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Association of AI quantified COVID-19 chest CT and patient outcome.


  • 影响因子:2.34
  • DOI:10.1007/s11548-020-02299-5
  • 作者列表:"Fang X","Kruger U","Homayounieh F","Chao H","Zhang J","Digumarthy SR","Arru CD","Kalra MK","Yan P
  • 发表时间:2021-03-01

PURPOSE:Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS:We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS:AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS:Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.


目的: 严重程度评分是管理新型冠状病毒肺炎肺炎患者的关键步骤。然而,放射科医师的人工定量分析是一项耗时的任务,而定性评估可能是快速但高度主观的。本研究旨在开发基于人工智能 (AI) 的方法来量化疾病严重程度并预测新型冠状病毒肺炎患者结果。 方法: 我们开发了一个基于AI的框架,利用深度神经网络有效地分割肺叶和肺部阴影。每个肺叶内肺部混浊的体积比给出肺叶的严重程度评分,然后用三种不同的机器学习方法预测ICU入院和死亡率。在来自两家医院的数据集上评估所开发的方法 (站点A: 伊朗Firoozgar医院,105名患者; 站点B: 美国马萨诸塞州总医院,88名患者)。 结果: 基于AI的严重程度评分与放射科医师评估的严重程度评分密切相关 (Spearman's等级相关性0.837,[公式: 见正文])。使用基于AI的评分产生显著更高的 ([公式: 参见文本]) ROC曲线下面积 (AUC) 值。开发的AI方法在预测ICU入院方面达到了AUC = 0.813 (95% CI [0.729,0.886]) 和AUC = 0.741 (95% CI [0.640,0.837]) 在两个数据集上的死亡率估计中的最佳性能。 结论: 使用开发的AI方法在胸部CT图像上可以获得准确的严重程度评分。通过一致地量化图像特征,计算的严重程度评分在预测新型冠状病毒肺炎患者结果方面实现了比放射科医生更好的性能。这种开发的严重性评估技术可以扩展到当前大流行之外的其他肺部疾病。



作者列表:["Juan-Carlos PM","Perla-Lidia PP","Stephanie-Talia MM","Mónica-Griselda AM","Luz-María TE"]

METHODS::The ATP binding-cassette superfamily corresponds the mostly transmembrane transporters family found in humans. These proteins actively transport endogenous and exogenous substrates through biological membranes in body tissues, so they have an important role in the regulation of many physiological functions necessary for human homeostasis, as well as in response regulation to several pharmacological substrates. The development of multidrug resistance has become one of the main troubles in conventional chemotherapy in different illnesses including cancer, being the increased efflux of antineoplastic drugs the main reason for this multidrug resistance, with a key role of the ABC superfamily. Likely, the interindividual variability in the pharmacological response among patients is well known, and may be due to intrinsically factors of the disease, genetic and environmental ones. Thus, the understanding of this variability, especially the genetic variability associated with the efficacy and toxicity of drugs, can provide a safer and more effective pharmacological treatment, so ABC genes are considered as important regulators due to their relationship with the reduction in pharmacological response. In this review, updated information about transporters belonging to this superfamily was collected, the possible role of these transporters in cancer, the role of genetic variability in their genes, as well as some therapeutic tools that have been tried to raise against main transporters associated with chemoresistance in cancer.

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作者列表:["Sawada H","Oeda T","Kohsaka M","Tomita S","Umemura A","Park K","Yamamoto K","Kiyohara K"]

METHODS:BACKGROUND:Cholinergic neurotransmission regulates neuroinflammation in Parkinson disease (PD). RESEARCH DESIGN AND METHODS:The authors conducted a delayed-start study of donepezil for cognitive decline in non-demented PD patients. The study consisted of a 96-week randomized placebo-controlled double-blind phase 1, followed by a 24-week donepezil extension phase 2. The primary outcome measure was a change in the Mini-Mental State Examination (MMSE) at week 120. RESULTS:A total of 98 patients were randomly allocated to the early-start (donepezil-to-donepezil) and delayed-start (placebo-to-donepezil) groups. Mean (SD) of the baseline MMSE was 27.6 (2.0) and 28.0 (2.1), respectively. MMSE change at week 120 was better in the early-start group than in the delayed-start group, but the difference was not significant. The MMSE declined in apolipoprotein ε4 carriers, but not in non-carriers, and the factor interaction (intervention × ε4 genotype) was highly significant (P < 0.001). Analyzed with the interaction, the difference was significant (group difference 1.95 [0.33 to 3.57], P = 0.018). The MMSE decline slope in phase 1 was significantly better in the early-start group than in the delayed-start group (P = 0.048). CONCLUSIONS:Cognitive function deteriorated in ε4 carriers, but not in non-carriers, and early-start donepezil may postpone cognitive decline in the former.

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作者列表:["Louvrier A","Terranova L","Meyer C","Meyer F","Euvrard E","Kroemer M","Rolin G"]

METHODS::Since the discovery of dental pulp stem cells, a lot of teams have expressed an interest in dental pulp regeneration. Many approaches, experimental models and biological explorations have been developed, each including the use of stem cells and scaffolds with the final goal being clinical application in humans. In this review, the authors' objective was to compare the experimental models and strategies used for the development of biomaterials for tissue engineering of dental pulp with stem cells. Electronic queries were conducted on PubMed using the following terms: pulp regeneration, scaffold, stem cells, tissue engineering and biomaterial. The extracted data included the following information: the strategy envisaged, the type of stem cells, the experimental models, the exploration or analysis methods, the cytotoxicity or viability or proliferation cellular tests, the tests of scaffold antibacterial properties and take into account the vascularization of the regenerated dental pulp. From the 71 selected articles, 59% focused on the "cell-transplantation" strategy, 82% used in vitro experimentation, 58% in vivo animal models and only one described an in vivo in situ human clinical study. 87% used dental pulp stem cells. A majority of the studies reported histology (75%) and immunohistochemistry explorations (66%). 73% mentioned the use of cytotoxicity, proliferation or viability tests. 48% took vascularization into account but only 6% studied the antibacterial properties of the scaffolds. This article gives an overview of the methods used to regenerate dental pulp from stem cells and should help researchers create the best development strategies for research in this field.

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