RT Journal Article
SR Electronic
T1 Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1528
OP 1535
DO 10.3174/ajnr.A8358
VO 45
IS 10
A1 Newbury-Chaet, Isabella
A1 Mercaldo, Sarah F.
A1 Chin, John K.
A1 Ghatak, Ankita
A1 Halle, Madeleine A.
A1 MacDonald, Ashley L.
A1 Buch, Karen
A1 Conklin, John
A1 Mehan, William A.
A1 Pomerantz, Stuart
A1 Rincon, Sandra
A1 Dreyer, Keith J.
A1 Bizzo, Bernardo C.
A1 Hillis, James M.
YR 2024
UL http://www.ajnr.org/content/45/10/1528.abstract
AB 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.AIartificial intelligenceAUCarea under the receiver operating characteristic curveCADtcomputer-assisted triage deviceNPVnegative predictive valuePPVpositive predictive value