Evaluation of intracerebral haemorrhages surface area using artificial intelligence in computed tomography

Introduction: AI-based techniques can be used to localize and measure the intracerebral haemorrhage (ICH) in computed tomography (CT). This study aims to develop an automated detection algorithm with higher sensitivity in ICH evaluation in comparison to the conventional method. This indirectly influences the patient’s prognosis by reducing the risk of delay or misdiagnosis. Methods: Selected 50 CT brain images with primary ICH were used for three different measurement approaches including the conventional Kothari method (Conventional), AI-based method (A.I.), and manually marking by the radiologist, which is the ground truth (G.T.). In the automated system, a convolutional neural network (CNN) is used to localize the ICH, followed by a thresholding technique to segment the ICH, and finally, the measurements are computed. The segmentation performance is measured using Dice similarity coefficient. The automated ICH measurements are compared against the ground truth (A.I. vs G.T.). Concurrently, the ICH measurements calculated using the conventional method are also compared against the ground truth (Conventional vs G.T). The t-test analysis is performed between the sum squared error (SSE) of ICH measurements from the automated-ground truth and the conventional-ground truth. Results: The mean volumetric Dice similarity coefficient for the automated segmentation algorithm when tested against the ground truth, is 0.859±0.135. The t-test analysis of the SSE between conventional-ground truth (median=5.45, SD=3.96) and automated-ground truth (median=0.73, SD=0.78) achieved p-value < 0.001 (p=5.10E-9). Conclusion: The automated AI-based algorithm significantly improved the ICH surface area measurement from the CT brain with higher accuracy and efficiency in comparison to the conventional method.

Actions (login required)

View Item