Ji Z, et al. Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images. Comput Methods Programs Biomed. 2019;176:69-80. Guan L, Yu K, Chen X. Fully automated detection and quantification of multiple retinal lesions in OCT volumes based on deep learning and improved DRLSE. In: Angelini ED, Landman BA, eds. Medical Imaging 2019: Image Processing. Vol 10949. SPIE; 2019:110. Chakravarthy U, Goldenberg D, Young G, et al. Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration. In: Ophthalmology. Vol 123. Elsevier Inc.; 2016:1731-1736. Ogino K, Murakami T, Tsujikawa A, et al. Characteristics of optical coherence tomographic hyperreflective foci in retinal vein occlusion. Retina. 2012;32(1):77-85. Iannetti L, Spinucci G, Abbouda A, et al. Spectral-Domain Optical Coherence Tomography in Uveitic Macular Edema: Morphological Features and Prognostic Factors. Ophthalmologica. 2012;228(1):13-8 Munk M, Sacu S, Huf W, et al. Differential diagnosis of macular edema of different pathophysiologic origins by spectral domain optical coherence tomography. Retina. 2014;34(11):2218-2232. Hassan B, Qin S, Ahmed R, et al. Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy. Comput Biol Med. 2021;136:104727. Tsuji T, Hirose Y, Fujimori K, et al. Classification of optical coherence tomography images using a capsule network. BMC Ophthalmol. 2020;20(1):114. Karri SP, Chakraborty D, Chatterjee J. Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age- related macular degeneration. Biomed Opt Express. 2017;8(2):579-592. Sanchez YD, Nieto B, Padilla FD, et al. Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans. In: Brieva J, Lepore N, Romero Castro E, Linguraru MG, eds. 16th International Symposium on Medical Information Processing and Analysis. Vol 11583. SPIE; 2020:38. Kermany DS, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018;172(5):1122- 1131.e9. Dataset disponible en http://dx.doi.org/10.17632/rscbjbr9sj.3 Farsiu S, Chiu SJ, O’Connell RV, et al. Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology. 2014;121(1):162-172. Dataset disponible en línea en: https://people.duke.edu/~sf59/RPEDC_Ophth_2013_dataset.htm Zou KH, Warfield SK, Bharatha A, et al. Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index. Acad Radiol. 2004;11(2):178-189. Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2018:7132-7141. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. 31st AAAI Conf Artif Intell AAAI 2017. 2016:4278-4284. Softmax Function Definition \| DeepAI. Recuperado el 28 de noviembre de 2021 de https://deepai.org/machine-learning-glossary-and-terms/softmax-layer. Ministerio de Salud y Protección Social. Resolución 8430 de 1993. Artículo 10, pp. 3. Bogotá DC; 1993. Disponible en: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/DIJ/RE SOLUCION-8430-DE-1993.PDF Asociación Médica Mundial. Declaración de Helsinki de la AMM - Principios éticos para las investigaciones médicas en seres humanos. Fortaleza; 2013. Disponible en: https://www.wma.net/es/policies-post/declaracion-de-helsinki- de-la-amm- principios-eticos-para-las-investigaciones-medicas-en-seres- humanos/ Congreso de la República de Colombia. Ley Estatutaria 1581 de 2012. Artículos 5 y 6, pp. 4. Bogotá DC; 2012. Disponible en: https://www.defensoria.gov.co/public/Normograma%202013_html/Normas/L ey_1581_2012.pdf Organización Panamericana de la Salud (OPS/OMS), Consejo de Organizaciones Internacionales de las Ciencias Médicas (CIOMS). Pautas éticas internacionales para la investigación relacionada con la salud con seres humanos, 4° Edición. [Internet] Ginebra: CIOMS; 2016. Disponible en: cioms.ch/wp-content/uploads/2017/12/CIOMS- EthicalGuideline_SP_INTERIOR- FINAL.pdf Jha D, Smedsrud PH, Riegler MA, et al. ResUNet++: An Advanced Architecture for Medical Image Segmentation. 2019 IEEE Int Symp Multimed. December 2019:225- 2255. Chen Z, Li D, Shen H, et al. Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration. Opt Laser Technol. 2020;122:105830. Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Int J Comput Vis. 2016;128(2):336-359. Li F, Chen H, Liu Z, et al. Deep learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed Opt Express. 2019;10(12):6204. Sundararajan M, Schallhorn JM, Doan T, et al. Changes to ophthalmic clinical care during the coronavirus disease 2019 pandemic. Curr Opin Ophthalmol. 2021;32(6):561-566. Gallardo M, Munk MR, Kurmann T, et al. Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema. Ophthalmol Retin. 2021;5(7):604-624.
