Evaluating artificial intelligence software for delineating hemorrhage extent on CT brain imaging in stroke: AI delineation of ICH on CT

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Adam Vacek - , Edinburgh Napier University (Autor:in)
  • Grant Mair - , Edinburgh Napier University (Autor:in)
  • Philip White - , Newcastle upon Tyne Hospitals NHS Foundation Trust (Autor:in)
  • Philip M Bath - , Nottingham Trent University (Autor:in)
  • Keith W Muir - , University of Glasgow (Autor:in)
  • Rustam Al-Shahi Salman - , Edinburgh Napier University (Autor:in)
  • Chloe Martin - , Edinburgh Napier University (Autor:in)
  • David Dye - , Edinburgh Napier University (Autor:in)
  • Francesca M Chappell - , Edinburgh Napier University (Autor:in)
  • Rüdiger von Kummer - , Institut und Poliklinik für Diagnostische und Interventionelle Neuroradiologie (Autor:in)
  • Malcolm Macleod - , Edinburgh Napier University (Autor:in)
  • Nikola Sprigg - , Nottingham Trent University (Autor:in)
  • Joanna M Wardlaw - , Edinburgh Napier University (Autor:in)

Abstract

BACKGROUND: The extent and distribution of intracranial hemorrhage (ICH) directly affects clinical management. Artificial intelligence (AI) software can detect and may delineate ICH extent on brain CT. We evaluated e-ASPECTS software (Brainomix Ltd.) performance for ICH delineation.

METHODS: We qualitatively assessed software delineation of ICH on CT using patients from six stroke trials. We assessed hemorrhage delineation in five compartments: lobar, deep, posterior fossa, intraventricular, extra-axial. We categorized delineation as excellent, good, moderate, or poor. We assessed quality of software delineation with number of affected compartments in univariate analysis (Kruskall-Wallis test) and ICH location using logistic regression (dependent variable: dichotomous delineation categories 'excellent-good' versus 'moderate-poor'), and report odds ratios (OR) and 95 % confidence intervals (95 %CI).

RESULTS: From 651 patients with ICH (median age 75 years, 53 % male), we included 628 with assessable CTs. Software delineation of ICH extent was 'excellent' in 189/628 (30 %), 'good' in 255/628 (41 %), 'moderate' in 127/628 (20 %), and 'poor' in 57/628 cases (9 %). The quality of software delineation of ICH was better when fewer compartments were affected (Z = 3.61-6.27; p = 0.0063). Software delineation of ICH extent was more likely to be 'excellent-good' quality when lobar alone (OR = 1.56, 95 %CI = 0.97-2.53) but 'moderate-poor' with any intraventricular (OR = 0.56, 95 %CI = 0.39-0.81, p = 0.002) or any extra-axial (OR = 0.41, 95 %CI = 0.27-0.62, p<0.001) extension.

CONCLUSIONS: Delineation of ICH extent on stroke CT scans by AI software was excellent or good in 71 % of cases but was more likely to over- or under-estimate extent when ICH was either more extensive, intraventricular, or extra-axial.

Details

OriginalspracheEnglisch
Aufsatznummer107512
Seitenumfang4
FachzeitschriftJournal of Stroke and Cerebrovascular Diseases
Jahrgang33(2024)
Ausgabenummer1
PublikationsstatusVeröffentlicht - Jan. 2024
Peer-Review-StatusJa

Externe IDs

Scopus 85178235645

Schlagworte

Schlagwörter

  • Humans, Male, Aged, Female, Cerebral Hemorrhage/diagnostic imaging, Artificial Intelligence, Stroke/diagnostic imaging, Intracranial Hemorrhages/diagnostic imaging, Tomography, X-Ray Computed, Software, Neuroimaging

Bibliotheksschlagworte