Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis
Antecedentes: La periodontitis es una enfermedad inflamatoria crónica que causa la destrucción progresiva de los tejidos de soporte de los dientes. El sistema de clasificación de 2018, propuesto por el World Workshop on the Classification of Periodontal and Peri-Implant Diseases, proporciona un méto...
- Autores:
-
Diaz Rios, Sonia Rocío
Escobar Minotas, Isabel
Sabogal Arguello, Camilo Andres
- 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
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/14211
- Acceso en línea:
- https://hdl.handle.net/20.500.12495/14211
- Palabra clave:
- Aprendizaje automático
Periodontitis
Clasificación por estadios
Algoritmo diagnóstico
Estudio de validación.
Machine Learning
Periodontitis
Stage classification
Diagnostic algorithm
Validation study
WU 240
- Rights
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
| id |
UNBOSQUE2_913ccaad03fb0c56f2411ca0b4a1a288 |
|---|---|
| oai_identifier_str |
oai:repositorio.unbosque.edu.co:20.500.12495/14211 |
| network_acronym_str |
UNBOSQUE2 |
| network_name_str |
Repositorio U. El Bosque |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| dc.title.translated.none.fl_str_mv |
Validation of a Machine Learning Algorithm for Stage Classification of periodontitis |
| title |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| spellingShingle |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis Aprendizaje automático Periodontitis Clasificación por estadios Algoritmo diagnóstico Estudio de validación. Machine Learning Periodontitis Stage classification Diagnostic algorithm Validation study WU 240 |
| title_short |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| title_full |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| title_fullStr |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| title_full_unstemmed |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| title_sort |
Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitis |
| dc.creator.fl_str_mv |
Diaz Rios, Sonia Rocío Escobar Minotas, Isabel Sabogal Arguello, Camilo Andres |
| dc.contributor.advisor.none.fl_str_mv |
Lafaurie Villamil, Gloria Ines Bermúdez Munar, José Alejandro Duitama Leal, Alejandro |
| dc.contributor.author.none.fl_str_mv |
Diaz Rios, Sonia Rocío Escobar Minotas, Isabel Sabogal Arguello, Camilo Andres |
| dc.subject.none.fl_str_mv |
Aprendizaje automático Periodontitis Clasificación por estadios Algoritmo diagnóstico Estudio de validación. |
| topic |
Aprendizaje automático Periodontitis Clasificación por estadios Algoritmo diagnóstico Estudio de validación. Machine Learning Periodontitis Stage classification Diagnostic algorithm Validation study WU 240 |
| dc.subject.keywords.none.fl_str_mv |
Machine Learning Periodontitis Stage classification Diagnostic algorithm Validation study |
| dc.subject.nlm.none.fl_str_mv |
WU 240 |
| description |
Antecedentes: La periodontitis es una enfermedad inflamatoria crónica que causa la destrucción progresiva de los tejidos de soporte de los dientes. El sistema de clasificación de 2018, propuesto por el World Workshop on the Classification of Periodontal and Peri-Implant Diseases, proporciona un método estandarizado para diagnosticar la periodontitis basado en etapas y grados. Sin embargo, persisten desafíos para garantizar la consistencia diagnóstica entre los clínicos. Este estudio valida un algoritmo de aprendizaje automático diseñado para clasificar de manera precisa y eficiente las etapas de la periodontitis, ofreciendo una herramienta potencial de apoyo al diagnóstico. Objetivo: Validar un algoritmo de aprendizaje automático para la clasificación por etapas de la periodontitis basado en los criterios de clasificación de 2018, con énfasis en la precisión, exactitud y consistencia en el diagnóstico clínico. Métodos: Se realizó un estudio analítico transversal para validar el algoritmo propuesto, el cual utiliza aprendizaje automático para clasificar casos. Expertos clasificaron manualmente 90 casos de periodontitis utilizando datos clínicos. Este conjunto de datos se utilizó para entrenar y probar el algoritmo. Se emplea el coeficiente Kappa de Cohen para evaluar la concordancia entre las clasificaciones manuales y automáticas. Además, se calcularán la precisión, sensibilidad y especificidad para evaluar el rendimiento del algoritmo. Resultados: El algoritmo mejoró sustancialmente la precisión (>95 %) en la clasificación de la periodontitis por etapas. La edad y el número de dientes fueron los factores más importantes para establecer las etapas de la periodontitis. El coeficiente Kappa de Cohen para datos categóricos ordinales evaluará la concordancia entre las clasificaciones del algoritmo y las de los expertos. Se espera que la precisión supere el 90 % en todas las etapas, observándose la mayor exactitud en las etapas avanzadas (III y IV). Conclusiones: El algoritmo de aprendizaje automático validado demuestra una alta precisión y fiabilidad en la clasificación por etapas de la periodontitis, proporcionando una herramienta valiosa para los clínicos. Su implementación puede agilizar el diagnóstico, reducir la variabilidad entre clínicos y respaldar la toma de decisiones basada en evidencia. Las investigaciones futuras deberían explorar su aplicación en contextos clínicos diversos y con conjuntos de datos más amplios. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-03-31T13:08:14Z |