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Quijano Nieto, Bernardo alfonso277ca8207ea099aec67446ecb64fe54b600Padilla Pantoja, Fabio Danielbdaafc40a10a6915aab76f437d949c05600Perdomo Charry, Oscar JuliánGonzález Osorio, Fabio AugustoGrupo de Investigacion en Oftalmología Básica y Clínica2022-01-31T19:08:41Z2022-01-31T19:08:41Z2022-01-30https://repositorio.unal.edu.co/handle/unal/80817Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasObjetivo: Desarrollar un método computacional basado en la estrategia de aprendizaje profundo (‘Deep Learning’, DL) para realizar un diagnóstico etiológico automatizado de edema macular (EM) a partir la evaluación de imágenes adquiridas por tomografía de coherencia óptica (OCT), clasificándolas entre edema macular diabético (EMD), degeneración macular exudativa (DMRE(e)) y EM secundario a oclusiones vasculares (EM 2a OVR). Diseño: Desarrollo de algoritmo de inteligencia artificial (IA) para la clasificación automatizada de enfermedades retinianas utilizando datos retrospectivos. Participantes: Se incluyeron 1343 imágenes de OCT de mácula, obtenidas de la base de datos de pacientes atendidos en una Clínica de Oftalmología y de bases de datos de libre acceso, que se utilizaron para entrenar y probar un modelo de inteligencia artificial para detectar EM y su diagnóstico etiológico. Métodos: Las imágenes de OCT fueron marcadas y segmentadas manualmente por dos oftalmólogos expertos, etiquetando biomarcadores (BMs) y clasificándolas en función de la enfermedad correspondiente (EMD, DMRE(e), EM 2a OVR) o como imágenes normales. Se entrenó y validó un modelo de inteligencia artificial usando el 80% de las imágenes y se probó con el 20% de las imágenes restantes. Nuestro método se desarrolló siguiendo cuatro fases consecutivas: segmentación e identificación de BMs, combinación de BMs y predicción de las máscaras, extracción de características mediante aplicación de redes neuronales convolucionales (CNNs) para la clasificación binaria para cada enfermedad y, finalmente, método de clasificación multiclase de las tres enfermedades. Principales medidas de resultados: Exactitud, área bajo la curva (AUC), sensibilidad y especificidad. Resultados: La exactitud diagnóstica lograda por el modelo para clasificar DMRE(e) fue 0.965, y para EMD, EM 2a OVR y controles, los valores fueron 0.94, 0.93 y 0.925, respectivamente. Los valores de área bajo la curva para EMD, DMRE(e), EM 2a OVR e imágenes controles fueron 0.99, 0.98, 0.96 y 0.97, respectivamente. Los valores de sensibilidad y especificidad para la clasificación de las tres enfermedades exudativas retinianas y para imágenes normales fueron comparables con el desempeño de un oftalmólogo experto, de acuerdo con lo reportado en la literatura. Conclusión: El modelo automatizado propuesto con enfoque de DL puede identificar imágenes normales y con edema macular a partir de escaneos adquiridos por OCT de mácula, y permite clasificar su causa entre las tres principales enfermedades exudativas retinianas con alta precisión y confiabilidad. (Texto tomado de la fuente).Purpose: To develop a computational method based on Deep Learning (DL) to automatically make an etiological diagnosis of macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans, by classifying the images between diabetic macular edema (DME) and ME caused by neovascular age-related macular degeneration (nAMD) and retinal vein occlusion (RVO). Design: Algorithm development for retinal disease classification using retrospective data. Participants: A total of 1343 OCT scans, obtained from data repositories of patients attended in an Ophthalmology Clinic and open-access databases, were used to train and test an artificial intelligence (AI) model to detect ME and its etiological diagnosis. Methods: The OCT scans were manually annotated with biomarkers (BMs) and labeled with disease (DME, nAMD, RVO) or control, by two expert ophthalmologists. A DL model was trained and validated using 80% of the images and tested on the remaining 20% of them. Our method was developed by following four consecutive phases: segmentation and identification of BMs, combination of BMs and mask predictions, feature extraction with convolutional neural networks (CNNs) to achieve binary classification for each disease and, finally, multiclass classification of three diseases and control images. Main Outcome Measures: Accuracy, area under the curve (AUC), sensitivity and specificity. Results: The classification accuracy of the model for nAMD was 0.97, and for DME, RVO associated with ME and control, the values were 0.94, 0.93 and 0.93, respectively. AUC values were 0.99, 0.98, 0.96 and 0.97 respectively. Sensitivity and specificity were comparable with the performance of an expert ophthalmologist, according to the literature. Conclusion: The proposed DL model may identify normal images and ME from OCT scans and classify its cause between three major exudative retinal diseases with high accuracy and reliability.Especialidades MédicasEspecialista en OftalmologíaInteligencia artificial en oftalmologíaxiii, 50 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Especialidad en OftalmologíaDepartamento de CirugíaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiologíaMacular EdemaArtificial IntelligenceTomography, OpticalEdema MacularInteligencia ArtificialTomografía ÓpticaMachine LearningArtificial intelligenceMacular edemaOptical coherence tomographyInteligencia artificialEdema macularTomografía de coherencia ópticaClasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de máculaEtiologic classification of macular edema using a Deep Learning approach in OCT scansTrabajo de grado - Especialidad Médicainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBiremeDaruich A, Matet A, Moulin A, et al. 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Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema. Ophthalmol Retin. 2021;5(7):604-624.EstudiantesInvestigadoresMaestrosMedios de comunicaciónPúblico generalORIGINAL1085309363.2022.pdf1085309363.2022.pdfTesis de Especialidad en Oftalmologíaapplication/pdf5896745https://repositorio.unal.edu.co/bitstream/unal/80817/3/1085309363.2022.pdf33e89236dd3922ebcceff2de21a41d5fMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80817/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1085309363.2022.pdf.jpg1085309363.2022.pdf.jpgGenerated Thumbnailimage/jpeg5386https://repositorio.unal.edu.co/bitstream/unal/80817/5/1085309363.2022.pdf.jpg8d796cf9a42acf53c9368da9c3912bb8MD55unal/80817oai:repositorio.unal.edu.co:unal/808172024-08-02 23:10:44.08Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

Clasificación etiológica automatizada de edema macular mediante estrategia de ‘Deep Learning’ aplicada en imágenes adquiridas por OCT de mácula

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