■有效且准确地检测脑微出血(CMBs)对于诊断痴呆症至关重要,中风,和创伤性脑损伤。手动CMB检测,然而,既耗时又容易出错。这项研究评估了一种新颖的人工智能(AI)软件,该软件旨在使用磁敏感加权成像(SWI)自动检测CMBs。
■来自265名患者的SWI数据,其中206人有中风史,其他人有各种其他病史,包括高血压,糖尿病,高脂血症,脑出血,脑血管畸形,肿瘤,和炎症,2015年1月至2018年12月收集,进行了分析。两名独立的放射科医生最初审查了图像以识别和计数CMB的数量。随后,使用自动CMB检测软件处理图像.然后由放射科医师审查生成的报告。两位放射科医生最终达成共识,在对图像进行第二次审查后获得的,用于比较从初始手动检测和自动CMB检测软件获得的结果。使用Pearson卡方检验比较了有或没有CMBs的患者以及放射科医师和自动CMB检测软件之间的单个CMBs的检测灵敏度和精度差异。
■在148例患者(71.4±10.7年,100名男性)来自分析的SWI数据。虽然放射科医生确定了139例CMBs,CMB自动检测软件检出145例。然而,与手动检测相比,自动CMB检测软件在确定CMBs患者方面的敏感性和特异性没有统计学差异(P=0.656和P=0.212,卡方检验).然而,放射科医生确定了93名没有CMB的患者,而自动CMB检测软件检测到121名没有CMB的患者,差异有统计学意义(P=0.016,卡方检验)。就个人CMB而言,放射科医师发现1,284例,而自动CMB检测软件检测到1,677例CMB.人与全自动CMB检测软件的检测灵敏度分别为75.5%和96.5%(P<0.001,卡方检验),准确率分别为92.2%和86.0%(P<0.001,卡方检验),分别。值得注意的是,当CMB数量较高(超过30个)时,放射科医师更有可能忽视CMB.
■自动CMB检测软件被证明是CMB检测和定量的有效工具。它显示出比放射科医生更高的灵敏度,特别是在检测微小的CMB和CMB患病率高的病例中。
UNASSIGNED: Efficiently and accurately detecting cerebral
microbleeds (CMBs) is crucial for diagnosing dementia, stroke, and traumatic brain injury. Manual CMB detection, however, is time-consuming and error-prone. This study evaluates a novel artificial intelligence (AI) software designed for the automated detection of CMBs using susceptibility weighted imaging (SWI).
UNASSIGNED: The SWI data from 265 patients, 206 of whom had a history of stroke and others of whom presented a variety of other medical histories, including hypertension, diabetes, hyperlipidemia, cerebral hemorrhage, intracerebral vascular malformations, tumors, and inflammation, collected between January 2015 and December 2018, were analyzed. Two independent radiologists initially reviewed the images to identify and count the number of CMBs. Subsequently, the images were processed using an automatic CMB detection software. The generated reports were then reviewed by the radiologists. A final consensus between the two radiologists, obtained after a second review of the images, was used to compare results obtained from the initial manual detection and those of the automatic CMB detection software. The differences of detection sensitivity and precision for patients with or without CMBs and for individual CMBs between the radiologist and the automatic CMB detection software were compared using Pearson chi-squared tests.
UNASSIGNED: A total of 1,738 CMBs were detected among 148 patients (71.4±10.7 years, 100 males) from the analyzed SWI data. While the radiologists identified 139 cases with CMBs, the automatic CMB detection software detected 145 cases. Nevertheless, there was no statistical difference in the sensitivity and specificity of the automatic CMB detection software compared to manual detection in determining patients with CMBs (P=0.656 and P=0.212, chi-square test). However, the radiologist identified 93 patients without CMBs, while the automatic CMB detection software detected 121 patients without CMBs, exhibiting a statistically significant difference (P=0.016, chi-square test). In terms of individual CMBs, the radiologists found 1,284, whereas the automatic CMB detection software detected 1,677 CMBs. The detection sensitivity for human versus the automatic CMB detection software were 75.5% and 96.5% respectively (P<0.001, chi-square test), while the precision rates were 92.2% and 86.0% (P<0.001, chi-square test), respectively. Notably, the radiologists were more likely to overlook CMBs when the number of CMBs was high (above 30).
UNASSIGNED: The automatic CMB detection software proved to be an effective tool for the detection and quantification of CMBs. It demonstrated higher sensitivity than the radiologists, especially in detecting minuscule CMBs and in cases with high CMB prevalence.