Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.
Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5–7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnost...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2392 - 2402 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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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=173050938&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050938 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050938 169931246 173050938 173050938 10.1007/s10278-023-00884-z 173050938 ppf: 2392 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation. aug: au: Barinov, Lev Jairaj, Ajit Middleton, William D. D., Michael Beland Kirsch, Jonathan Filice, Ross W. Reverter, Jordi L. Arguelles, Iñaki Grant, Edward G. affil: https://ror.org/00b30xv10 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA sug: subj: Thyroid Nodule Ultrasonography Thyroid Neoplasms Risk Factors Risk Assessment Methods Decision Support Systems, Clinical Deep Learning Methods Image Interpretation, Computer Assisted Prediction Models Human Male Female Adult Middle Age Aged Funding Source Biopsy, Needle Retrospective Design Record Review Descriptive Statistics ROC Curve Sensitivity and Specificity Pearson's Correlation Coefficient Time Factors Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5–7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based decision support systems to improve physician accuracy, variability, and efficiency. The decision support system (DSS) assessed was Koios DS, which provides automated sonographic nodule descriptor predictions and a direct cancer risk assessment aligned to ACR TI-RADS. The study was conducted retrospectively between (08/2020) and (10/2020). The set of cases used included 650 patients (21% male, 79% female) of age 53 ± 15. Fifteen physicians assessed each of the cases in the set, both unassisted and aided by the DSS. The order of the reading condition was randomized, and reading blocks were separated by a period of 4 weeks. The system's impact on reader accuracy was measured by comparing the area under the ROC curve (AUC), sensitivity, and specificity of readers with and without the DSS with FNA as ground truth. The impact on reader variability was evaluated using Pearson's correlation coefficient. The impact on efficiency was determined by comparing the average time per read. There was a statistically significant increase in average AUC of 0.083 [0.066, 0.099] and an increase in sensitivity and specificity of 8.4% [5.4%, 11.3%] and 14% [12.5%, 15.5%], respectively, when aided by Koios DS. The average time per case decreased by 23.6% (p = 0.00017), and the observed Pearson's correlation coefficient increased from r = 0.622 to r = 0.876 when aided by Koios DS. These results indicate that providing physicians with automated clinical decision support significantly improved diagnostic accuracy, as measured by AUC, sensitivity, and specificity, and reduced inter-reader variability and interpretation times. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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