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Background and purpose Methods Results Conclusions Parent artery atherosclerosis is an important aetiology of recent subcortical ischaemic stroke (RSIS). However, comparisons of RSIS with different degrees of parent artery atherosclerosis are lacking.Prospectively collected data from our multicentre cohort (all were tertiary centres) of the Stroke Imaging Package Study between 2015 and 2017 were retrospectively reviewed. The patients with RSIS defined as a single clinically relevant diffusion‐weighted imaging positive lesion in the territory of lenticulostriate arteries were categorized into three subgroups: (1) normal middle cerebral artery (MCA) on magnetic resonance angiography and high‐resolution magnetic resonance imaging (HR‐MRI); (2) low‐grade MCA atherosclerosis (normal or <50% stenosis on magnetic resonance angiography and with MCA plaques on HR‐MRI); (3) steno‐occlusive MCA atherosclerosis (stenosis ≥50% or occlusion). The primary outcome was 90‐day functional dependence (modified Rankin Scale score >2). The clinical and imaging findings were compared between subgroups.A total of 239 patients (median age 60.0 [52.0–67.0] years, 72% male) were enrolled, including 140 with normal MCA, 64 with low‐grade MCA atherosclerosis and 35 with steno‐occlusive MCA atherosclerosis. Patients with steno‐occlusive MCA atherosclerosis had the largest infarct volume. Low‐grade MCA atherosclerosis was independently associated with cerebral microbleeding, more severe perivascular spaces in basal ganglia and higher total cerebral small vessel disease burden. Low‐grade MCA atherosclerosis was an independent determinant of 90‐day functional dependence (odds ratio 3.897; 95% confidence interval 1.309–11.604).Our study suggested RSIS with varying severity of parent artery atherosclerosis exhibits distinctive clinical and neuroimaging characteristics, with low‐grade MCA atherosclerosis associating with higher cerebral small vessel disease burden and worse prognosis. [ABSTRACT FROM AUTHOR]

Objective To explore the application value of machine learning in preliminary evaluation of the degree of co-ronary artery stenosis caused by different types of plaques. Methods Eighty patients who underwent coronary CT angiography (CCTA) and coronary angiography (CAG) in the following 14 d from January 2020 to October 2022 were selected. During CCTA, 103 coronary artery stenosis sites were randomly selected and divided into calcified plaque group (38 sites), non-calcified plaque group (34 sites), and mixed plaque group (31 sites) according to plaque properties. Subjective evaluation (SA), post-processing workstation measurement (AW), artificial intelligence (AI), and SA combined with AI (Semi-AI) were used to assess the degree of coronary artery stenosis caused by plaques in each group. CAG results were used as the gold standard for the degree of coronary artery stenosis. The coincidence, underestimation, and overestimation rates were calculated based on the gold standard and compared between the four methods. Results Among the four methods, there were no significant differences in the coincidence rate, underestimation rate, and overestimation rate between AI and SA (P>0.008 3). In the evaluation of non-calcified plaque and mixed plaque, the coincidence rate of AI was significantly higher than those of AW and Semi-AI (χ2=7.65-16.20,P0.05). In the evaluation of calcified plaque and mixed plaque, the overestimation rate of Semi-AI was significantly lower than those of the other three methods (χ2=8.77-23.62,P