Artificial intelligence in the early diagnosis of digestive cancer
DOI:
https://doi.org/10.56294/evk2025281Keywords:
artificial intelligence, early diagnosis, digestive cancer, computer-aided detection (CADe), colorectal cancerAbstract
Digestive cancer is one of the leading causes of mortality worldwide, with an increasing incidence in several regions, including Latin America. Early detection of these pathologies is crucial to improve clinical outcomes and reduce the economic burden associated with the treatment of advanced stage cancers. This review is justified by the need to address the significant challenge of early detection of digestive cancer, especially in asymptomatic patients. The main objective of this study is to learn about the benefits of artificial intelligence (AI) in improving early diagnosis of digestive cancers, evaluating its effectiveness in identifying early lesions and its impact on diagnostic accuracy. Through a systematic review of the literature, and the application of the PRISMA model for its development, we examine various applications of AI as applied to medicine and specifically to the diagnosis of digestive cancers, including computer-aided detection (CADe), and discuss the benefits of its implementation in clinical practice. The findings suggest that AI has the potential to transform digestive cancer diagnosis, although further research is required to overcome current barriers and validate its use in clinical settings.
References
1. Internacional Agency for Research on cancer. Cancer Today. World Healt Organization.(2022). Revisado el 22 de noviembre del 2024. Obtenido de: https://gco.iarc.fr/today/en/dataviz/maps-most-common-sites?mode=cancer&key=total
2. Internacional Agency for Research on cancer. Cancer Today. World Healt Organization. (2022). Revisado el 22 de noviembre del 2024. Obtenido de: https://gco.iarc.fr/today/en/dataviz/pie?mode=population&key=total&cancers=7&group_populations=0&populations=152_170_188_192_214_218_222_254_312_32_320_328_332_340_388_44_474_484_52_558_591_600_604_630_662_68_740_76_780_84_858_862
3. Vazquez, N (2023). Inteliencia artificial y detección precoz de cáncer: una nueva esperanza. New Medical. [Internet]. www.newmedicaleconomics.com Revisado el 22 de noviembre del 2024. Obtenido de: https://www.newmedicaleconomics.es/gestion/inteligencia-artificial-y-deteccion-precoz-del-cancer-una-nueva-esperanza/
4. Mori, Y., Bretthauer, M., & Kalager, M. (2021). Hopes and hypes for artificial intelligence in colorectal cancer screening. Gastroenterology, 161(3), 774–777. https://doi.org/10.1053/j.gastro.2021.04.078
5. Wuhan ENDOANGEL Medical TechnologyCo., Ltd. (s/f). ENDOANGEL Endoscopio Gastrointestinal inferior, equipo de diagnóstico auxiliar de imagen. (s/f). Revisado el 22 de noviembre del 2024. Obtenido de: http://en.endoangel.cn/product/1.html
6. National Lbrary of Medicine (NIH) (2024). ENDOANGEL mejora la detección de adenomas colorrectales no detectados en una segunda colonoscopia: un estudio retrospectivo.National Center for Biotechnology Information. Revisado el 22 de noviembre del 2024. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC11245239/
7. Aguilera, M; Sánchez, S; Gonzalez, B; et, al. El papel emergente de la inteligencia artificial en la endoscopia gastrointestinal: una revisión de la literatura. Publicado el noviembre del 2021. Elsevier. Revisado el 22 de noviembre del 2024. Obtenido de: https://www.elsevier.es/es-revista-gastroenterologia-hepatologia-14-pdf-S0210570521003095
8. Urrútia, G; Bonfill. Declaración Prisma: una propuesta para mejorar la publicación de revisiones sistemáticas y metanalisis. España. Elsevier. Marzo 2010. Revisado el 22 de diciembre del 2024.
9. Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion. 2024;105(6):419-435. doi: 10.1159/000540251. Epub 2024 Jul 26. PMID: 39068926. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/39068926/
10. Zou PY, Zhu JR, Zhao Z, Mei H, Zhao JT, Sun WJ, Wang GH, Chen DF, Fan LL, Lan CH. Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study. BMC Gastroenterol. 2024 Sep 30;24(1):335. doi: 10.1186/s12876-024-03389-3. PMID: 39350033; PMCID: PMC11440712. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/39350033/
11. Pateras IS, Igea A, Nikas IP, Leventakou D, Koufopoulos NI, Ieronimaki AI, Bergonzini A, Ryu HS, Chatzigeorgiou A, Frisan T, Kittas C, Panayiotides IG. Diagnostic Challenges during Inflammation and Cancer: Current Biomarkers and Future Perspectives in Navigating through the Minefield of Reactive versus Dysplastic and Cancerous Lesions in the Digestive System. Int J Mol Sci. 2024 Jan 19;25(2):1251. doi: 10.3390/ijms25021251. PMID: 38279253; PMCID: PMC10816510. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38279253/
12. Chen H, Liu SY, Huang SH, Liu M, Chen GX. Aplicaciones de la inteligencia artificial en gastroscopia: una revisión narrativa. J Int Med Res. 2024 Ene; 52(1):3000605231223454. DOI: 10.1177/03000605231223454. PMID: 38235690; PMCID: PMC10798083. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38235690/
13. Khalaf K, Fujiyoshi MRA, Spadaccini M, Rizkala T, Ramai D, Colombo M, Fugazza A, Facciorusso A, Carrara S, Hassan C, Repici A. From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization. Medicina (Kaunas). 2024 Jan 3;60(1):89. doi: 10.3390/medicina60010089. PMID: 38256350; PMCID: PMC10818333. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38256350/
14. Ichimasa K, Kudo SE, Misawa M, Takashina Y, Yeoh KG, Miyachi H. Role of the artificial intelligence in the management of T1 colorectal cancer. Dig Liver Dis. 2024 Jul;56(7):1144-1147. doi: 10.1016/j.dld.2024.01.202. Epub 2024 Feb 3. PMID: 38311532. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38311532/
15. Yang C, Sharma K, Mow RJ, Bolay E, Srinivasan A, Merlin D. Unleashing the Potential of Oral Deliverable Nanomedicine in the Treatment of Inflammatory Bowel Disease. Cell Mol Gastroenterol Hepatol. 2024;18(2):101333. doi: 10.1016/j.jcmgh.2024.03.005. Epub 2024 Mar 13. PMID: 38490294; PMCID: PMC11176790. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38490294/
16. Bektas,M; Chia, C; Burchell, G; Daams, F; Bonjer,H; et al. Artifcial intelligence‑aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?. Surgical Endoscopy. (2024). 38:4869-4879. https://doi.org/10.1007/s00464-024-11130-0. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC11362182/pdf/464_2024_Article_11130.pdf
17. Gonzalez, J; Dayana, W. Detección temprana de cáncer gástrico utilizando nuevas técnicas endoscópicas. Revisión sistemática. Universidad Católica de Cuenca. (2024). Obtenido de: https://dspace.ucacue.edu.ec/items/90dae8cd-5a0d-4527-b34f-8f2493d3db35
18. Auzine MM, Khan MH, Baichoo S, Nuzhah GS, Bissoonauth-Daiboo P, Gao X, et al. Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer. PLoS One 2024 06;19(6). Obtenido de: https://www.proquest.com/healthcomplete/docview/3072226141/abstract/850317A5A41A4D0DPQ/2?accountid=37408&sourcetype=Scholarly%20Journals#
19. Peng Y, Deng H. Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer. BMC Medical Imaging 2024;24:1-11. Obtenido de: https://www.proquest.com/healthcomplete/docview/2925590708/abstract/850317A5A41A4D0DPQ/3?accountid=37408&sourcetype=Scholarly%20Journals
