Precisión diagnóstica de la inteligencia artificial en colposcopia para lesiones cervicales de alto grado: una revisión sistemática
DOI:
https://doi.org/10.36097/rgcs.v3i2.3234Palabras clave:
inteligencia artificial, colposcopia, neoplasia intraepitelial cervical, lesiones de alto grado, precisión diagnóstica, cáncer cervicouterinoResumen
La colposcopia tiene limitaciones por la variabilidad del observador, por lo que la inteligencia artificial (IA) se ha propuesto para mejorar su precisión. Esta revisión sistemática (PRISMA 2020) analizó la precisión diagnóstica de sistemas de IA en imágenes colposcópicas para detectar lesiones intraepiteliales cervicales de alto grado (CIN2+, CIN3+, HSIL). Se buscó en PubMed, Scopus, Web of Science, ScienceDirect, IEEE Xplore y Google Scholar (enero de 2021 - junio de 2026), incluyendo estudios con confirmación histopatológica como estándar de referencia y evaluando la calidad con QUADAS-2. Se incluyeron 10 estudios (más de 15.000 imágenes y 7.500 pacientes), mayoritariamente retrospectivos y asiáticos. El rendimiento fue heterogéneo: sensibilidad (63,0-99,9 %), especificidad (57,6-99,8 %) y AUC hasta 0,95. La IA resultó útil como apoyo, especialmente para colposcopistas noveles. Los principales sesgos se relacionaron con la selección de pacientes y el diseño retrospectivo. En conclusión, la IA muestra una precisión diagnóstica prometedora para lesiones de alto grado, aunque con variabilidad significativa. Se necesitan estudios prospectivos, multicéntricos y en condiciones reales para confirmar su efectividad y costo-efectividad antes de su implementación clínica generalizada.
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Referencias
Aquilina, A., & Papagiannakis, E. (2024). Deep learning diagnostic classification of cervical images to augment colposcopic impression. Journal of Lower Genital Tract Disease, 28(3), 224. https://doi.org/10.1097/LGT.0000000000000815
Bifulco, G., De Rosa, N., Lavitola, G., Piccoli, R., Bertrando, A., Natella, V., Di Carlo, C., Insabato, L., & Nappi, C. (2015). A prospective randomized study on limits of colposcopy and histology: The skill of colposcopist and colposcopy-guided biopsy in diagnosis of cervical intraepithelial lesions. Infectious Agents and Cancer, 10(1), 47. https://doi.org/10.1186/s13027-015-0042-9
Booth, B. B., Khare, S. K., Blanes-Vidal, V., Bor, P., Nadimi, E. S., & Petersen, L. K. (2026). Higher diagnostic accuracy of an AI model for colposcopy compared with conventional and digital colposcopic evaluation. Journal of Lower Genital Tract Disease, 30(2), 118-123. https://doi.org/10.1097/LGT.0000000000000931
Bouvard, V., Wentzensen, N., Mackie, A., Berkhof, J., Brotherton, J., Giorgi-Rossi, P., Kupets, R., Smith, R., Arrossi, S., Bendahhou, K., Canfell, K., Chirenje, Z. M., Chung, M. H., Pino, M. del, de Sanjosé, S., Elfström, M., Franco, E. L., Hamashima, C., Hamers, F. F., ... Lauby-Secretan, B. (2021). The IARC perspective on cervical cancer screening. New England Journal of Medicine, 385(20), 1908-1918. https://doi.org/10.1056/NEJMsr2030640
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263. https://doi.org/10.3322/caac.21834
Chang, Y., Li, T., Zhou, Q., Kang, D., Zhu, L., Yang, J., Kou, Q., He, H., Zhou, Y., Liao, Q., Du, J., Yu, X., & Zhao, Y. (2025). Effectiveness of artificial intelligence-assisted colposcopy in a resource-limited population. Obstetrics & Gynecology, 146(4), 545-554. https://doi.org/10.1097/AOG.0000000000006014
