Unsupervised Learning for Identifying Clinical and Symptom-Based Profiles in Endometriosis: A Systematic Literature Review
Resumo
A endometriose é uma condição heterogênea que não é plenamente representada pelos atuais sistemas de estadiamento. Esta Revisão Sistemática da Literatura investigou o uso do aprendizado de máquina não supervisionado na identificação de perfis clínico-sintomáticos em pacientes. Foram incluídos oito estudos usando variáveis como dor, comorbidades e qualidade de vida. Os resultados evidenciaram perfis distintos relacionados à carga sintomática, à multimorbidade e ao impacto biopsicossocial. Conclui-se que esses métodos podem complementar as classificações anatômicas, mas ainda requerem validação externa e longitudinal antes da aplicação clínica.
Referências
Aggarwal, C. C. and Reddy, C. K., editors (2013). Data Clustering: Algorithms and Applications. CRC Press, Boca Raton.
Åkerblom, S., Jönsson, I. P., Ringqvist, Å., Nordengren, J., and Zhao, X. (2025). Identifying and characterizing clinical subgroups in individuals with endometriosis. Frontiers in Pain Research, Volume 6 - 2025.
AlSaad, R., Farrell, T., Elhenidy, A., Albasha, S., and Thomas, R. (2026). Artificial intelligence in endometriosis imaging: A scoping review. AI, 7(2).
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York.
Esmailzadeh, A., Rashki Kemmak, A., Sezavar Dokhtfaroughi, S., Rasoulian, A., and Mazaheri Habibi, M. R. (2026). Investigating the role of artificial intelligence in the diagnosis and prediction of endometriosis using ultrasound images: a systematic review. Reproductive Health, 23(1):31.
Fuldeore, M. J. and Soliman, A. M. (2017). Prevalence and symptomatic burden of diagnosed endometriosis in the united states: National estimates from a cross-sectional survey of 59,411 women. Gynecologic and obstetric investigation, 82(5):453—461.
Guare, L., Humphrey, L. A., Rush, M., Pollie, M., Luo, Y., Weng, C., Wei, W.-Q., Kottyan, L., Jarvik, G., Elhadad, N., Penn Medicine Biobank, Regeneron Genetics Center, Zondervan, K., Missmer, S., Vujkovic, M., Velez-Edwards, D., Senapati, S., and Setia-Verma, S. (2024). Enhancing genetic association power in endometriosis through unsupervised clustering of clinical subtypes identified from electronic health records. medRxiv.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, 2 edition.
Kanti, F. S., Allard, V., Métivier, A.-A., Lemyre, M., Arendas, K., and Maheux-Lacroix, S. (2024). Pain phenotypes in endometriosis: A population-based study using latent class analysis. BJOG: An International Journal of Obstetrics & Gynaecology, 132:492–503.
Khan, U., Oskotsky, T. T., Yilmaz, B. D., Roger, J., Gjoni, K., Irwin, J. C., Opoku-Anane, J., Elhadad, N., Giudice, L. C., and Sirota, M. (2025). Comorbidity analysis and clustering of endometriosis patients using electronic health records. Cell Reports Medicine, 6. DOI: 10.1016/j.xcrm.2025.102245.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE 2007-001, Keele University and Durham University.
Mian, P. G., Conte, T., Natali, A. C. C., Biolchini, J., and Travassos, G. H. (2005). A systematic review process for software engineering. In Proceedings of the 2nd Experimental Software Engineering Latin American Workshop (ESELAW’05), pages 1–6, Uberlândia, Brazil.
Montanari, E., Dauser, B., Keckstein, J., Kirchner, E., Nemeth, Z., and Hudelist, G. (2019). Association between disease extent and pain symptoms in patients with deep infiltrating endometriosis. Reproductive BioMedicine Online, 39:845–851. DOI: 10.1016/j.rbmo.2019.06.006.
Nnoaham, K. E., Hummelshoj, L., Webster, P., d’Hooghe, T., de Cicco Nardone, F., de Cicco Nardone, C., Jenkinson, C., Kennedy, S. H., and Zondervan, K. T. (2011). Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertility and Sterility, 96:366–373.e8. DOI: 10.1016/j.fertnstert.2011.05.090.
Pereira, J. O. and Junior, J. K. (2025). Exploring endometriosis before surgical treatment: unraveling pain, sexual function and quality of life patterns. Revista Brasileira de Ginecologia e Obstetrícia, 47.
Ramadan, Z. M., Mouazen, S. M., Khan, S. S., Khan, S., and Farag, N. S. (2025). Machine learning in the early detection of endometriosis: a literature review on symptom clustering and imaging integration. Precision and Future Medicine, 9(3):117–128.
Sarria-Santamera, A., Yemenkhan, Y., Terzic, M., Ortega, M. A., and del Barco, A. (2023). A novel classification of endometriosis based on clusters of comorbidities. Biomedicines, 11.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão Sistemática da Literatura em Engenharia de Software: teoria e prática. Elsevier.
Sivajohan, B., Elgendi, M., Menon, C., Allaire, C., Yong, P., and Bedaiwy, M. A. (2022). Clinical use of artificial intelligence in endometriosis: a scoping review. npj Digital Medicine, 5(109).
Taylor, H. S., Kotlyar, A. M., and Flores, V. A. (2021). Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. The Lancet, 397:839–852. DOI: 10.1016/S0140-6736(21)00389-5.
Urteaga, I., McKillop, M., and Elhadad, N. (2020). Learning endometriosis phenotypes from patient-generated data. npj Digital Medicine, 3:88.