| dc.date.available.none.fl_str_mv |
2025-03-31T13:08:14Z |
| dc.date.issued.none.fl_str_mv |
2025-01 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.local.spa.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Especialización |
| dc.type.coar.none.fl_str_mv |
https://purl.org/coar/resource_type/c_7a1f |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.coarversion.none.fl_str_mv |
https://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
https://purl.org/coar/resource_type/c_7a1f |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12495/14211 |
| dc.identifier.instname.spa.fl_str_mv |
instname:Universidad El Bosque |
| dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad El Bosque |
| dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.unbosque.edu.co |
| url |
https://hdl.handle.net/20.500.12495/14211 |
| 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 |
Akram Z, Safii SH, Vaithilingam RD, Baharuddin NA, Javed F, Vohra F. Efficacy of non-surgical periodontal therapy in the management of chronic periodontitis among obese and non-obese patients: a systematic review and meta-analysis. Clin Oral Investig. 2016 jun;20(5):903-14. doi: 10.1007/s00784-016-1793-4. Epub 2016 Mar 23. PMID: 27005812. Albandar, J. M., Susin, C., & Hughes, F. J. (2018). Manifestations of systemic diseases and conditions that affect the periodontal attachment apparatus: Case definitions and diagnostic considerations. Journal of clinical periodontology, 45, S171-S189. Araújo, A. L. D., da Silva, V. M., Kudo, M. S., de Souza, E. S. C., Saldivia-Siracusa, C., Giraldo- Roldán, D., ... & Moraes, M. C. (2023). Machine Learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach. Journal of Oral Pathology & Medicine. Arsiwala-Scheppach, L. T., Chaurasia, A., Müller, A., Krois, J., & Schwendicke, F. (2023). Machine Learning in dentistry: a scoping review. Journal of Clinical Medicine. Barbi W, Kumar S, Sinha S, Askari M, Priya S, Kumar SJ. Reliability of C-reactive Protein as a Biomarker for Cardiovascular and Oral Diseases in Young and Old Subjects. J Pharm Bioallied Sci. 2021 nov;13(Suppl 2): S1458-S1461. doi: 10.4103/jpbs.jpbs_251_21. Epub 2021 Nov 10. PMID: 35018009; PMCID: PMC8686985. Barbosa VL, Angst PD, Finger Stadler A, Oppermann RV, Gomes SC. Clinical attachment loss: estimation by direct and indirect methods. Int Dent J. 2016 Jun;66(3):144-9. doi: 10.1111/idj.12218. Epub 2016 Feb 5. PMID: 26846817; PMCID: PMC9376648 Baser U, Oztekin G, Ademoglu E, Isik G, Yalcin F. Is the severity of periodontitis related to gingival crevicular fluid and serum high-sensitivity C-reactive protein concentrations? Clin Lab. 2014;60(10):1653-8. doi: 10.7754/clin.lab.2014.131217. PMID: 25651710. Bashir, N. Z., Rahman, Z., & Chen, S. L. S. (2022). Systematic comparison of Machine Learning algorithms to develop and validate predictive models for periodontitis. Journal of Clinical Periodontology, 49(10), 958-969. Botero JE, Zuluaga AI, Suárez-Córdoba V, Calzada MT, Gutiérrez-Quiceno B, Gutiérrez AF, Mateus-Londoño N. Using Machine Learning to study the association of sociodemographic indicators, biomarkers, and oral condition in older adults in Colombia. J Am Dent Assoc. 2023 Aug;154(8):715-726.e5. doi: 10.1016/j.adaj.2023.04.017. PMID: 37500234. Chapple ILC, Mealey BL, Van Dyke TE, Bartold PM, Dommisch H, Eickholz P, Geisinger ML, Genco RJ, Glogauer M, Goldstein M, Griffin TJ, Holmstrup P, Johnson GK, Kapila Y, Lang NP, Meyle J, Murakami S, Plemons J, Romito GA, Shapira L, Tatakis DN, Teughels W, Trombelli L, Walter C, Wimmer G, Xenoudi P, Yoshie H. Periodontal health and gingival diseases and conditions on an intact and a reduced periodontium: Consensus report of workgroup 1 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Periodontol. 2018 jun;89 Suppl 1: S74-S84. doi: 10.1002/JPER.17-0719. PMID: 29926944. Chatzopoulos, G. S., Jiang, Z., Marka, N., & Wolff, L. F. (2023). Association between periodontitis extent, severity, and progression rate with systemic diseases and smoking: A retrospective study. Journal of Personalized Medicine, 13(5), 814. Cooray U, Watt RG, Tsakos G, Heilmann A, Hariyama M, Yamamoto T, Kuruppuarachchige I, Kondo K, Osaka K, Aida J. Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a Machine Learning analysis. Soc Sci Med. 2021 Dec; 291:114486. doi: 10.1016/j.socscimed.2021.114486. Epub 2021 Oct 13. PMID: 34700121. Du, M., Bo, T., Kapellas, K., & Peres, M. A. (2018). Prediction models for the incidence and progression of periodontitis: A systematic review. Journal of clinical periodontology, 45(12), 1408-1420. Duarte, P. M., Nogueira, C. F. P., Silva, S. M., Pannuti, C. M., Schey, K. C., & Miranda, T. S. (2021). Impact of smoking cessation on periodontal tissues. Journal of Dental Research. Gomes-Filho, I. S., Santos, P. N. P., Cruz, S. S., Figueiredo, A. C. M. G., Trindade, S. C., Ladeia, A. M., ... Scannapieco, F. A. (2021). Periodontitis and its higher levels of severity are associated with the triglyceride/high density lipoprotein cholesterol ratio. Journal of Periodontology, 92(12), 1509--1521. https://doi.org/10.1002/JPER.21-0004 Goswami S, Ylöstalo P, Khan S, Knuuttila M, Bernabe E, Suominen AL. Effect of smoking on periodontal health and validation of self-reported smoking status with serum cotinine levels. Acta Odontol Scand. 2021 nov;79(8):573-581. doi: 10.1080/00016357.2021.1917655. Epub 2021 May 8. PMID: 33966590. