BACKGROUND
Stroke is the second leading cause of death and the leading cause of permanent disability globally. Vietnam is a developing country with a high prevalence of stroke.
OBJECTIVE
This study aimed to present the clinical features and neuroimaging findings in acute cerebral infarction patients using RAPID Artificial Intelligent (RAPID AI) Software Analysis
METHODS
A case series of 54 stroke patients based on data from the electronic medical records who admitted between October 2019 and October 2020, for whom Rapid AI was used to analyze images of stroke.
RESULTS
The results showed that the mean age of patients was 73.39 ± 12.46 years and males comprised 57.4 of the sample. The most common risk factor identified was hypertension (75.9%), followed by atrial fibrillation (24%), diabetes (20%), alcohol (15%), and smoking (9%). The most common clinical signs were hemiparesis in 76% of the patients, followed by dysphasia in 50% and memory loss in 28% of the sample. 7% presented with dizziness and 7% with headache. 6% were unconscious on admission. ASPECTS evaluation showed that 24 (44%) patients had good ASPECTS scores of 8-10, 17 (32%) patients had ASPECTS scores of 5-7, and 13 (24%) patients had ASPECTS scores of 0-4. The number of patients with an infarct core volume <70 mL was 50 (93%), while a mismatch volume of >15 mL was observed in 31 (55%) patients and 22 (41%) patients had a mismatch ratio >1.8. The assessment of CT imaging of thrombi showed 51 cases of anterior cerebral circulation, including 13 (24%) cases diagnosed as ICA, 30 (76%) cases diagnosed as MCA, and 8 (15%) cases diagnosed as SA. There were 10 cases of MCA-M1 (19%), 7 cases of MCA-M2 and MCA-M4 (13%) cases and 6 cases of MCA-M3 (11%), respectively. There were three cases of posterior cerebral circulation, comprising one case of Posterior cerebral artery (PCA) and two cases of Basilar artery (BA) territory infarction.
CONCLUSIONS
A collection of clinical features and neuroradiological assessment based on RAPID Artificial Intelligent (RAPID AI) Software Analysis can be used in identify stroke patients in the hospitals.