Desai, K. T., Befano, B., Xue, Z., Kelly, H., Campos, N. G., Egemen, D., Gage, J. C., Rodriguez, A.-C., Sahasrabuddhe, V., Levitz, D., Pearlman, P., Jeronimo, J., Antani, S., Schiffman, M., & de Sanjosé, S. (2022). The development of automated visual evaluation for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing. International Journal of Cancer, 150(5), 741-752. https://doi.org/10.1002/ijc.33879
Dorji, N., Tshering, S., Choden, S., Chhetri, M., Bhujel, D., Wangden, T., Pradhan, B., Bhutia, P. C., & Tshomo, U. (2022). Evaluation of the diagnostic performance of colposcopy in the diagnosis of histologic cervical intraepithelial neoplasia 2+ (CIN2+). BMC Cancer, 22, 930. https://doi.org/10.1186/s12885-022-10030-7
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Hu, L., Bell, D., Antani, S., Xue, Z., Yu, K., Horning, M. P., Gachuhi, N., Wilson, B., Jaiswal, M. S., Befano, B., Long, L. R., Herrero, R., Einstein, M. H., Burk, R. D., Demarco, M., Gage, J. C., Rodriguez, A. C., Wentzensen, N., & Schiffman, M. (2019). An observational study of deep learning and automated evaluation of cervical images for cancer screening. Journal of the National Cancer Institute, 111(9), 923-932. https://doi.org/10.1093/jnci/djy225
Ito, Y., Miyoshi, A., Ueda, Y., Tanaka, Y., Nakae, R., Morimoto, A., Shiomi, M., Enomoto, T., Sekine, M., Sasagawa, T., Yoshino, K., Harada, H., Nakamura, T., Murata, T., Hiramatsu, K., Saito, J., Yagi, J., Tanaka, Y., & Kimura, T. (2022). An artificial intelligence-assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions. Molecular and Clinical Oncology, 16(2), 27. https://doi.org/10.3892/mco.2021.2460
Kim, S., An, H., Cho, H.-W., Min, K.-J., Hong, J.-H., Lee, S., Song, J.-Y., Lee, J.-K., & Lee, N.-W. (2023). Pivotal clinical study to evaluate the efficacy and safety of assistive artificial intelligence-based software for cervical cancer diagnosis. Journal of Clinical Medicine, 12(12), 4024. https://doi.org/10.3390/jcm12124024
Kim, S., Lee, H., Lee, S., Song, J.-Y., Lee, J.-K., & Lee, N.-W. (2022). Role of artificial intelligence interpretation of colposcopic images in cervical cancer screening. Healthcare, 10(3), 468. https://doi.org/10.3390/healthcare10030468
Liu, L., Liu, J., Su, Q., Chu, Y., Xia, H., & Xu, R. (2025). Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: A systematic review and meta-analysis. eClinicalMedicine, 80, Article 102992. https://doi.org/10.1016/j.eclinm.2024.102992
Liu, L., Wang, Y., Liu, X., Han, S., Jia, L., Meng, L., Yang, Z., Chen, W., Zhang, Y., & Qiao, X. (2021). Computer-aided diagnostic system based on deep learning for classifying colposcopy images. Annals of Translational Medicine, 9(13), 1045. https://doi.org/10.21037/atm-21-885
Mascarenhas, M., Alencoão, I., Carinhas, M. J., Martins, M., Cardoso, P., Mendes, F., Fernandes, J., Ferreira, J., Macedo, G., & Zulmira Macedo, R. (2024). Artificial intelligence and colposcopy: Automatic identification of cervical squamous cell carcinoma precursors. Journal of Clinical Medicine, 13(10), 3003. https://doi.org/10.3390/jcm13103003
Miyagi, Y., Takehara, K., Nagayasu, Y., & Miyake, T. (2020). Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types. Oncology Letters, 19(2), 1602–1610. https://doi.org/10.3892/ol.2019.11214
Nakisige, C., de Fouw, M., Kabukye, J., Sultanov, M., Nazrui, N., Rahman, A., de Zeeuw, J., Koot, J., Rao, A. P., Prasad, K., Shyamala, G., Siddharta, P., Stekelenburg, J., & Beltman, J. J. (2023). Artificial intelligence and visual inspection in cervical cancer screening. International Journal of Gynecological Cancer, 33(10), 1515-1521. https://doi.org/10.1136/ijgc-2023-004397
Ouh, Y.-T., Kim, T. J., Ju, W., Kim, S. W., Jeon, S., Kim, S.-N., Kim, K. G., & Lee, J.-K. (2024). Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia. Scientific Reports, 14(1), 1957. https://doi.org/10.1038/s41598-024-51880-4