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer. Haworth, S., Shungin, D., Kwak, S. Y., Kim, H. Y., West, N. X., Thomas, S. J., Franks, P. W., Timpson, N. J., Shin, M. J., & Johansson, I. (2018). Tooth loss is a complex measure of oral disease: Determinants and methodological considerations. Community dentistry and oral epidemiology, 46(6), 555--562. https://doi.org/10.1111/cdoe.12391. Heitz-Mayfield LJ. Disease progression: identification of high-risk groups and individuals for periodontitis. J Clin Periodontol. 2005;32 Suppl 6:196-209. doi: 10.1111/j.1600- 051X.2005.00803.x. PMID: 16128838. Herrera, D.; Figuero, E.; Shapira, L.; Jin, L. & Sanz, M. La Nueva clasificación de enfermedades periodontales y periimplantares. Rev. Cient. Soc. Esp. Periodoncia, 11, 201. Huang, Y. Q., Xu, J. N., Huang, Y., Xu, Y. D., Wang, H. L., Shi, W. T., Wang, J., & Wang, H. (2024). Independent and combined effects of smoking, drinking and depression on periodontal disease. BMC Oral Health, 24, 535. Jaramillo A, Contreras A, Lafaurie GI, Duque A, Ardila CM, Duarte S, Osorio L. Association of metabolic syndrome and chronic periodontitis in Colombians. Clin Oral Investig. 2017 jun;21(5):1537-1544. doi: 10.1007/s00784-016-1942-9. Jundaeng J, Chamchong R, Nithikathkul C. Periodontitis Diagnosis: A Review of Current and Future Trends in Artificial Intelligence. J Esthet Restor Dent. 2022 Jan;34(1):259-280. doi:10.1111/jerd.12844. PMID: 34842324. Kharroubi AT, Darwish HM. Diabetes mellitus: The epidemic of the century. World J Diabetes. 2015 jun 25;6(6):850-67. doi: 10.4239/wjd. v6.i6.850. PMID: 26131326; PMCID: PMC4478580. Klonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol. 2023;17(5):1226-1242. doi:10.1177/19322968221085273. Kim, E.-K., Cho, J.-Y., & Park, E. Y. (2023). Association between periodontal disease and hypertriglyceridemia. Medicine, 102(51), 1-8. DOI: 10.1097/MD.0000000000036502 Kinane DF, Mooney J, Ebersole JL. Humoral immune response to Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis in periodontal disease. Periodontol 2000. 1999 jun; 20:289-340. doi: 10.1111/j.1600-0757. 1999.tb00164. x. PMID: 10522229. Lafaurie, G. I., Sabogal, M. A., Castillo, D. M., Gualtero, D. F., De Avila, J., Trujillo, T. G., Ardila, C. M., Duque, A., Giraldo, A., Duarte, S., Gutiérrez, S. J., & Contreras, A. (Year). Factors associated with increasing clinical attachment loss in chronic periodontitis: A cross-sectional study. Lakio L, Antinheimo J, Paju S, Buhlin K, Pussinen PJ, Alfthan G. Tracking of plasma antibodies against Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis during 15 years. J Oral Microbiol. 2009 Aug 3;1. doi: 10.3402/jom. v1i0.1979. PMID: 21523211; PMCID: PMC3077000. Lee, Y-C., Lee, J-W., & Kwon, Y-J. (2022). Comparison of the triglyceride glucose (TyG) index, triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, and metabolic score for insulin resistance (METS-IR) associated with periodontitis in Korean adults. Therapeutic Advances in Chronic Disease, 13, 1--15. https://doi.org/10.1177/20406223221122671. Li, Y., Xu, Y., et al. (2021). Asociación entre el índice triglicéridos-glucosa y la periodontitis: un estudio de la Encuesta Nacional de Salud y Nutrición de Estados Unidos. International Journal of General Medicine, 14. https://doi.org/10.2147/IJGM.S339863. Liu C, Shi F, Li W, Chen J. Efficacy of non-surgical periodontal treatment on patients with coronary artery disease: a meta-analysis of randomized controlled trials. Med Oral Patol Oral Cir Bucal. 2022 nov 1;27(6): e578-e587. doi: 10.4317/medoral.25514. PMID: 36243997; PMCID: PMC9648645. Lopez-Jaramillo, P., Gomez-Arbelaez, D., Martinez-Bello, D., Abat, M. E. M., Alhabib, K. F., Avezum, Á., ... Yusuf, S. (2023). Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. The Lancet Healthy Longevity, 4, e23- e33. https://doi.org/10.1016/S2666-7568(22)00247-1. Martínez-García M, Castrejón-Pérez RC, Rodríguez-Hernández AP, Sandoval-Motta S, Vallejo M, Borges-Yáñez SA, Hernández-Lemus E. Incidence of Arterial Hypertension in People With Periodontitis and Characterization of the Oral and Subgingival Microbiome: A Study Protocol. Front Cardiovasc Med. 2022. Mattos M, Barbosa JC, Almeida A, et al. Expresión de mediadores inflamatorios en pacientes con periodontitis y diabetes tipo 2: una revisión sistemática y metaanálisis. Braz Oral Res. 2022;36:e0098. doi:10.1590/1807-3107bor-2022.vol36.0098. Disponible en: https://www.scielo.br/j/bor/a/9j3YQ3TfGxCrX49TdvpVN5j/ Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020 Apr;16(4):223-237. doi: 10.1038/s41581-019-0244-2. Epub 2020 Feb 5. PMID: 32024986; PMCID: PMC7998524. Nascimento GG, Leite FRM, Vestergaard P, Scheutz F, López R. Does diabetes increase the risk of periodontitis? A systematic review and meta-regression analysis of longitudinal prospective studies. Acta Diabetol. 2018 jul;55(7):653-667. doi: 10.1007/s00592-018-1120- 4. Epub 2018 Mar 3. PMID: 29502214. Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today. 2015 May;50(3):117-128. doi: 10.1097/NT.0000000000000092. Epub 2015 Apr 7. PMID: 27340299; PMCID: PMC4890841. OMS. Enfermedades cardiovasculares. Revisado en http://www.who.int/mediacentre/factsheets/fs317/es/ Papapanou PN, Sanz M, Buduneli N, Dietrich T, Feres M, Fine DH, Flemmig TF, Garcia R, Giannobile WV, Graziani F, Greenwell H, Herrera D, Kao RT, Kebschull M, Kinane DF, Kirkwood KL, Kocher T, Kornman KS, Kumar PS, Loos BG, Machtei E, Meng H, Mombelli A, Needleman I, Offenbacher S, Seymour GJ, Teles R, Tonetti MS. Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Periodontol. 2018 jun;89 Suppl 1: S173-S182. doi: 10.1002/JPER.17-0721. PMID: 29926951. Papapanou, PN, Sanz, M, et al. Periodontitis: Consensus report of Workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Clin Periodontol. 2018; 45(Suppl 20): S162-- S170 Peng J, Song J, Han J, Chen Z, Yin X, Zhu J, Song J. The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: systematic review and dose-response meta-analysis of prospective cohort studies. Biosci Rep. 2019 Jan 11;39(1): BSR20181773. doi: 10.1042/BSR20181773. PMID: 30530864; PMCID: PMC6328868. Parihar AS, Narang S, Tyagi S, Narang A, Dwivedi S, Katoch V, Laddha R. Artificial Intelligence in Periodontics: A Comprehensive Review. J Pharm Bioallied Sci. 2024 May 13;16(Suppl 3):S1956–S1958. doi: 10.4103/jpbs.jpbs_129_24. PMID: 39346158; PMCID: PMC11426892. Polizzi A, Quinzi V, Lo Giudice A, Marzo G, Leonardi R, Isola G. (2024) Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review. JDR Clin Trans. doi: 10.1177/23800844241232318. Raschka, S. (2015). Python Machine Learning. Packt publishing ltd. Revilla-León, M., Gómez-Polo, M., Barmak, A. B., Inam, W., Kan, J. Y. K., Kois, J. C., & Akal, O. (2023). Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. The Journal of Prosthetic Dentistry, 130(6), 816--824. Sanz M, Marco Del Castillo A, Jepsen S, Gonzalez-Juanatey JR, D'Aiuto F, Bouchard P, Chapple I, Dietrich T, Gotsman I, Graziani F, Herrera D, Loos B, Madianos P, Michel JB, Perel P, Pieske B, Shapira L, Shechter M, Tonetti M, Vlachopoulos C, Wimmer G. Periodontitis and cardiovascular diseases: Consensus report. J Clin Periodontol. 2020 Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021;2(3):160. doi:10.1007/s42979-021-00592-x Schmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, Volume 61, January 2015, Pages 85-117 (DOI: 10.1016/j.neunet.2014.09.003 Scott J, Biancardi AM, Jones O, Andrew D. Artificial Intelligence in Periodontology: A Scoping Review. Dent J. 2023;11(2):43. https://doi.org/10.3390/dj11020043 Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomark Insights. 2016 jul 3; 11:95-104. doi: 10.4137/BMI.S38440. PMID: 27398023; PMCID: PMC4933534. Stöhr J, Barbaresko J, Neuenschwander M, Schlesinger S. Bidirectional association between periodontal disease and diabetes mellitus: a systematic review and meta-analysis of cohort studies. Sci Rep. 2021 Jul 1;11(1):13686. doi: 10.1038/s41598-021-93062-6. PMID: 34211029; PMCID: PMC8249442. Su N, Teeuw WJ, Loos BG, Kosho MXF, van der Heijden GJMG. Development and validation of a screening model for diabetes mellitus in patients with periodontitis in dental settings. Clin Oral Investig. 2020 nov;24(11):4089-4100. doi: 10.1007/s00784-020-03281-w. Epub 2020 Jun 15. PMID: 32542584; PMCID: PMC7544748. Sun et al., "Application of Machine Learning to Stomatology: A Comprehensive Review," in IEEE Access, vol. 8, pp. 184360-184374, 2020, doi: 10.1109/ACCESS.2020.3028600. Tonetti MS. Periodontitis and risk for atherosclerosis: an update on intervention trials. J Clin Periodontol. 2009 jul;36 Suppl 10:15-9. doi: 10.1111/j.1600-051X.2009.01417. x. PMID: 19432627. Tonetti, M. S.; Greenwell, H. & Kornman, K. S. Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J. Periodontol., 89 Suppl. 1: S159-72, 2018. Zatt, F. P., Cordeiro, J. V. C., Bohner, L., Souza, B. D. M. de, Caldas, V. E. A., & Caldas, R. A. (2024). Using Machine Learning to classify temporomandibular disorders: A proof of concept. Journal of Applied Oral Science, 32 Zhang Z. A gentle introduction to artificial neural networks. Ann Transl Med 2016; 4: 370. Zhu, H., Ye, G., Xie, Y., Zhu, K., Zhu, F., & Chen, Q. (2022). Association of high-density lipoprotein cholesterol and periodontitis severity in Chinese elderly: a cross-sectional study. Clinical Oral Investigations. https://doi.org/10.1007/s00784-022-04439-4 |
| dc.rights.en.fl_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.local.spa.fl_str_mv |
Acceso abierto |
| dc.rights.accessrights.none.fl_str_mv |
https://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ Acceso abierto https://purl.org/coar/access_right/c_abf2 http://purl.org/coar/access_right/c_abf2 |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.program.spa.fl_str_mv |
Especialización en periodoncia y medicina oral |
| dc.publisher.grantor.spa.fl_str_mv |
Universidad El Bosque |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Odontología |
| institution |
Universidad El Bosque |
| bitstream.url.fl_str_mv |
https://repositorio.unbosque.edu.co/bitstreams/e7fc7c83-407a-44ca-bb4e-0837f18ba0cd/download https://repositorio.unbosque.edu.co/bitstreams/1677e4a2-da72-4997-bc6a-fb77ab778b07/download https://repositorio.unbosque.edu.co/bitstreams/2ff158f1-73a1-4281-97c7-97f88be5c5f9/download https://repositorio.unbosque.edu.co/bitstreams/f915779c-4d4a-4b07-ba57-d4507b200be3/download https://repositorio.unbosque.edu.co/bitstreams/7417ff39-c310-4e6c-8737-6fa81b828aad/download https://repositorio.unbosque.edu.co/bitstreams/f448a5a6-baec-4d8e-a1ac-be62430194d4/download https://repositorio.unbosque.edu.co/bitstreams/237a87be-68ca-48c0-9c45-fd9be20a0b76/download https://repositorio.unbosque.edu.co/bitstreams/bba91e6b-70b5-41af-8b08-841337f5bf86/download |
| bitstream.checksum.fl_str_mv |
