Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head.

Autor:	Newbury-Chaet I; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts., Mercaldo SF; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts.; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Chin JK; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts., Ghatak A; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts., Halle MA; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts., MacDonald AL; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts., Buch K; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Conklin J; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Mehan WA Jr; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Pomerantz S; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts.; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Rincon S; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Dreyer KJ; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts.; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Bizzo BC; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts.; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Radiology (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B.), Massachusetts General Hospital, Boston, Massachusetts., Hillis JM; From the Data Science Office (I.N.-C., S.F.M., J.K.C., A.G., M.A.H., A.L.M., S.P., K.J.D., B.C.B., J.M.H,), Massachusetts General Brigham, Boston, Massachusetts james.hillis@mgh.harvard.edu.; Harvard Medical School (S.F.M., K.B., J.C., W.A.M., S.P., S.R., K.J.D., B.C.B., J.M.H.), Boston, Massachusetts.; Department of Neurology (J.M.H.), Massachusetts General Hospital, Boston, Massachusetts.
Jazyk:	angličtina
Zdroj:	AJNR. American journal of neuroradiology [AJNR Am J Neuroradiol] 2024 Oct 03; Vol. 45 (10), pp. 1528-1535. Date of Electronic Publication: 2024 Oct 03.
DOI:	10.3174/ajnr.A8358
Abstrakt:	Background and Purpose: Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) with consensus neuroradiologists' interpretations in detecting mass effect and vasogenic edema. Materials and Methods: A retrospective stand-alone performance assessment was conducted on data sets of noncontrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients 18 years of age or older from 5 hospitals in the United States. The positive cases were selected consecutively on the basis of the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up-to-three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the artificial intelligence model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The artificial intelligence model separately received thin (≤1.5 mm) and/or thick (>1.5 and ≤5 mm) axial series. Results: The 2 cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The artificial intelligence model identified mass effect with a sensitivity of 96.6% (95% CI, 94.9%-98.2%) and a specificity of 89.8% (95% CI, 84.7%-94.2%) for the thin series, and 95.3% (95% CI, 93.5%-96.8%) and 93.1% (95% CI, 89.1%-96.6%) for the thick series. It identified vasogenic edema with a sensitivity of 90.2% (95% CI, 82.0%-96.7%) and a specificity of 93.5% (95% CI, 88.9%-97.2%) for the thin series, and 90.0% (95% CI, 84.0%-96.0%) and 95.5% (95% CI, 92.5%-98.0%) for the thick series. The corresponding areas under the curve were at least 0.980. Conclusions: The assessed artificial intelligence model accurately identified mass effect and vasogenic edema in this CT data set. It could assist the clinical workflow by prioritizing interpretation of cases with abnormal findings, possibly benefiting patients through earlier identification and subsequent treatment. (© 2024 by American Journal of Neuroradiology.)
Databáze:	MEDLINE
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