FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048378651> ?p ?o ?g. }

• W3048378651 abstract "Abstract Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland–Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists." @default.
• W3048378651 created "2020-08-18" @default.
• W3048378651 creator A5007143993 @default.
• W3048378651 creator A5008966257 @default.
• W3048378651 creator A5011863700 @default.
• W3048378651 creator A5031859542 @default.
• W3048378651 creator A5040305341 @default.
• W3048378651 creator A5058218881 @default.
• W3048378651 creator A5059748507 @default.
• W3048378651 creator A5075196521 @default.
• W3048378651 creator A5084832433 @default.
• W3048378651 date "2020-08-12" @default.
• W3048378651 modified "2023-10-16" @default.
• W3048378651 title "Development and clinical application of deep learning model for lung nodules screening on CT images" @default.
• W3048378651 cites W130099911 @default.
• W3048378651 cites W1937824714 @default.
• W3048378651 cites W1944881280 @default.
• W3048378651 cites W1950640967 @default.
• W3048378651 cites W1973242649 @default.
• W3048378651 cites W1983785604 @default.
• W3048378651 cites W1986649315 @default.
• W3048378651 cites W1997087602 @default.
• W3048378651 cites W2017898137 @default.
• W3048378651 cites W2022232296 @default.
• W3048378651 cites W2034743729 @default.
• W3048378651 cites W2067123389 @default.
• W3048378651 cites W2074086437 @default.
• W3048378651 cites W2102634410 @default.
• W3048378651 cites W2112467442 @default.
• W3048378651 cites W2120903075 @default.
• W3048378651 cites W2147757711 @default.
• W3048378651 cites W2169296882 @default.
• W3048378651 cites W2322371438 @default.
• W3048378651 cites W2336466552 @default.
• W3048378651 cites W2406413993 @default.
• W3048378651 cites W2490901342 @default.
• W3048378651 cites W2524399695 @default.
• W3048378651 cites W2550664648 @default.
• W3048378651 cites W2557738935 @default.
• W3048378651 cites W2573467917 @default.
• W3048378651 cites W2581082771 @default.
• W3048378651 cites W2584017349 @default.
• W3048378651 cites W2594318146 @default.
• W3048378651 cites W2608876019 @default.
• W3048378651 cites W2636128649 @default.
• W3048378651 cites W2751696055 @default.
• W3048378651 cites W2762406675 @default.
• W3048378651 cites W2765571304 @default.
• W3048378651 cites W2770261599 @default.
• W3048378651 cites W2772723798 @default.
• W3048378651 cites W2790246571 @default.
• W3048378651 cites W2791142503 @default.
• W3048378651 cites W2793538065 @default.
• W3048378651 cites W2793564293 @default.
• W3048378651 cites W2800126561 @default.
• W3048378651 cites W2801761532 @default.
• W3048378651 cites W2805698644 @default.
• W3048378651 cites W2806867168 @default.
• W3048378651 cites W2888316531 @default.
• W3048378651 cites W2896745856 @default.
• W3048378651 cites W3098977020 @default.
• W3048378651 doi "https://doi.org/10.1038/s41598-020-70629-3" @default.
• W3048378651 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7423892" @default.
• W3048378651 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32788705" @default.
• W3048378651 hasPublicationYear "2020" @default.
• W3048378651 type Work @default.
• W3048378651 sameAs 3048378651 @default.
• W3048378651 citedByCount "51" @default.
• W3048378651 countsByYear W30483786512020 @default.
• W3048378651 countsByYear W30483786512021 @default.
• W3048378651 countsByYear W30483786512022 @default.
• W3048378651 countsByYear W30483786512023 @default.
• W3048378651 crossrefType "journal-article" @default.
• W3048378651 hasAuthorship W3048378651A5007143993 @default.
• W3048378651 hasAuthorship W3048378651A5008966257 @default.
• W3048378651 hasAuthorship W3048378651A5011863700 @default.
• W3048378651 hasAuthorship W3048378651A5031859542 @default.
• W3048378651 hasAuthorship W3048378651A5040305341 @default.
• W3048378651 hasAuthorship W3048378651A5058218881 @default.
• W3048378651 hasAuthorship W3048378651A5059748507 @default.
• W3048378651 hasAuthorship W3048378651A5075196521 @default.
• W3048378651 hasAuthorship W3048378651A5084832433 @default.
• W3048378651 hasBestOaLocation W30483786511 @default.
• W3048378651 hasConcept C108583219 @default.
• W3048378651 hasConcept C119857082 @default.
• W3048378651 hasConcept C126322002 @default.
• W3048378651 hasConcept C126838900 @default.
• W3048378651 hasConcept C142724271 @default.
• W3048378651 hasConcept C151730666 @default.
• W3048378651 hasConcept C154945302 @default.
• W3048378651 hasConcept C1862650 @default.
• W3048378651 hasConcept C19527891 @default.
• W3048378651 hasConcept C2776256026 @default.
• W3048378651 hasConcept C2776731575 @default.
• W3048378651 hasConcept C2777405583 @default.
• W3048378651 hasConcept C2777714996 @default.
• W3048378651 hasConcept C2779974597 @default.
• W3048378651 hasConcept C41008148 @default.
• W3048378651 hasConcept C50644808 @default.
• W3048378651 hasConcept C544519230 @default.

• next