17cc15b951e7cc6b3728a574117320f9 2732878e5bac158d632516fc55a3e6a1 6f6c2bb848d99a9cb68aab4b88bcb2c5 d146cbbc76d2cef619074bff0ace8dca a23b69f9a107f9ac19be28374a9e7f60 5643bfd9bcf29d560eeec56d584edaa9 908aa0f1ed71afd47ad696fbf2e497a5 3de840c8c7fd7ba3abe0470b195d9f15 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad El Bosque |
| repository.mail.fl_str_mv |
bibliotecas@biteca.com |
| _version_ |
1831931381121286144 |
| spelling |
Lafaurie Villamil, Gloria InesBermúdez Munar, José AlejandroDuitama Leal, AlejandroDiaz Rios, Sonia RocíoEscobar Minotas, IsabelSabogal Arguello, Camilo Andres2025-03-31T13:08:14Z2025-03-31T13:08:14Z2025-01https://hdl.handle.net/20.500.12495/14211instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coAntecedentes: La periodontitis es una enfermedad inflamatoria crónica que causa la destrucción progresiva de los tejidos de soporte de los dientes. El sistema de clasificación de 2018, propuesto por el World Workshop on the Classification of Periodontal and Peri-Implant Diseases, proporciona un método estandarizado para diagnosticar la periodontitis basado en etapas y grados. Sin embargo, persisten desafíos para garantizar la consistencia diagnóstica entre los clínicos. Este estudio valida un algoritmo de aprendizaje automático diseñado para clasificar de manera precisa y eficiente las etapas de la periodontitis, ofreciendo una herramienta potencial de apoyo al diagnóstico. Objetivo: Validar un algoritmo de aprendizaje automático para la clasificación por etapas de la periodontitis basado en los criterios de clasificación de 2018, con énfasis en la precisión, exactitud y consistencia en el diagnóstico clínico. Métodos: Se realizó un estudio analítico transversal para validar el algoritmo propuesto, el cual utiliza aprendizaje automático para clasificar casos. Expertos clasificaron manualmente 90 casos de periodontitis utilizando datos clínicos. Este conjunto de datos se utilizó para entrenar y probar el algoritmo. Se emplea el coeficiente Kappa de Cohen para evaluar la concordancia entre las clasificaciones manuales y automáticas. Además, se calcularán la precisión, sensibilidad y especificidad para evaluar el rendimiento del algoritmo. Resultados: El algoritmo mejoró sustancialmente la precisión (>95 %) en la clasificación de la periodontitis por etapas. La edad y el número de dientes fueron los factores más importantes para establecer las etapas de la periodontitis. El coeficiente Kappa de Cohen para datos categóricos ordinales evaluará la concordancia entre las clasificaciones del algoritmo y las de los expertos. Se espera que la precisión supere el 90 % en todas las etapas, observándose la mayor exactitud en las etapas avanzadas (III y IV). Conclusiones: El algoritmo de aprendizaje automático validado demuestra una alta precisión y fiabilidad en la clasificación por etapas de la periodontitis, proporcionando una herramienta valiosa para los clínicos. Su implementación puede agilizar el diagnóstico, reducir la variabilidad entre clínicos y respaldar la toma de decisiones basada en evidencia. Las investigaciones futuras deberían explorar su aplicación en contextos clínicos diversos y con conjuntos de datos más amplios.Grupo de Investigación UIBO-Unidad de Investigación Básica OralEspecialista en periodoncia y medicina oralEspecializaciónBackground: periodontitis is a chronic inflammatory disease that causes progressive destruction of the supporting tissues of teeth. The 2018 classification system proposed by the world workshop on the classification of periodontal and peri-implant diseases provides a standardized method to diagnose periodontitis based on stages and grades. However, challenges persist in ensuring diagnostic consistency among clinicians. This study validates a Machine Learning algorithm designed to classify periodontitis stages accurately and efficiently, offering a potential diagnostic support tool. Objective: to validate a Machine Learning algorithm for stage classification of periodontitis based on the 2018 classification criteria, focusing on accuracy, precision, and consistency in clinical diagnosis. Methods: a cross-sectional analytical study was conducted to validate the proposed algorithm, which uses Machine Learning to clarify cases. Experts manually classified 90 periodontitis cases using clinical data. The algorithm was trained and tested using this dataset. Cohen's kappa coefficient will evaluate the concordance between manual and automated classifications. Precision, sensitivity, and specificity will be calculated to assess the algorithm's performance. Results: the algorithm substantially improved accuracy (>95%) in classifying periodontitis by stage. The age and tooth number were the most critical factors in establishing the stages of periodontitis. Cohen’s kappa coefficient for ordinal categorical data will assess the concordance between algorism and expert classifications. We hope the precision exceeded 90% across all stages, with the highest accuracy observed in advanced stages (iii and iv). Conclusions: the validated Machine Learning algorithm demonstrates high accuracy and reliability in classifying periodontitis stages, providing a valuable tool for clinicians. Its implementation can streamline diagnosis, reduce inter-clinician variability, and support evidence-based decision-making. Future research should explore its application across diverse clinical contexts and larger