TY - JOUR
T1 - Automated CT angiography collateral scoring in anterior large vessel occlusion stroke
T2 - A multireader study
AU - Jabal, Mohamed Sobhi
AU - Kallmes, David F.
AU - Harston, George
AU - Campeau, Norbert
AU - Schwartz, Kara
AU - Messina, Steven
AU - Carr, Carrie
AU - Benson, John
AU - Little, Jason
AU - Nagelschneider, Alex
AU - Madhavan, Ajay
AU - Nasr, Deena
AU - Braksick, Sherry
AU - Klaas, James
AU - Scharf, Eugene
AU - Bilgin, Cem
AU - Brinjikji, Waleed
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2023
Y1 - 2023
N2 - Background: Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring. Materials and Methods: Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers’ performance metrics were analyzed between the e-CTA assisted and unassisted sessions. Results: e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009). Conclusion: Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.
AB - Background: Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring. Materials and Methods: Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers’ performance metrics were analyzed between the e-CTA assisted and unassisted sessions. Results: e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009). Conclusion: Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.
KW - artificial intelligence
KW - computed tomography angiography
KW - Decision support system
KW - ischemic stroke
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85146620629&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146620629&partnerID=8YFLogxK
U2 - 10.1177/15910199221150470
DO - 10.1177/15910199221150470
M3 - Article
C2 - 36650942
AN - SCOPUS:85146620629
SN - 1591-0199
JO - Interventional Neuroradiology
JF - Interventional Neuroradiology
ER -