20. Khalaf K, Mary Raina AF, Spadaccini M, Rizkala T, Ramai D, Colombo M, et al. From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization. Medicina 2024;60(1):89. Obtenido de: https://www.proquest.com/healthcomplete/docview/2918773019/abstract/850317A5A41A4D0DPQ/5?accountid=37408&sourcetype=Scholarly%20Journals
21. Khalaf K, Mary Raina AF, Spadaccini M, Rizkala T, Ramai D, Colombo M, et al. From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization. Medicina 2024;60(1):89. Obtenido de: https://www.proquest.com/healthcomplete/docview/2918773019/abstract/850317A5A41A4D0DPQ/5?accountid=37408&sourcetype=Scholarly%20Journals
22. Sreetama M, Sunita V, Pravin G. Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer. Cureus 2024;16(2). Obtenido de: https://www.proquest.com/healthcomplete/docview/3020759397/850317A5A41A4D0DPQ/6?accountid=37408&sourcetype=Scholarly%20Journals
23. Zhao L, Wang N, Zhu X, Wu Z, Shen A, Zhang L, et al. Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma. Scientific Reports (Nature Publisher Group) 2024;14(1):10750. Obtenido de: https://www.proquest.com/healthcomplete/docview/3053361844/abstract/850317A5A41A4D0DPQ/7?accountid=37408&sourcetype=Scholarly%20Journals
24. Mota J, Maria João Almeida, Martins M, Mendes F, Cardoso P, Afonso J, et al. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. Journal of Clinical Medicine 2024;13(19):5842. Obtenido de: https://www.proquest.com/healthcomplete/docview/3116653624/F2D2284C1BCA4FEDPQ/9?accountid=37408&sourcetype=Scholarly%20Journals
25. Wan J, Zhu P, Chen B, Yu Y. A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images. Scientific Reports (Nature Publisher Group) 2024;14(1):15478. Obtenido de: https://www.proquest.com/healthcomplete/docview/3076105598/abstract/F2D2284C1BCA4FEDPQ/6?accountid=37408&sourcetype=Scholarly%20Journals
26. Zubair Rahman AMJM, Mythili R, Chokkanathan K, Mahesh TR, Vanitha K, Yimer TE. Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques. BMC Medical Imaging 2024;24:1-15. Obtenido de: https://www.proquest.com/healthcomplete/docview/3142292303/abstract/F2D2284C1BCA4FEDPQ/11?accountid=37408
27. Sinonquel P, Eelbode T, Pech O, De Wulf D, Dewint P, Neumann H, et al. Clinical consequences of computer-aided colorectal polyp detection. Gut 2024 06. Obtenido de: https://www.proquest.com/healthcomplete/docview/3068109825/fulltextPDF/F2D2284C1BCA4FEDPQ/16?accountid=37408&sourcetype=Scholarly%20Journals
28. Chen J, Wang G, Zhou J, Zhang Z, Ding Y, Xia K, et al. AI support for colonoscopy quality control using CNN and transformer architectures. BMC Gastroenterology 2024;24:1-13. Obtenido de: https://www.proquest.com/healthcomplete/docview/3091290206/fulltextPDF/F2D2284C1BCA4FEDPQ/15?accountid=37408&sourcetype=Scholarly%20Journals
29. Elshamy R, Abu-Elnasr O, Elhoseny M, Elmougy S. Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer. Scientific Reports (Nature Publisher Group) 2024;14(1):19534. Obtenido de: https://www.proquest.com/healthcomplete/docview/3095827897/F078172D2AE44545PQ/18?accountid=37408&sourcetype=Scholarly%20Journals
30. Thiruvengadam, N; Solaimani. P; Shrestha, M; Buller, S; Carson, R; et al. La eficacia de la detección asistida por computadora en tiempo real de la neoplasia de colon en la práctica comunitaria: un ensayo controlado aleatorio pragmático. Elsevier. Volumen 22, Número 11. Pag. 2221-2230.e15. (2024) Obtenido de: https://www.sciencedirect.com/science/article/pii/S1542356524002258?via%3Dihub
31. Mori Y, Jin EH, Lee D. Mejora de la colaboración entre la inteligencia artificial y los médicos para el diagnóstico asistido por computadora en la colonoscopia a través de una mejor alfabetización digital. Dig Liver Dis. 2024 Jul; 56(7):1140-1143. DOI: 10.1016/j.dld.2023.11.033. Epub 16 de diciembre de 2023. PMID: 38105144. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/38105144/
