Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach.
This study aims to evaluate an AI model designed to automatically classify skull fractures and visualize segmentation on emergent CT scans. The model's goal is to boost diagnostic accuracy, alleviate radiologists' workload, and hasten diagnosis, thereby enhancing patient outcomes. Unique to this res...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 31 - 47 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471459&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471459 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471459 184471459 184471459 10.1007/s10278-024-01156-0 184471459 ppf: 31 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach. aug: au: Lu, Chia-Yin Wang, Yu-Hsin Chen, Hsiu-Ling Goh, Yu-Xin Chiu, I-Min Hou, Ya-Yuan Kuo, Kuei-Hong Lin, Wei-Che affil: https://ror.org/02verss31 Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Kaohsiung, Taiwan sug: subj: Skull Fractures Diagnosis Skull Fractures Radiography Radiographic Image Interpretation, Computer-Assisted Methods Artificial Intelligence Evaluation Skull Fractures Classification Tomography, X-Ray Computed Human Male Female Middle Age Retrospective Design Brain Radiography Descriptive Statistics Paired T-Tests Data Analysis Software Funding Source Middle Aged: 45-64 years Male Female ab: This study aims to evaluate an AI model designed to automatically classify skull fractures and visualize segmentation on emergent CT scans. The model's goal is to boost diagnostic accuracy, alleviate radiologists' workload, and hasten diagnosis, thereby enhancing patient outcomes. Unique to this research, both pediatric and post-operative patients were not excluded, and diagnostic durations were analyzed. Our testing dataset for the observer studies involved 671 patients, with a mean age of 58.88 years and fairly balanced gender representation. Model 1 of our AI algorithm, trained with 1499 fracture-positive cases, showed a sensitivity of 0.94 and specificity of 0.87, with a DICE score of 0.65. Implementing post-processing rules (specifically Rule B) improved the model's performance, resulting in a sensitivity of 0.94, specificity of 0.99, and a DICE score of 0.63. AI-assisted diagnosis resulted in significantly enhanced performance for all participants, with sensitivity almost doubling for junior radiology residents and other specialists. Additionally, diagnostic durations were significantly reduced (p < 0.01) with AI assistance across all participant categories. Our skull fracture detection model, employing a segmentation approach, demonstrated high performance, enhancing diagnostic accuracy and efficiency for radiologists and clinical physicians. This underlines the potential of AI integration in medical imaging analysis to improve patient care. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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