Academic Journal
Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review.
| العنوان: | Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review. |
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| المؤلفون: | Chiesa-Estomba, Carlos M, Graña, Manuel, Medela, Alfonso, Sistiaga-Suarez, Jon A, Lechien, Jérome, Calvo-Henriquez, Christian, Mayo-Yanez, Miguel, Vaira, Luigi Angelo, Grammatica, Alberto, Cammaroto, Giovanni, Ayad, Tareck, Fagan, Johannes J |
| المساهمون: | P362 - Métrologie et Sciences du langage, R350 - Institut de recherche en sciences et technologies du langage, R550 - Institut des Sciences et Technologies de la Santé |
| المصدر: | ORL, 84 (4), 278 - 288 (2022) |
| بيانات النشر: | S. Karger AG |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Cancer, Machine learning, Management, Oral cavity, Prognosis, Algorithms, Artificial Intelligence, Humans, Squamous Cell Carcinoma of Head and Neck/therapy, Carcinoma, Squamous Cell/therapy, Head and Neck Neoplasms/diagnosis, Head and Neck Neoplasms/therapy, Mouth Neoplasms/diagnosis, Mouth Neoplasms/therapy, Otorhinolaryngology, Human health sciences, Otolaryngology, Sciences de la santé humaine, Oto-rhino-laryngologie |
| الوصف: | peer reviewed ; [en] INTRODUCTION: Despite multiple prognostic indicators described for oral cavity squamous cell carcinoma (OCSCC), its management still continues to be a matter of debate. Machine learning is a subset of artificial intelligence that enables computers to learn from historical data, gather insights, and make predictions about new data using the model learned. Therefore, it can be a potential tool in the field of head and neck cancer. METHODS: We conducted a systematic review. RESULTS: A total of 81 manuscripts were revised, and 46 studies met the inclusion criteria. Of these, 38 were excluded for the following reasons: use of a classical statistical method (N = 16), nonspecific for OCSCC (N = 15), and not being related to OCSCC survival (N = 7). In total, 8 studies were included in the final analysis. CONCLUSIONS: ML has the potential to significantly advance research in the field of OCSCC. Advantages are related to the use and training of ML models because of their capability to continue training continuously when more data become available. Future ML research will allow us to improve and democratize the application of algorithms to improve the prediction of cancer prognosis and its management worldwide. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 0301-1569 1423-0275 |
| Relation: | https://www.karger.com/Article/Pdf/520672; urn:issn:0301-1569; urn:issn:1423-0275; https://orbi.umons.ac.be/handle/20.500.12907/45330; info:hdl:20.500.12907/45330; https://orbi.umons.ac.be/bitstream/20.500.12907/45330/1/10.1159000520672.pdf; info:pmid:35021182 |
| DOI: | 10.1159/000520672 |
| الاتاحة: | https://orbi.umons.ac.be/handle/20.500.12907/45330 https://hdl.handle.net/20.500.12907/45330 https://orbi.umons.ac.be/bitstream/20.500.12907/45330/1/10.1159000520672.pdf https://doi.org/10.1159/000520672 |
| Rights: | open access ; http://purl.org/coar/access_right/c_abf2 ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.F9225FC9 |
| قاعدة البيانات: | BASE |
| تدمد: | 03011569 14230275 |
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| DOI: | 10.1159/000520672 |