FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3169969992> ?p ?o ?g. }

• W3169969992 abstract "Abstract Background Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary." @default.
• W3169969992 created "2021-06-22" @default.
• W3169969992 creator A5007965763 @default.
• W3169969992 creator A5018636685 @default.
• W3169969992 creator A5042870457 @default.
• W3169969992 creator A5058824167 @default.
• W3169969992 creator A5064582174 @default.
• W3169969992 creator A5074238976 @default.
• W3169969992 creator A5083399995 @default.
• W3169969992 date "2021-05-01" @default.
• W3169969992 modified "2023-09-23" @default.
• W3169969992 title "Machine learning in oral squamous cell carcinoma : current status, clinical concerns and prospects for future - A systematic review" @default.
• W3169969992 hasPublicationYear "2021" @default.
• W3169969992 type Work @default.
• W3169969992 sameAs 3169969992 @default.
• W3169969992 citedByCount "0" @default.
• W3169969992 crossrefType "journal-article" @default.
• W3169969992 hasAuthorship W3169969992A5007965763 @default.
• W3169969992 hasAuthorship W3169969992A5018636685 @default.
• W3169969992 hasAuthorship W3169969992A5042870457 @default.
• W3169969992 hasAuthorship W3169969992A5058824167 @default.
• W3169969992 hasAuthorship W3169969992A5064582174 @default.
• W3169969992 hasAuthorship W3169969992A5074238976 @default.
• W3169969992 hasAuthorship W3169969992A5083399995 @default.
• W3169969992 hasConcept C119857082 @default.
• W3169969992 hasConcept C142724271 @default.
• W3169969992 hasConcept C154945302 @default.
• W3169969992 hasConcept C17744445 @default.
• W3169969992 hasConcept C189708586 @default.
• W3169969992 hasConcept C19527891 @default.
• W3169969992 hasConcept C199539241 @default.
• W3169969992 hasConcept C2777466982 @default.
• W3169969992 hasConcept C2779473830 @default.
• W3169969992 hasConcept C41008148 @default.
• W3169969992 hasConcept C71924100 @default.
• W3169969992 hasConcept C83867959 @default.
• W3169969992 hasConcept C95190672 @default.
• W3169969992 hasConceptScore W3169969992C119857082 @default.
• W3169969992 hasConceptScore W3169969992C142724271 @default.
• W3169969992 hasConceptScore W3169969992C154945302 @default.
• W3169969992 hasConceptScore W3169969992C17744445 @default.
• W3169969992 hasConceptScore W3169969992C189708586 @default.
• W3169969992 hasConceptScore W3169969992C19527891 @default.
• W3169969992 hasConceptScore W3169969992C199539241 @default.
• W3169969992 hasConceptScore W3169969992C2777466982 @default.
• W3169969992 hasConceptScore W3169969992C2779473830 @default.
• W3169969992 hasConceptScore W3169969992C41008148 @default.
• W3169969992 hasConceptScore W3169969992C71924100 @default.
• W3169969992 hasConceptScore W3169969992C83867959 @default.
• W3169969992 hasConceptScore W3169969992C95190672 @default.
• W3169969992 hasLocation W31699699921 @default.
• W3169969992 hasOpenAccess W3169969992 @default.
• W3169969992 hasPrimaryLocation W31699699921 @default.
• W3169969992 hasRelatedWork W1976882619 @default.
• W3169969992 hasRelatedWork W2899170201 @default.
• W3169969992 hasRelatedWork W2951078712 @default.
• W3169969992 hasRelatedWork W2954583151 @default.
• W3169969992 hasRelatedWork W3024721521 @default.
• W3169969992 hasRelatedWork W3034655770 @default.
• W3169969992 hasRelatedWork W3042782223 @default.
• W3169969992 hasRelatedWork W3086470062 @default.
• W3169969992 hasRelatedWork W3105318844 @default.
• W3169969992 hasRelatedWork W3138745635 @default.
• W3169969992 hasRelatedWork W3144765272 @default.
• W3169969992 hasRelatedWork W3153509619 @default.
• W3169969992 hasRelatedWork W3155158672 @default.
• W3169969992 hasRelatedWork W3159851199 @default.
• W3169969992 hasRelatedWork W3165265064 @default.
• W3169969992 hasRelatedWork W3173574091 @default.
• W3169969992 hasRelatedWork W3194043859 @default.
• W3169969992 hasRelatedWork W3211527358 @default.
• W3169969992 hasRelatedWork W3213172847 @default.
• W3169969992 hasRelatedWork W3214280225 @default.
• W3169969992 isParatext "false" @default.
• W3169969992 isRetracted "false" @default.
• W3169969992 magId "3169969992" @default.
• W3169969992 workType "article" @default.