datasets.application/pdfAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Aprendizaje automáticoPeriodontitisClasificación por estadiosAlgoritmo diagnósticoEstudio de validación.Machine LearningPeriodontitisStage classificationDiagnostic algorithmValidation studyWU 240Validación de un algoritmo de Machine Learning para la clasificación por estadios de la periodontitisValidation of a Machine Learning Algorithm for Stage Classification of periodontitisEspecialización en periodoncia y medicina oralUniversidad El BosqueFacultad de OdontologíaTesis/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_970fb48d4fbd8a85Akram Z, Safii SH, Vaithilingam RD, Baharuddin NA, Javed F, Vohra F. Efficacy of non-surgical periodontal therapy in the management of chronic periodontitis among obese and non-obese patients: a systematic review and meta-analysis. Clin Oral Investig. 2016 jun;20(5):903-14. doi: 10.1007/s00784-016-1793-4. Epub 2016 Mar 23. PMID: 27005812.Albandar, J. M., Susin, C., & Hughes, F. J. (2018). Manifestations of systemic diseases and conditions that affect the periodontal attachment apparatus: Case definitions and diagnostic considerations. Journal of clinical periodontology, 45, S171-S189.Araújo, A. L. D., da Silva, V. M., Kudo, M. S., de Souza, E. S. C., Saldivia-Siracusa, C., Giraldo- Roldán, D., ... & Moraes, M. C. (2023). Machine Learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach. Journal of Oral Pathology & Medicine.Arsiwala-Scheppach, L. T., Chaurasia, A., Müller, A., Krois, J., & Schwendicke, F. (2023). Machine Learning in dentistry: a scoping review. Journal of Clinical Medicine.Barbi W, Kumar S, Sinha S, Askari M, Priya S, Kumar SJ. Reliability of C-reactive Protein as a Biomarker for Cardiovascular and Oral Diseases in Young and Old Subjects. J Pharm Bioallied Sci. 2021 nov;13(Suppl 2): S1458-S1461. doi: 10.4103/jpbs.jpbs_251_21. Epub 2021 Nov 10. PMID: 35018009; PMCID: PMC8686985.Barbosa VL, Angst PD, Finger Stadler A, Oppermann RV, Gomes SC. Clinical attachment loss: estimation by direct and indirect methods. Int Dent J. 2016 Jun;66(3):144-9. doi: 10.1111/idj.12218. Epub 2016 Feb 5. PMID: 26846817; PMCID: PMC9376648Baser U, Oztekin G, Ademoglu E, Isik G, Yalcin F. Is the severity of periodontitis related to gingival crevicular fluid and serum high-sensitivity C-reactive protein concentrations? Clin Lab. 2014;60(10):1653-8. doi: 10.7754/clin.lab.2014.131217. PMID: 25651710.Bashir, N. Z., Rahman, Z., & Chen, S. L. S. (2022). Systematic comparison of Machine Learning algorithms to develop and validate predictive models for periodontitis. Journal of Clinical Periodontology, 49(10), 958-969.Botero JE, Zuluaga AI, Suárez-Córdoba V, Calzada MT, Gutiérrez-Quiceno B, Gutiérrez AF, Mateus-Londoño N. Using Machine Learning to study the association of sociodemographic indicators, biomarkers, and oral condition in older adults in Colombia. J Am Dent Assoc. 2023 Aug;154(8):715-726.e5. doi: 10.1016/j.adaj.2023.04.017. PMID: 37500234.Chapple ILC, Mealey BL, Van Dyke TE, Bartold PM, Dommisch H, Eickholz P, Geisinger ML, Genco RJ, Glogauer M, Goldstein M, Griffin TJ, Holmstrup P, Johnson GK, Kapila Y, Lang NP, Meyle J, Murakami S, Plemons J, Romito GA, Shapira L, Tatakis DN, Teughels W, Trombelli L, Walter C, Wimmer G, Xenoudi P, Yoshie H. Periodontal health and gingival diseases and conditions on an intact and a reduced periodontium: Consensus report of workgroup 1 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Periodontol. 2018 jun;89 Suppl 1: S74-S84. doi: 10.1002/JPER.17-0719. PMID: 29926944.Chatzopoulos, G. S., Jiang, Z., Marka, N., & Wolff, L. F. (2023). Association between periodontitis extent, severity, and progression rate with systemic diseases and smoking: A retrospective study. Journal of Personalized Medicine, 13(5), 814.Cooray U, Watt RG, Tsakos G, Heilmann A, Hariyama M, Yamamoto T, Kuruppuarachchige I, Kondo K, Osaka K, Aida J. Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a Machine Learning analysis. Soc Sci Med. 2021 Dec; 291:114486. doi: 10.1016/j.socscimed.2021.114486. Epub 2021 Oct 13. PMID: 34700121.Du, M., Bo, T., Kapellas, K., & Peres, M. A. (2018). Prediction models for the incidence and progression of periodontitis: A systematic review. Journal of clinical periodontology, 45(12), 1408-1420.Duarte, P. M., Nogueira, C. F. P., Silva, S. M., Pannuti, C. M., Schey, K. C., & Miranda, T. S. (2021). Impact of smoking cessation on periodontal tissues. Journal of Dental Research.Gomes-Filho, I. S., Santos, P. N. P., Cruz, S. S., Figueiredo, A. C. M. G., Trindade, S. C., Ladeia, A. M., ... Scannapieco, F. A. (2021). Periodontitis and its higher levels of severity are associated with the triglyceride/high density lipoprotein cholesterol ratio. Journal of Periodontology, 92(12), 1509--1521. https://doi.org/10.1002/JPER.21-0004Goswami S, Ylöstalo P, Khan S, Knuuttila M, Bernabe E, Suominen AL. Effect of smoking on periodontal health and validation of self-reported smoking status with serum cotinine levels. Acta Odontol Scand. 2021 nov;79(8):573-581. doi: 10.1080/00016357.2021.1917655. Epub 2021 May 8. PMID: 33966590.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.Haworth, S., Shungin, D., Kwak, S. Y., Kim, H. Y., West, N. X., Thomas, S. J., Franks, P. W., Timpson, N. J., Shin, M. J., & Johansson, I. (2018). Tooth loss is a complex measure of oral disease: Determinants and methodological considerations. Community dentistry and oral epidemiology, 46(6), 555--562. https://doi.org/10.1111/cdoe.12391.Heitz-Mayfield LJ. Disease progression: identification of high-risk groups and individuals for periodontitis. J Clin Periodontol. 