Deep Learning‐Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset

Abstract

This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic therapy for acute cerebral infarct with unknown time of onset. A total of 100 patients with acute cerebral infarct with unknown time of onset were selected as the research objects. All patients received thrombolytic therapy. According to different image processing methods, they were divided into the algorithm group (artificial intelligence algorithm-based image processing group) and the control group (conventional method-based image processing group). After that, the evaluations of effectiveness and safety of thrombolytic therapy for the patients with acute cerebral infarct in the two groups were compared. The research results demonstrated that artificial intelligence algorithm-based CTP imaging showed significant diagnostic effects and the image quality in the algorithm group was remarkably higher than that in the control group ( P < 0.05 ). Besides, the overall image quality of algorithm group was relatively higher. The differences in the National Institute of Health stroke scale (NIHSS) scores for the two groups indicated that the thrombolytic effect on the algorithm group was superior to that on the control group. Thrombolytic therapy for the algorithm group showed therapeutic effects on neurologic impairment. The symptomatic intracranial hemorrhage rate of the algorithm group within 24 hours was lower than the hemorrhage conversion rate of the control group, and the difference between the two groups was 14%. The data differences between the two groups showed statistical significance ( P < 0.05 ). The results demonstrated that the safety of guided thrombolytic therapy for the algorithm group was higher than that in the control group. To sum up, deep learning-based CTP images showed the clinical application values in the diagnosis of cerebral infarct.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

“…Immediate effects on users include the workload placed on clinicians to review and confirm AI output including the potential for errors [80,81]. We found only one in five studies of assistive systems examined the effects on clinician time, and the two studies that assessed safety involved autonomous systems [58,82]. The long-term effects on users include the loss of situational awareness and skill degradation which are well-documented effects of automation in other domains [9].…”

“…The system was found to be highly accurate when used to scan all head and neck CT angiograms over a 15-month period. Hu et al's [82] examination of the safety and effectiveness of an AI to improve the quality of CT perfusion images reported improvements in image quality and thrombolytic therapy of acute cerebral infarct (14%).…”

Predictive Value of CT Perfusion in Hemorrhagic Transformation after Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

“…In addition, the information lost is inevitable in the post-processing of CTP images [53]. CTP could be integrated with other clinical information to achieve a more individualized, precise, and stable multi-parameter diagnostic framework for predicting HT [54][55][56].…”

“…MMPs could strongly degrade TJs and the basement membrane [81,82], aggravate BBB injury, and increase the possibility of HT. In addition, hypoxia also increases MMP9 activity, ultimately inducing more severe brain damage [54]. At the same time, a more severe hypoperfusion status might reflect collateral inadequacy [83,84].…”