Department of Medical Imaging, Maternal and Child Health Hospital of Hubei Province, Wuhan, China.
Department of Radiology, General Hospital of China Resources & Wuhan Iron and Steel Corporation, Wuhan, China.
Med Phys. 2022 Aug;49(8):5604-5615. doi: 10.1002/mp.15803. Epub 2022 Jun 22.
Currently, most researchers mainly analyzed coronavirus disease 2019 (COVID-19) pneumonia visually or qualitatively, probably somewhat time-consuming and not precise enough.
This study aimed to excavate more information, such as differences in distribution, density, and severity of pneumonia lesions between males and females in a specific age group using artificial intelligence (AI)-based computed tomography (CT) metrics. Besides, these metrics were incorporated into a clinical regression model to predict the short-term outcome.
The clinical, laboratory information and a series of HRCT images from 49 patients, aged from 20 to 50 years and confirmed with COVID-19, were collected. The volumes and percentages of infection (POIs) among bilateral lungs and each bronchopulmonary segment were extracted using uAI-Discover-NCP software (version R001). The POI in three HU ranges (i.e., <-300, -300-49, and ≥50 HU representing ground-glass opacity [GGO], mixed opacity, and consolidation) were also extracted. Hospital stay was predicted with several POI after adjusting days from illness onset to admission, leucocytes, lymphocytes, C-reactive protein, age, and gender using a multiple linear regression model. A total of 91 patients aged 20-50 from public database were selected.
Right lower lobes had the highest POI, followed by left lower lobes, right upper lobes, middle lobes, and left upper lobes. The distributions in lung lobes and segments were different between the sexes. Men had a higher total POI and GGO of the lungs, but less consolidation than women in initial CT (all p < 0.05). The total POI, percentage of consolidation on initial CT, and changed POI were positively correlated with hospital stay in the model. A total of 91 patients aged 20-50 years in the public database were selected, and AI segmentation was performed. The POI of the lower lobes was obviously higher than that in the upper lobes; the POI of each segment of the right upper lobe in the males was higher than that in the females, which was consistent with the result of the 49 patients previously.
Both men and women had characteristic distributions in lung lobes and bronchopulmonary segments. AI-based CT quantitative metrics can provide more precise information regarding lesion distribution and severity to predict clinical outcome.
目前，大多数研究人员主要通过视觉或定性分析 2019 年冠状病毒病（COVID-19）肺炎，这可能有些耗时且不够精确。
本研究旨在利用基于人工智能（AI）的计算机断层扫描（CT）指标，挖掘更多信息，例如特定年龄组中男性和女性之间肺炎病变的分布、密度和严重程度的差异。此外，这些指标被纳入临床回归模型以预测短期预后。
收集了 49 例年龄在 20 至 50 岁之间并确诊为 COVID-19 的患者的临床、实验室信息和一系列 HRCT 图像。使用 uAI-Discover-NCP 软件（版本 R001）提取双侧肺和每个支气管肺段的感染体积和百分比（POI）。还提取了三个 HU 范围（即，<-300、-300-49 和≥50 HU 分别代表磨玻璃影 [GGO]、混合密度影和实变）中的 POI。使用多元线性回归模型，根据发病至入院天数、白细胞、淋巴细胞、C 反应蛋白、年龄和性别调整后，预测住院时间。从公共数据库中选择了 91 名年龄在 20-50 岁的患者。
右下叶的 POI 最高，其次是左下叶、右上叶、中叶和左上叶。性别之间的肺叶和节段分布不同。男性在初始 CT 中有更高的总 POI 和肺部 GGO，但实变程度低于女性（均 p<0.05）。模型中，总 POI、初始 CT 上的实变百分比和变化的 POI 与住院时间呈正相关。从公共数据库中选择了 91 名年龄在 20-50 岁的患者，并进行了 AI 分割。下叶的 POI 明显高于上叶；男性右上叶各段的 POI 高于女性，与之前的 49 例患者的结果一致。
男性和女性的肺叶和支气管肺段分布均具有特征性。基于 AI 的 CT 定量指标可以提供更精确的病变分布和严重程度信息，以预测临床结局。