2005;32 Suppl 6:196-209. doi: 10.1111/j.1600- 051X.2005.00803.x. PMID: 16128838.Herrera, D.; Figuero, E.; Shapira, L.; Jin, L. & Sanz, M. La Nueva clasificación de enfermedades periodontales y periimplantares. Rev. Cient. Soc. Esp. Periodoncia, 11, 201.Huang, Y. Q., Xu, J. N., Huang, Y., Xu, Y. D., Wang, H. L., Shi, W. T., Wang, J., & Wang, H. (2024). Independent and combined effects of smoking, drinking and depression on periodontal disease. BMC Oral Health, 24, 535.Jaramillo A, Contreras A, Lafaurie GI, Duque A, Ardila CM, Duarte S, Osorio L. Association of metabolic syndrome and chronic periodontitis in Colombians. Clin Oral Investig. 2017 jun;21(5):1537-1544. doi: 10.1007/s00784-016-1942-9.Jundaeng J, Chamchong R, Nithikathkul C. Periodontitis Diagnosis: A Review of Current and Future Trends in Artificial Intelligence. J Esthet Restor Dent. 2022 Jan;34(1):259-280. doi:10.1111/jerd.12844. PMID: 34842324.Kharroubi AT, Darwish HM. Diabetes mellitus: The epidemic of the century. World J Diabetes. 2015 jun 25;6(6):850-67. doi: 10.4239/wjd. v6.i6.850. PMID: 26131326; PMCID: PMC4478580.Klonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol. 2023;17(5):1226-1242. doi:10.1177/19322968221085273.Kim, E.-K., Cho, J.-Y., & Park, E. Y. (2023). Association between periodontal disease and hypertriglyceridemia. Medicine, 102(51), 1-8. DOI: 10.1097/MD.0000000000036502Kinane DF, Mooney J, Ebersole JL. Humoral immune response to Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis in periodontal disease. Periodontol 2000. 1999 jun; 20:289-340. doi: 10.1111/j.1600-0757. 1999.tb00164. x. PMID: 10522229.Lafaurie, G. I., Sabogal, M. A., Castillo, D. M., Gualtero, D. F., De Avila, J., Trujillo, T. G., Ardila, C. M., Duque, A., Giraldo, A., Duarte, S., Gutiérrez, S. J., & Contreras, A. (Year). Factors associated with increasing clinical attachment loss in chronic periodontitis: A cross-sectional study.Lakio L, Antinheimo J, Paju S, Buhlin K, Pussinen PJ, Alfthan G. Tracking of plasma antibodies against Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis during 15 years. J Oral Microbiol. 2009 Aug 3;1. doi: 10.3402/jom. v1i0.1979. PMID: 21523211; PMCID: PMC3077000.Lee, Y-C., Lee, J-W., & Kwon, Y-J. (2022). Comparison of the triglyceride glucose (TyG) index, triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, and metabolic score for insulin resistance (METS-IR) associated with periodontitis in Korean adults. Therapeutic Advances in Chronic Disease, 13, 1--15. https://doi.org/10.1177/20406223221122671.Li, Y., Xu, Y., et al. (2021). Asociación entre el índice triglicéridos-glucosa y la periodontitis: un estudio de la Encuesta Nacional de Salud y Nutrición de Estados Unidos. International Journal of General Medicine, 14. https://doi.org/10.2147/IJGM.S339863.Liu C, Shi F, Li W, Chen J. Efficacy of non-surgical periodontal treatment on patients with coronary artery disease: a meta-analysis of randomized controlled trials. Med Oral Patol Oral Cir Bucal. 2022 nov 1;27(6): e578-e587. doi: 10.4317/medoral.25514. PMID: 36243997; PMCID: PMC9648645.Lopez-Jaramillo, P., Gomez-Arbelaez, D., Martinez-Bello, D., Abat, M. E. M., Alhabib, K. F., Avezum, Á., ... Yusuf, S. (2023). Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. The Lancet Healthy Longevity, 4, e23- e33. https://doi.org/10.1016/S2666-7568(22)00247-1.Martínez-García M, Castrejón-Pérez RC, Rodríguez-Hernández AP, Sandoval-Motta S, Vallejo M, Borges-Yáñez SA, Hernández-Lemus E. Incidence of Arterial Hypertension in People With Periodontitis and Characterization of the Oral and Subgingival Microbiome: A Study Protocol. Front Cardiovasc Med. 2022.Mattos M, Barbosa JC, Almeida A, et al. Expresión de mediadores inflamatorios en pacientes con periodontitis y diabetes tipo 2: una revisión sistemática y metaanálisis. Braz Oral Res. 2022;36:e0098. doi:10.1590/1807-3107bor-2022.vol36.0098. Disponible en: https://www.scielo.br/j/bor/a/9j3YQ3TfGxCrX49TdvpVN5j/Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020 Apr;16(4):223-237. doi: 10.1038/s41581-019-0244-2. Epub 2020 Feb 5. PMID: 32024986; PMCID: PMC7998524.Nascimento GG, Leite FRM, Vestergaard P, Scheutz F, López R. Does diabetes increase the risk of periodontitis? A systematic review and meta-regression analysis of longitudinal prospective studies. Acta Diabetol. 2018 jul;55(7):653-667. doi: 10.1007/s00592-018-1120- 4. Epub 2018 Mar 3. PMID: 29502214.Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today. 2015 May;50(3):117-128. doi: 10.1097/NT.0000000000000092. Epub 2015 Apr 7. PMID: 27340299; PMCID: PMC4890841.OMS. Enfermedades cardiovasculares. Revisado en http://www.who.int/mediacentre/factsheets/fs317/es/Papapanou PN, Sanz M, Buduneli N, Dietrich T, Feres M, Fine DH, Flemmig TF, Garcia R, Giannobile WV, Graziani F, Greenwell H, Herrera D, Kao RT, Kebschull M, Kinane DF, Kirkwood KL, Kocher T, Kornman KS, Kumar PS, Loos BG, Machtei E, Meng H, Mombelli A, Needleman I, Offenbacher S, Seymour GJ, Teles R, Tonetti MS. Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Periodontol. 2018 jun;89 Suppl 1: S173-S182. doi: 10.1002/JPER.17-0721. PMID: 29926951.Papapanou, PN, Sanz, M, et al. Periodontitis: Consensus report of Workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Clin Periodontol. 