Impact of Data Presentation on Physician Performance Utilizing Artificial Intelligence-Based Computer-Aided Diagnosis and Decision Support Systems.
Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 3; pp. 408 - 417 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136223483&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223483 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2019 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223483 136223483 136223483 10.1007/s10278-018-0132-5 136223483 ppf: 408 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Impact of Data Presentation on Physician Performance Utilizing Artificial Intelligence-Based Computer-Aided Diagnosis and Decision Support Systems. aug: au: Jairaj, A. Seymour, S Barinov, L. Becker, M. Lee, E. Schram, A. Goldszal, A. Paster, L. Lane, E. Quigley, D. affil: Koios Medical, New York, NY, USA sug: subj: Artificial Intelligence Diagnosis, Computer Assisted Methods Decision Support Systems, Clinical Breast Neoplasms Ultrasonography Workflow Human Biopsy ROC Curve Kendall's tau Technology Machine Learning ab: Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions sent for biopsy (PPV3) and specificity. A recent study demonstrated that incorporating an AI-based decision support (DS) system into US image analysis could help improve US diagnostic performance. While the DS system is promising, its efficacy in terms of its impact also needs to be measured when integrated into existing clinical workflows. The current study evaluates workflow schemas for DS integration and its impact on diagnostic accuracy. The impact on two different reading methodologies, sequential and independent, was assessed. This study demonstrates significant accuracy differences between the two workflow schemas as measured by area under the receiver operating curve (AUC), as well as inter-operator variability differences as measured by Kendall's tau-b. This evaluation has practical implications on the utilization of such technologies in diagnostic environments as compared to previous studies. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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