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71
Perkins, R. B., Guido, R. S., Castle, P. E., Chelmow, D., Einstein, M. H., Garcia, F., Huh, W. K., Kim, J. J., Moscicki, A.-B., Nayar, R., Saraiya, M., Sawaya, G. F., Wentzensen, N., Schiffman, M., & 2019 ASCCP Risk-Based Management Consensus Guidelines Committee. (2020). 2019 ASCCP risk-based management consensus guidelines for abnormal cervical cancer screening tests and cancer precursors. Journal of Lower Genital Tract Disease, 24(2), 102-131. https://doi.org/10.1097/LGT.0000000000000525
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31-38. https://doi.org/10.1038/s41591-021-01614-0
Takahashi, T., Kobayashi, Y., Sakurai, R., Matsuoka, K., Akatsuka, J., Kisu, I., Iwata, T., Takayama, J., Matsuzaki, M., Yamagami, W., Banno, K., Yamamoto, Y., Matsuoka, H., & Tamiya, G. (2025). A systematic review of the application of artificial intelligence in colposcopy: Diagnostic accuracy for cervical intraepithelial neoplasia and cervical cancer. Clinical Medicine Insights: Oncology, 19, 11795549251374908. https://doi.org/10.1177/11795549251374908
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Vallikad, E., Siddartha, P. T., Kulkarni, K. A., Firtion, C., Keswarpu, P., Vajinepalli, P., Naik, S., & Gupta, L. (2017). Intra and inter-observer variability of transformation zone assessment in colposcopy: A qualitative and quantitative study. Journal of Clinical and Diagnostic Research, 11(1), XC04-XC06. https://doi.org/10.7860/JCDR/2017/21943.9168
Wentzensen, N., Walker, J. L., Gold, M. A., Smith, K. M., Zuna, R. E., Mathews, C., Dunn, S. T., Zhang, R., Moxley, K., Bishop, E., Tenney, M., Nugent, E., Graubard, B. I., Wacholder, S., & Schiffman, M. (2015). Multiple biopsies and detection of cervical cancer precursors at colposcopy. Journal of Clinical Oncology, 33(1), 83-89. https://doi.org/10.1200/JCO.2014.55.9948
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & QUADAS-2 Group. (2011). QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529-536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
World Health Organization. (2020). Global strategy to accelerate the elimination of cervical cancer as a public health problem. World Health Organization. https://www.who.int/publications/i/item/9789240014107
World Health Organization. (2021). WHO guideline for screening and treatment of cervical pre-cancer lesions for cervical cancer prevention (2nd ed.). World Health Organization. https://www.who.int/publications/i/item/9789240030824
World Health Organization. (2025). Cervical cancer. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/cervical-cancer
Wu, A., Xue, P., Abulizi, G., Tuerxun, D., Rezhake, R., & Qiao, Y. (2023). Artificial intelligence in colposcopic examination: A promising tool to assist junior colposcopists. Frontiers in Medicine, 10, 1060451. https://doi.org/10.3389/fmed.2023.1060451
Yuan, C., Yao, Y., Cheng, B., Cheng, Y., Li, Y., Li, Y., Liu, X., Cheng, X., Xie, X., Wu, J., Wang, X., & Lu, W. (2020). The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images. Scientific Reports, 10(1), 11639. https://doi.org/10.1038/s41598-020-68252-3
Zhao, Y., Li, Y., Xing, L., Lei, H., Chen, D., Tang, C., & Li, X. (2022). The performance of artificial intelligence in cervical colposcopy: A retrospective data analysis. Journal of Oncology, 2022, 4370851. https://doi.org/10.1155/2022/4370851
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Derechos de autor 2026 Estefanía Bailón-Mieles, Jessica Pilozo
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