2018; 45(Suppl 20): S162-- S170Peng J, Song J, Han J, Chen Z, Yin X, Zhu J, Song J. The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: systematic review and dose-response meta-analysis of prospective cohort studies. Biosci Rep. 2019 Jan 11;39(1): BSR20181773. doi: 10.1042/BSR20181773. PMID: 30530864; PMCID: PMC6328868.Parihar AS, Narang S, Tyagi S, Narang A, Dwivedi S, Katoch V, Laddha R. Artificial Intelligence in Periodontics: A Comprehensive Review. J Pharm Bioallied Sci. 2024 May 13;16(Suppl 3):S1956–S1958. doi: 10.4103/jpbs.jpbs_129_24. PMID: 39346158; PMCID: PMC11426892.Polizzi A, Quinzi V, Lo Giudice A, Marzo G, Leonardi R, Isola G. (2024) Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review. JDR Clin Trans. doi: 10.1177/23800844241232318.Raschka, S. (2015). Python Machine Learning. Packt publishing ltd.Revilla-León, M., Gómez-Polo, M., Barmak, A. B., Inam, W., Kan, J. Y. K., Kois, J. C., & Akal, O. (2023). Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. The Journal of Prosthetic Dentistry, 130(6), 816--824.Sanz M, Marco Del Castillo A, Jepsen S, Gonzalez-Juanatey JR, D'Aiuto F, Bouchard P, Chapple I, Dietrich T, Gotsman I, Graziani F, Herrera D, Loos B, Madianos P, Michel JB, Perel P, Pieske B, Shapira L, Shechter M, Tonetti M, Vlachopoulos C, Wimmer G. Periodontitis and cardiovascular diseases: Consensus report. J Clin Periodontol. 2020Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021;2(3):160. doi:10.1007/s42979-021-00592-xSchmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, Volume 61, January 2015, Pages 85-117 (DOI: 10.1016/j.neunet.2014.09.003Scott J, Biancardi AM, Jones O, Andrew D. Artificial Intelligence in Periodontology: A Scoping Review. Dent J. 2023;11(2):43. https://doi.org/10.3390/dj11020043Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomark Insights. 2016 jul 3; 11:95-104. doi: 10.4137/BMI.S38440. PMID: 27398023; PMCID: PMC4933534.Stöhr J, Barbaresko J, Neuenschwander M, Schlesinger S. Bidirectional association between periodontal disease and diabetes mellitus: a systematic review and meta-analysis of cohort studies. Sci Rep. 2021 Jul 1;11(1):13686. doi: 10.1038/s41598-021-93062-6. PMID: 34211029; PMCID: PMC8249442.Su N, Teeuw WJ, Loos BG, Kosho MXF, van der Heijden GJMG. Development and validation of a screening model for diabetes mellitus in patients with periodontitis in dental settings. Clin Oral Investig. 2020 nov;24(11):4089-4100. doi: 10.1007/s00784-020-03281-w. Epub 2020 Jun 15. PMID: 32542584; PMCID: PMC7544748.Sun et al., "Application of Machine Learning to Stomatology: A Comprehensive Review," in IEEE Access, vol. 8, pp. 184360-184374, 2020, doi: 10.1109/ACCESS.2020.3028600.Tonetti MS. Periodontitis and risk for atherosclerosis: an update on intervention trials. J Clin Periodontol. 2009 jul;36 Suppl 10:15-9. doi: 10.1111/j.1600-051X.2009.01417. x. PMID: 19432627.Tonetti, M. S.; Greenwell, H. & Kornman, K. S. Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J. Periodontol., 89 Suppl. 1: S159-72, 2018.Zatt, F. P., Cordeiro, J. V. C., Bohner, L., Souza, B. D. M. de, Caldas, V. E. A., & Caldas, R. A. (2024). Using Machine Learning to classify temporomandibular disorders: A proof of concept. Journal of Applied Oral Science, 32Zhang Z. A gentle introduction to artificial neural networks. Ann Transl Med 2016; 4: 370.Zhu, H., Ye, G., Xie, Y., Zhu, K., Zhu, F., & Chen, Q. (2022). Association of high-density lipoprotein cholesterol and periodontitis severity in Chinese elderly: a cross-sectional study. Clinical Oral Investigations. https://doi.org/10.1007/s00784-022-04439-4spaLICENSElicense.txtlicense.txttext/plain; charset=utf-82000https://repositorio.unbosque.edu.co/bitstreams/e7fc7c83-407a-44ca-bb4e-0837f18ba0cd/download17cc15b951e7cc6b3728a574117320f9MD52Carta autorizacion.pdfapplication/pdf196051https://repositorio.unbosque.edu.co/bitstreams/1677e4a2-da72-4997-bc6a-fb77ab778b07/download2732878e5bac158d632516fc55a3e6a1MD55Anexo 1 Acta de aprobacion.pdfapplication/pdf204361https://repositorio.unbosque.edu.co/bitstreams/2ff158f1-73a1-4281-97c7-97f88be5c5f9/download6f6c2bb848d99a9cb68aab4b88bcb2c5MD56Mención responsabilidad.pdfapplication/pdf257565https://repositorio.unbosque.edu.co/bitstreams/f915779c-4d4a-4b07-ba57-d4507b200be3/downloadd146cbbc76d2cef619074bff0ace8dcaMD57ORIGINALTrabajo final.pdfTrabajo final.pdfapplication/pdf1125883https://repositorio.unbosque.edu.co/bitstreams/7417ff39-c310-4e6c-8737-6fa81b828aad/downloada23b69f9a107f9ac19be28374a9e7f60MD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81160https://repositorio.unbosque.edu.co/bitstreams/f448a5a6-baec-4d8e-a1ac-be62430194d4/download5643bfd9bcf29d560eeec56d584edaa9MD54TEXTTrabajo final.pdf.txtTrabajo final.pdf.txtExtracted texttext/plain97507https://repositorio.unbosque.edu.co/bitstreams/237a87be-68ca-48c0-9c45-fd9be20a0b76/download908aa0f1ed71afd47ad696fbf2e497a5MD58THUMBNAILTrabajo final.pdf.jpgTrabajo final.pdf.jpgGenerated Thumbnailimage/jpeg2683https://repositorio.unbosque.edu.co/bitstreams/bba91e6b-70b5-41af-8b08-841337f5bf86/download3de840c8c7fd7ba3abe0470b195d9f15MD5920.500.12495/14211oai:repositorio.unbosque.edu.co:20.500.12495/142112025-04-02 10:29:52.467http://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalopen.accesshttps://repositorio.unbosque.edu.coRepositorio Institucional Universidad El Bosquebibliotecas@biteca.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 |