FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309691802> ?p ?o ?g. }

• W4309691802 endingPage "1025" @default.
• W4309691802 startingPage "1017" @default.
• W4309691802 abstract "BACKGROUND: Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians’ diagnostic accuracy for nasal bone fractures with the aid of AI technology. OBJECTIVE: This study aims to determine the value of the AI model in improving the diagnostic accuracy for nasal bone fractures compared with manual reading. METHODS: A total of 252 consecutive patients who had undergone facial computed tomography (CT) between January 2020 and January 2021 were enrolled in this study. The presence or absence of a nasal bone fracture was determined by two experienced radiologists. An AI algorithm based on the deep-learning algorithm was engineered, trained and validated to detect fractures on CT images. Twenty readers with various experience were invited to read CT images with or without AI. The accuracy, sensitivity and specificity with the aid of the AI model were calculated by the readers. RESULTS: The deep-learning AI model had 84.78% sensitivity, 86.67% specificity, 0.857 area under the curve (AUC) and a 0.714 Youden index in identifying nasal bone fractures. For all readers, regardless of experience, AI-aided reading had higher sensitivity ([94.00 ± 3.17]% vs [83.52 ± 10.16]%, P< 0.001), specificity ([89.75 ± 6.15]% vs [77.55 ± 11.38]%, P< 0.001) and AUC (0.92 ± 0.04 vs 0.81 ± 0.10, P< 0.001) compared with reading without AI. With the aid of AI, the sensitivity, specificity and AUC were significantly improved in readers with 1–5 years or 6–10 years of experience (all P< 0.05, Table 4). For readers with 11–15 years of experience, no evidence suggested that AI could improve sensitivity and AUC (P= 0.124 and 0.152, respectively). CONCLUSION: The AI model might aid less experienced physicians and radiologists in improving their diagnostic performance for the localisation of nasal bone fractures on CT images." @default.
• W4309691802 created "2022-11-29" @default.
• W4309691802 creator A5005105786 @default.
• W4309691802 creator A5036534201 @default.
• W4309691802 creator A5071986353 @default.
• W4309691802 creator A5088510485 @default.
• W4309691802 creator A5077475298 @default.
• W4309691802 date "2023-05-12" @default.
• W4309691802 modified "2023-10-01" @default.
• W4309691802 title "Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures" @default.
• W4309691802 cites W2020421316 @default.
• W4309691802 cites W2024674268 @default.
• W4309691802 cites W2733840449 @default.
• W4309691802 cites W2795520566 @default.
• W4309691802 cites W2800043213 @default.
• W4309691802 cites W2803212018 @default.
• W4309691802 cites W2886172374 @default.
• W4309691802 cites W2907106331 @default.
• W4309691802 cites W2934730619 @default.
• W4309691802 cites W3008685122 @default.
• W4309691802 cites W3014698192 @default.
• W4309691802 cites W3046342815 @default.
• W4309691802 cites W3081651468 @default.
• W4309691802 cites W3092153824 @default.
• W4309691802 cites W3117124430 @default.
• W4309691802 cites W3132434189 @default.
• W4309691802 cites W3158653447 @default.
• W4309691802 cites W3217211370 @default.
• W4309691802 cites W4200013359 @default.
• W4309691802 cites W4205476689 @default.
• W4309691802 cites W4207017347 @default.
• W4309691802 cites W4220933981 @default.
• W4309691802 doi "https://doi.org/10.3233/thc-220501" @default.
• W4309691802 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36442167" @default.
• W4309691802 hasPublicationYear "2023" @default.
• W4309691802 type Work @default.
• W4309691802 citedByCount "4" @default.
• W4309691802 countsByYear W43096918022023 @default.
• W4309691802 crossrefType "journal-article" @default.
• W4309691802 hasAuthorship W4309691802A5005105786 @default.
• W4309691802 hasAuthorship W4309691802A5036534201 @default.
• W4309691802 hasAuthorship W4309691802A5071986353 @default.
• W4309691802 hasAuthorship W4309691802A5077475298 @default.
• W4309691802 hasAuthorship W4309691802A5088510485 @default.
• W4309691802 hasConcept C112705442 @default.
• W4309691802 hasConcept C126322002 @default.
• W4309691802 hasConcept C126838900 @default.
• W4309691802 hasConcept C141071460 @default.
• W4309691802 hasConcept C154945302 @default.
• W4309691802 hasConcept C17744445 @default.
• W4309691802 hasConcept C199539241 @default.
• W4309691802 hasConcept C2779613018 @default.
• W4309691802 hasConcept C2989005 @default.
• W4309691802 hasConcept C3020132585 @default.
• W4309691802 hasConcept C3020225094 @default.
• W4309691802 hasConcept C41008148 @default.
• W4309691802 hasConcept C43346845 @default.
• W4309691802 hasConcept C554936623 @default.
• W4309691802 hasConcept C58471807 @default.
• W4309691802 hasConcept C71924100 @default.
• W4309691802 hasConceptScore W4309691802C112705442 @default.
• W4309691802 hasConceptScore W4309691802C126322002 @default.
• W4309691802 hasConceptScore W4309691802C126838900 @default.
• W4309691802 hasConceptScore W4309691802C141071460 @default.
• W4309691802 hasConceptScore W4309691802C154945302 @default.
• W4309691802 hasConceptScore W4309691802C17744445 @default.
• W4309691802 hasConceptScore W4309691802C199539241 @default.
• W4309691802 hasConceptScore W4309691802C2779613018 @default.
• W4309691802 hasConceptScore W4309691802C2989005 @default.
• W4309691802 hasConceptScore W4309691802C3020132585 @default.
• W4309691802 hasConceptScore W4309691802C3020225094 @default.
• W4309691802 hasConceptScore W4309691802C41008148 @default.
• W4309691802 hasConceptScore W4309691802C43346845 @default.
• W4309691802 hasConceptScore W4309691802C554936623 @default.
• W4309691802 hasConceptScore W4309691802C58471807 @default.
• W4309691802 hasConceptScore W4309691802C71924100 @default.
• W4309691802 hasIssue "3" @default.
• W4309691802 hasLocation W43096918021 @default.
• W4309691802 hasLocation W43096918022 @default.
• W4309691802 hasOpenAccess W4309691802 @default.
• W4309691802 hasPrimaryLocation W43096918021 @default.
• W4309691802 hasRelatedWork W2073887351 @default.
• W4309691802 hasRelatedWork W2347370180 @default.
• W4309691802 hasRelatedWork W2364996258 @default.
• W4309691802 hasRelatedWork W2382086840 @default.
• W4309691802 hasRelatedWork W2415320528 @default.
• W4309691802 hasRelatedWork W2884294720 @default.
• W4309691802 hasRelatedWork W2994885177 @default.
• W4309691802 hasRelatedWork W4309691802 @default.
• W4309691802 hasRelatedWork W4311931036 @default.
• W4309691802 hasRelatedWork W52484821 @default.
• W4309691802 hasVolume "31" @default.
• W4309691802 isParatext "false" @default.
• W4309691802 isRetracted "false" @default.
• W4309691802 workType "article" @default.