32. Wang KW, Dong M. Posibles aplicaciones de la inteligencia artificial en pólipos colorrectales y cáncer: avances y perspectivas recientes. Mundo J Gastroenterol. 14 de septiembre de 2020; 26(34):5090-5100. doi: 10.3748/wjg.v26.i34.5090. PMID: 32982111; PMCID: PMC7495038. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC7495038/
33. Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Inteligencia artificial para la detección precoz del cáncer colorrectal: una revisión exhaustiva de sus ventajas y conceptos erróneos. Mundo J Gastroenterol. 14 de octubre de 2021; 27(38):6399-6414. doi: 10.3748/wjg.v27.i38.6399. PMID: 34720530; PMCID: PMC8517786. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC8517786/
34. Mehta A, Kumar H, Yazji K, Wireko AA, Sivanandan Nagarajan J, Ghosh B, Nahas A, Morales Ojeda L, Anand A, Sharath M, Huang H, Garg T, Isik A. Effectiveness of artificial intelligence-assisted colonoscopy in early diagnosis of colorectal cancer: a systematic review. Int J Surg. 2023 Apr 1;109(4):946-952. doi: 10.1097/JS9.0000000000000285. PMID: 36917126; PMCID: PMC10389330. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC10389330/
35. Attardo S, Chandrasekar VT, Spadaccini M, Maselli R, Patel HK, Desai M, Capogreco A, Badalamenti M, Galtieri PA, Pellegatta G, Fugazza A, Carrara S, Anderloni A, Occhipinti P, Hassan C, Sharma P, Repici A. Tecnologías de inteligencia artificial para la detección de lesiones colorrectales: el futuro es ahora. Mundo J Gastroenterol. 7 de octubre de 2020; 26(37):5606-5616. doi: 10.3748/wjg.v26.i37.5606. PMID: 33088155; PMCID: PMC7545398. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC7545398/
36. Kim KO, Kim EY. Aplicación de la Inteligencia Artificial en la detección y caracterización de neoplasias colorrectales. Hígado de tripa. 15 de mayo de 2021; 15(3):346-353. DOI: 10.5009/GNL20186. PMID: 32773386; PMCID: PMC8129657. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC8129657/
37. Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, Moore JW, Sammour T. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer. 2021 Sep 26;21(1):1058. doi: 10.1186/s12885-021-08773-w. PMID: 34565338; PMCID: PMC8474828. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/34565338/
38. Qiu H, Ding S, Liu J, Wang L, Wang X. Aplicaciones de la inteligencia artificial en la detección, el diagnóstico, el tratamiento y el pronóstico del cáncer colorrectal. Curr Oncol. 7 de marzo de 2022; 29(3):1773-1795. doi: 10.3390/curroncol29030146. PMID: 35323346; PMCID: PMC8947571. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC8947571/
39. Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, Vieth M, Speranza I, Spadaccini M, Desai M, Lukens FJ, Babameto G, Batista D, Singh D, Palmer W, Ramirez F, Palmer R, Lunsford T, Ruff K, Bird-Liebermann E, Ciofoaia V, Arndtz S, Cangemi D, Puddick K, Derfus G, Johal AS, Barawi M, Longo L, Moro L, Repici A, Hassan C. Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia. Gastroenterology. 2022 Jul;163(1):295-304.e5. doi: 10.1053/j.gastro.2022.03.007. Epub 2022 Mar 15. PMID: 35304117. Obtenido de: https://pubmed.ncbi.nlm.nih.gov/35304117/
40. Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Inteligencia artificial en el cribado, diagnóstico y tratamiento del cáncer colorrectal. Una nueva era. Curr Oncol. 23 de abril de 2021; 28(3):1581-1607. doi: 10.3390/curroncol28030149. PMID: 33922402; PMCID: PMC8161764. Obtenido de: https://pmc.ncbi.nlm.nih.gov/articles/PMC8161764/.
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