Automated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study

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dc.catalogadorjwg
dc.contributor.authorClausdorff Fiedler, Hans Jurgen
dc.contributor.authorPrager, Ross
dc.contributor.authorSmith, Delaney
dc.contributor.authorWu, Derek
dc.contributor.authorDave, Chintan
dc.contributor.authorTschirhart, Jared
dc.contributor.authorWu, Ben
dc.contributor.authorVanBerlo, Blake
dc.contributor.authorMalthaner, Richard
dc.contributor.authorArntfield, Robert
dc.date.accessioned2024-03-14T17:24:49Z
dc.date.available2024-03-14T17:24:49Z
dc.date.issued2024
dc.description.abstractBackground: Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathologic features, artificial intelligence (AI)-assisted imaging has been adopted; however, the diagnostic accuracy of AI-assisted LUS (AI-LUS) deployed in real time to diagnose pneumothorax remains unknown. Research Question: In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? Study Design and Methods: We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared with a reference standard of expert consensus. Results: Two hundred forty-one lung sliding evaluations were derived from 62 patients. AI-LUS showed a sensitivity of 0.921 (95% CI, 0.792-0.973), specificity of 0.802 (95% CI, 0.735-0.856), area under the receiver operating characteristic curve of 0.885 (95% CI, 0.828-0.956), and accuracy of 0.824 (95% CI, 0.766-0.870) for the diagnosis of absent lung sliding. Interpretation: In this study, real-time AI-LUS showed high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.
dc.fechaingreso.objetodigital2024-09-05
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.chest.2024.02.011
dc.identifier.urihttp://dx.doi.org/10.1016/j.chest.2024.02.011
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84412
dc.identifier.wosidWOS:001291087800001
dc.information.autorucEscuela de Medicina; Clausdorff Fiedler, Hans Jurgen; 0000-0002-0571-7815; 172140
dc.language.isoen
dc.nota.accesocontenido parcial
dc.rightsacceso restringido
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subjectArtificial intelligence
dc.subjectLung sliding
dc.subjectLung ultrasound
dc.subjectPneumothorax
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleAutomated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study
dc.typeartículo
sipa.codpersvinculados172140
sipa.trazabilidadORCID;2024-02-19
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