Kai Chen
ABSTRACT
Objective: To evaluate the performance of an artificial intelligence (AI)-based medical analysis tool called uAI-HematomaCare in automatically detecting, classifying, and measuring the volume of acute spontaneous intracranial hemorrhage (ICH) on non-contrast-enhanced CT (NCCT) images.
Methods: A 1:2 matched case-control study design was used. The analysis included NCCT images from 70 cases with ICH and 140 non-ICH cases (70 cases with hemorrhage-like hyperdensities). Initially, the algorithm's performance in detecting and classifying acute ICH subtypes was compared to that of two radiologists. The criterion standard was determined by consensus of two blinded neuroradiology experts. Subsequently, the algorithm and two radiologists quantified the volumes of intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), and total ICH using manual segmentation. The performance of the measurement methods was evaluated by calculating the intraclass correlation coefficient (ICC) and absolute error.
Results: The algorithm showed a sensitivity of 100% and specificity of 80% for detecting any ICH, while radiologist 1 showed a sensitivity of 100% and specificity of 99.29%, and radiologist 2 showed a sensitivity of 100% and specificity of 100%. Regarding the classification of ICH subtypes, the algorithm showed a higher sensitivity in detecting IVH (100%) and SAH (100%), but lower specificity (97.71% and 95.19%, respectively) compared to the two radiologists. ICCs ranging from 0.932 to 0.983 indicated good agreement between the algorithm and manual segmentation methods for measuring summation and individual hemorrhage volumes (IPH, IVH, and SAH). Bland-Altman analysis of absolute errors revealed that the algorithm performed well in quantifying the volume of total ICH and the three subtypes, but radiologist 1 had better overall performance.
Conclusion: The AI-based algorithm analysis tool, uAI-HematomaCare, demonstrated a high accuracy in automatically detecting, classifying, and quantifying acute spontaneous ICH. However, further improvements are needed to enhance its ability to differentiate between hyperdensities and ICH.
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Index Terms
- Artificial intelligence based algorithm for automatic diagnosis and quantification of acute spontaneous intracranial hemorrhage
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