Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography.
Purpose: To evaluate the impact of an AI-based software trained to detect cerebral aneurysms on TOF-MRA on the diagnostic performance and reading times across readers with varying experience levels. Methods: One hundred eighty-six MRI studies were reviewed by six readers to detect cerebral aneurysms...
| Publicado en: | Neuroradiology Vol. 66; no. 7; pp. 1153 - 1161 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2024
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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=177648389&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177648389 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2024 vid: 66 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177648389 176606617 177648389 177648389 10.1007/s00234-024-03351-w 177648389 ppf: 1153 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography. aug: au: Lehnen, Nils C. Schievelkamp, Arndt-Hendrik Gronemann, Christian Haase, Robert Krause, Inga Gansen, Max Fleckenstein, Tobias Dorn, Franziska Radbruch, Alexander Paech, Daniel affil: Department of Neuroradiology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127, Bonn, Germany sug: subj: Artificial Intelligence Evaluation Software Utilization Cerebral Aneurysm Radiography Cerebral Aneurysm Diagnosis Magnetic Resonance Angiography Methods Diagnosis, Computer Assisted Reading Evaluation Time Factors Work Experiences Human Male Female Adolescence Adult Middle Age Aged Aged, 80 and Over Image Interpretation, Computer Assisted Methods Sensitivity and Specificity Clinical Competence Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: To evaluate the impact of an AI-based software trained to detect cerebral aneurysms on TOF-MRA on the diagnostic performance and reading times across readers with varying experience levels. Methods: One hundred eighty-six MRI studies were reviewed by six readers to detect cerebral aneurysms. Initially, readings were assisted by the CNN-based software mdbrain. After 6 weeks, a second reading was conducted without software assistance. The results were compared to the consensus reading of two neuroradiological specialists and sensitivity (lesion and patient level), specificity (patient level), and false positives per case were calculated for the group of all readers, for the subgroup of physicians, and for each individual reader. Also, reading times for each reader were measured. Results: The dataset contained 54 aneurysms. The readers had no experience (three medical students), 2 years experience (resident in neuroradiology), 6 years experience (radiologist), and 12 years (neuroradiologist). Significant improvements of overall specificity and the overall number of false positives per case were observed in the reading with AI support. For the physicians, we found significant improvements of sensitivity on lesion and patient level and false positives per case. Four readers experienced reduced reading times with the software, while two encountered increased times. Conclusion: In the reading with the AI-based software, we observed significant improvements in terms of specificity and false positives per case for the group of all readers and significant improvements of sensitivity and false positives per case for the physicians. Further studies are needed to investigate the effects of the AI-based software in a prospective setting. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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