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dc.contributor.authorÖzçelik, Neslihan
dc.contributor.authorÖzçelik, Ali Erdem
dc.contributor.authorBülbül, Yılmaz
dc.contributor.authorÖztuna, Funda
dc.contributor.authorÖzlü, Tevfik
dc.date.accessioned2020-12-19T19:32:16Z
dc.date.available2020-12-19T19:32:16Z
dc.date.issued2020
dc.identifier.citationOzcelik, N., Ozcelik, A. E., Bulbul, Y., Oztuna, F., & Ozlu, T. (2020). Can artificial intelligence distinguish between malignant and benign mediastinal lymph nodes using sonographic features on EBUS images?. Current medical research and opinion, 36(12), 2019–2024. https://doi.org/10.1080/03007995.2020.1837763en_US
dc.identifier.issn0300-7995
dc.identifier.issn1473-4877
dc.identifier.urihttps://doi.org/10.1080/03007995.2020.1837763
dc.identifier.urihttps://hdl.handle.net/11436/971
dc.descriptionOZCELIK, Neslihan/0000-0002-4672-6179en_US
dc.descriptionWOS: 000583594300001en_US
dc.descriptionPubMed: 33054411en_US
dc.description.abstractAims This study aimed to develop a new intelligent diagnostic approach using an artificial neural network (ANN). Moreover, we investigated whether the learning-method-guided quantitative analysis approach adequately described mediastinal lymphadenopathies on endobronchial ultrasound (EBUS) images. Methods in total, 345 lymph nodes (LNs) from 345 EBUS images were used as source input datasets for the application group. the group consisted of 300 and 45 textural patterns as input and output variables, respectively. the input and output datasets were processed using MATLAB. All these datasets were utilized for the training and testing of the ANN. Results the best diagnostic accuracy was 82% of that obtained from the textural patterns of the LNs pattern (89% sensitivity, 72% specificity, and 78.2% area under the curve). the negative predictive values were 81% compared to the corresponding positive predictive values of 83%. Due to the application group's pattern-based evaluation, the LN pattern was statistically significant (p = .002). Conclusions the proposed intelligent approach could be useful in making diagnoses. Further development is required to improve the diagnostic accuracy of the visual interpretation.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francis Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectEndobronchial ultrasounden_US
dc.subjectInterventional pulmonologyen_US
dc.subjectLung canceren_US
dc.titleCan artificial intelligence distinguish between malignant and benign mediastinal lymph nodes using sonographic features on EBUS images?en_US
dc.typearticleen_US
dc.contributor.departmentRTEÜ, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.contributor.institutionauthorÖzçelik, Neslihan
dc.contributor.institutionauthorÖzçelik, Ali Erdem
dc.identifier.doi10.1080/03007995.2020.1837763
dc.relation.journalCurrent Medical Research and Opinionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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