FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220804745> ?p ?o ?g. }

• W4220804745 abstract "The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT.In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model.All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively.Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images." @default.
• W4220804745 created "2022-04-03" @default.
• W4220804745 creator A5004493619 @default.
• W4220804745 creator A5005660358 @default.
• W4220804745 creator A5040348993 @default.
• W4220804745 creator A5060795328 @default.
• W4220804745 creator A5069314490 @default.
• W4220804745 creator A5086567026 @default.
• W4220804745 creator A5088258012 @default.
• W4220804745 date "2022-03-18" @default.
• W4220804745 modified "2023-10-17" @default.
• W4220804745 title "Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography" @default.
• W4220804745 cites W1063657237 @default.
• W4220804745 cites W1580314556 @default.
• W4220804745 cites W1988873933 @default.
• W4220804745 cites W1998166655 @default.
• W4220804745 cites W2001659713 @default.
• W4220804745 cites W2004376208 @default.
• W4220804745 cites W2014086933 @default.
• W4220804745 cites W2016621996 @default.
• W4220804745 cites W2016949052 @default.
• W4220804745 cites W2027819506 @default.
• W4220804745 cites W2044573147 @default.
• W4220804745 cites W2071738126 @default.
• W4220804745 cites W2076907871 @default.
• W4220804745 cites W2079674597 @default.
• W4220804745 cites W2081933348 @default.
• W4220804745 cites W2084269726 @default.
• W4220804745 cites W2088333572 @default.
• W4220804745 cites W2099676413 @default.
• W4220804745 cites W2106179110 @default.
• W4220804745 cites W2112841401 @default.
• W4220804745 cites W2116584347 @default.
• W4220804745 cites W2118147646 @default.
• W4220804745 cites W2129663164 @default.
• W4220804745 cites W2131831965 @default.
• W4220804745 cites W2139277921 @default.
• W4220804745 cites W2166521422 @default.
• W4220804745 cites W2170889767 @default.
• W4220804745 cites W2173515524 @default.
• W4220804745 cites W2752814753 @default.
• W4220804745 cites W2767236661 @default.
• W4220804745 cites W2794327830 @default.
• W4220804745 cites W2794679997 @default.
• W4220804745 cites W2802442470 @default.
• W4220804745 cites W2905226353 @default.
• W4220804745 cites W2921560160 @default.
• W4220804745 cites W2968063452 @default.
• W4220804745 cites W3014641072 @default.
• W4220804745 cites W3023024079 @default.
• W4220804745 cites W3093301038 @default.
• W4220804745 cites W3105395935 @default.
• W4220804745 cites W3140743785 @default.
• W4220804745 cites W3155088231 @default.
• W4220804745 cites W3160098307 @default.
• W4220804745 cites W3163495318 @default.
• W4220804745 cites W3208129528 @default.
• W4220804745 cites W40384799 @default.
• W4220804745 cites W4233520842 @default.
• W4220804745 doi "https://doi.org/10.3389/fmed.2022.773041" @default.
• W4220804745 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35372415" @default.
• W4220804745 hasPublicationYear "2022" @default.
• W4220804745 type Work @default.
• W4220804745 citedByCount "3" @default.
• W4220804745 countsByYear W42208047452022 @default.
• W4220804745 countsByYear W42208047452023 @default.
• W4220804745 crossrefType "journal-article" @default.
• W4220804745 hasAuthorship W4220804745A5004493619 @default.
• W4220804745 hasAuthorship W4220804745A5005660358 @default.
• W4220804745 hasAuthorship W4220804745A5040348993 @default.
• W4220804745 hasAuthorship W4220804745A5060795328 @default.
• W4220804745 hasAuthorship W4220804745A5069314490 @default.
• W4220804745 hasAuthorship W4220804745A5086567026 @default.
• W4220804745 hasAuthorship W4220804745A5088258012 @default.
• W4220804745 hasBestOaLocation W42208047451 @default.
• W4220804745 hasConcept C108583219 @default.
• W4220804745 hasConcept C126322002 @default.
• W4220804745 hasConcept C126838900 @default.
• W4220804745 hasConcept C151730666 @default.
• W4220804745 hasConcept C154945302 @default.
• W4220804745 hasConcept C163716698 @default.
• W4220804745 hasConcept C2775842073 @default.
• W4220804745 hasConcept C2776731575 @default.
• W4220804745 hasConcept C2777415128 @default.
• W4220804745 hasConcept C2780244788 @default.
• W4220804745 hasConcept C2989005 @default.
• W4220804745 hasConcept C41008148 @default.
• W4220804745 hasConcept C544519230 @default.
• W4220804745 hasConcept C58471807 @default.
• W4220804745 hasConcept C71924100 @default.
• W4220804745 hasConcept C86803240 @default.
• W4220804745 hasConcept C89600930 @default.
• W4220804745 hasConceptScore W4220804745C108583219 @default.
• W4220804745 hasConceptScore W4220804745C126322002 @default.
• W4220804745 hasConceptScore W4220804745C126838900 @default.
• W4220804745 hasConceptScore W4220804745C151730666 @default.
• W4220804745 hasConceptScore W4220804745C154945302 @default.
• W4220804745 hasConceptScore W4220804745C163716698 @default.
• W4220804745 hasConceptScore W4220804745C2775842073 @default.
• W4220804745 hasConceptScore W4220804745C2776731575 @default.

• next