Vallée, A., Arutkin, M., Ceccaldi, P.-F., and Ayoubi, J.-M. (2025). Quality of life identification by unsupervised cluster analysis: A new approach to modelling the burden of endometriosis. PLOS ONE, 20:e0317178.
Zondervan, K., M, C. B., and A, S. M. (2020). Endometriosis. New England Journal of Medicine, 382:1244–1256. DOI: 10.1056/NEJMra1810764.
Åkerblom, S., Jönsson, I. P., Ringqvist, Å., Nordengren, J., and Zhao, X. (2025). Identifying and characterizing clinical subgroups in individuals with endometriosis. Frontiers in Pain Research, Volume 6 - 2025.
AlSaad, R., Farrell, T., Elhenidy, A., Albasha, S., and Thomas, R. (2026). Artificial intelligence in endometriosis imaging: A scoping review. AI, 7(2).
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York.
Esmailzadeh, A., Rashki Kemmak, A., Sezavar Dokhtfaroughi, S., Rasoulian, A., and Mazaheri Habibi, M. R. (2026). Investigating the role of artificial intelligence in the diagnosis and prediction of endometriosis using ultrasound images: a systematic review. Reproductive Health, 23(1):31.
Fuldeore, M. J. and Soliman, A. M. (2017). Prevalence and symptomatic burden of diagnosed endometriosis in the united states: National estimates from a cross-sectional survey of 59,411 women. Gynecologic and obstetric investigation, 82(5):453—461.
Guare, L., Humphrey, L. A., Rush, M., Pollie, M., Luo, Y., Weng, C., Wei, W.-Q., Kottyan, L., Jarvik, G., Elhadad, N., Penn Medicine Biobank, Regeneron Genetics Center, Zondervan, K., Missmer, S., Vujkovic, M., Velez-Edwards, D., Senapati, S., and Setia-Verma, S. (2024). Enhancing genetic association power in endometriosis through unsupervised clustering of clinical subtypes identified from electronic health records. medRxiv.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, 2 edition.
Kanti, F. S., Allard, V., Métivier, A.-A., Lemyre, M., Arendas, K., and Maheux-Lacroix, S. (2024). Pain phenotypes in endometriosis: A population-based study using latent class analysis. BJOG: An International Journal of Obstetrics & Gynaecology, 132:492–503.
Khan, U., Oskotsky, T. T., Yilmaz, B. D., Roger, J., Gjoni, K., Irwin, J. C., Opoku-Anane, J., Elhadad, N., Giudice, L. C., and Sirota, M. (2025). Comorbidity analysis and clustering of endometriosis patients using electronic health records. Cell Reports Medicine, 6. DOI: 10.1016/j.xcrm.2025.102245.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE 2007-001, Keele University and Durham University.
Mian, P. G., Conte, T., Natali, A. C. C., Biolchini, J., and Travassos, G. H. (2005). A systematic review process for software engineering. In Proceedings of the 2nd Experimental Software Engineering Latin American Workshop (ESELAW’05), pages 1–6, Uberlândia, Brazil.
Montanari, E., Dauser, B., Keckstein, J., Kirchner, E., Nemeth, Z., and Hudelist, G. (2019). Association between disease extent and pain symptoms in patients with deep infiltrating endometriosis. Reproductive BioMedicine Online, 39:845–851. DOI: 10.1016/j.rbmo.2019.06.006.
Nnoaham, K. E., Hummelshoj, L., Webster, P., d’Hooghe, T., de Cicco Nardone, F., de Cicco Nardone, C., Jenkinson, C., Kennedy, S. H., and Zondervan, K. T. (2011). Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertility and Sterility, 96:366–373.e8. DOI: 10.1016/j.fertnstert.2011.05.090.
Pereira, J. O. and Junior, J. K. (2025). Exploring endometriosis before surgical treatment: unraveling pain, sexual function and quality of life patterns. Revista Brasileira de Ginecologia e Obstetrícia, 47.
Ramadan, Z. M., Mouazen, S. M., Khan, S. S., Khan, S., and Farag, N. S. (2025). Machine learning in the early detection of endometriosis: a literature review on symptom clustering and imaging integration. Precision and Future Medicine, 9(3):117–128.
Sarria-Santamera, A., Yemenkhan, Y., Terzic, M., Ortega, M. A., and del Barco, A. (2023). A novel classification of endometriosis based on clusters of comorbidities. Biomedicines, 11.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão Sistemática da Literatura em Engenharia de Software: teoria e prática. Elsevier.
Sivajohan, B., Elgendi, M., Menon, C., Allaire, C., Yong, P., and Bedaiwy, M. A. (2022). Clinical use of artificial intelligence in endometriosis: a scoping review. npj Digital Medicine, 5(109).
Taylor, H. S., Kotlyar, A. M., and Flores, V. A. (2021). Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. The Lancet, 397:839–852. DOI: 10.1016/S0140-6736(21)00389-5.
Urteaga, I., McKillop, M., and Elhadad, N. (2020). Learning endometriosis phenotypes from patient-generated data. npj Digital Medicine, 3:88.
Vallée, A., Arutkin, M., Ceccaldi, P.-F., and Ayoubi, J.-M. (2025). Quality of life identification by unsupervised cluster analysis: A new approach to modelling the burden of endometriosis. PLOS ONE, 20:e0317178.
Zondervan, K., M, C. B., and A, S. M. (2020). Endometriosis. New England Journal of Medicine, 382:1244–1256. DOI: 10.1056/NEJMra1810764.
Publicado
19/10/2026
Como Citar
FREITAS, Sabrina Ramos de; ADAMATTI, Diana Francisca.
Unsupervised Learning for Identifying Clinical and Symptom-Based Profiles in Endometriosis: A Systematic Literature Review. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 3582-3593.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31473.
