关键词： 无创诊断   诊断准确度   三甲医院   核磁共振图像   人工智能技术   卵巢癌   科研人员   机器学习方法  

摘要： 2月24日从中科院苏州医工所获悉,该所研究人员与复旦大学附属金山医院等8家我国华东、华南、华北三甲医院医院合作,最新研发出一种新的卵巢癌无创诊断方法。运用这种方法,医疗人员仅利用患者的核磁共振图像就可对卵巢癌分类进行精准判断。该研究率先将人工智能技术运用到卵巢癌患者的影像学诊断中。科研人员共收集两大类、共501名卵巢癌患者的核磁影像资料,通过机器学习方法筛选特征、构建模型,最终形成一套智能卵巢癌无创诊断的新方法。将新方法得出的机器诊断结论与6名从业2年至13年影像科医生的诊断比对,结果显示,医生平均诊断准确度为79.5%,机器平均诊断准确度达到91.7%。新方法诊断精度已明显高于人工。

关键词： 塑料   机器学习   Inception卷积神经网络   分类回收  

摘要： 塑料分类回收预测系统主要由垃圾接收装置、垃圾预测分类装置、垃圾压缩装置和垃圾回收储存装置4部分组成,其中垃圾预测分类装置凭借提出的塑料预测分类模型执行塑料分类工作。塑料预测分类模型应用深层次的Inception卷积神经网络,提取高度抽象的关键塑料特征。实验结果表明:塑料分类回收预测系统的预测分类准确率高于传统的预测分类模型约2%。

关键词： 无创诊断   三甲医院   核磁共振图像   人工智能技术   卵巢癌   科研人员   研究团队   机器学习方法  

摘要： 2月24日,中科院苏州医工所研究人员与复旦大学附属金山医院等8家医院合作,最新研发出一种新的卵巢癌无创诊断方法。运用这种方法,医疗人员仅利用患者的核磁共振图像就可对卵巢癌分类进行精准判断。此次,研究团队与我国华东、华南、华北8家三甲医院合作,率先将人工智能技术运用到卵巢癌患者的影像学诊断中。科研人员共收集两大类、共501名卵巢癌患者的核磁影像资料,通过机器学习方法筛选特征、构建模型,最终形成一套智能卵巢癌无创诊断的新方法。

关键词： 阿尔兹海默症   机器学习   L1正则Logistic回归   分类预测  

摘要： 目的:应用机器学习方法,将脑结构磁共振(sMRI)、年龄、性别、受教育年限和MMSE量表评分作为特征,对阿尔兹海默症进行分类预测。方法:特征选择后,用L1正则Logistic回归、L1正则支持向量机、梯度提升树分别对脑sMRI数据进行分类预测,选出最优模型后引入年龄、性别、受教育年限和MMSE量表评分特征优化模型,用10-折交叉验证评价模型性能。结果:L1正则Logistic回归分类效果最好,加入年龄、性别、受教育年限和MMSE评分后预测准确率提高0.89%~11.42%。结论:L1正则化Logistic回归模型的sMRI+年龄+性别+受教育年限+MMSE评分特征集对阿尔兹海默症有更好的分类效果,可作为辅助诊断阿尔兹海默症的依据。

关键词： mathematical modeling   grid-characteristic method   machine learning   neural networks   inverse exploration seismology problem   fracture  

摘要： Inverse problems of fracture exploration seismology are solved using machine learning methods. A single fracture of fixed size and subvertical orientation is considered in the two-dimensional case. The spatial position and the inclination angle of the fracture are determined using a neural network. The training set consists of solutions of direct problems produced by the grid-characteristic method on regular rectangular meshes in the form of synthetic seismograms obtained by measuring the vertical velocity on the surface of the medium.

关键词： classification   coronary artery disease   machine learning   polygenic risk scores   prediction  

摘要： Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effects into account and restricted in that they focused on genetic loci associated with CAD, only. We benchmarked PRS, (penalized) logistic regression, naive Bayes (NB), random forests (RF), support vector machines (SVM), and gradient boosting (GB) on a data set of 7,736 CAD cases and 6,774 controls from Germany to identify the algorithms for most accurate classification of CAD status. The final models were tested on an independent data set from Germany (527 CAD cases and 473 controls). We found PRS to be the best algorithm, yielding an area under the receiver operating curve (AUC) of 0.92 (95% CI [0.90, 0.95], 50,633 loci) in the German test data. NB and SVM (AUC similar to 0.81) performed better than RF and GB (AUC similar to 0.75). We conclude that using PRS to predict CAD is superior to machine learning methods.

关键词： machine learning   microbiome   supervised   unsupervised   deep learning   semi-supervised  

摘要： Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis methods can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.

关键词： Uterine cervical cancer   Radiomics   Machine learning   In-field recurrence   Prognosis  

摘要： Purpose This study aimed to identify the most appropriate volume of interest (VOI) setting in prognostic prediction using pretreatment magnetic resonance imaging (MRI) radiomic analysis for cervical cancer (CC) treated with definitive radiotherapy. Materials and methods The study participants were 87 patients who had undergone pretreatment MRI and definitive radiotherapy for CC. VOItumor was created with tumor alone and VOI+4 mm-VOI+20 mm mechanically expanded by 4-20 mm around each VOItumor in axial T2-weighted images (T2WI) and an apparent diffusion coefficient (ADC) map. A model was constructed to predict recurrence within the irradiation field within 2 years after treatment using imaging features from the VOI of each sequence. Sorting ability was evaluated by area under the receiver operator characteristic curve (AUC-ROC) analysis. Results VOI expansion improved AUC-ROCs compared with the predictive models of VOItumor (0.59 and 0.67 in T2WI and ADC, respectively). The AUC-ROCs of the models with imaging features from expanded VOI+4 mm in T2WI and VOI+4 mm and VOI+8 mm in ADC were 0.82, 0.82, and 0.86, respectively. Conclusion Recurrence could be predicted with high accuracy using expanded VOI for CC treated with definitive radiotherapy, suggesting that including the pathological characteristics of invasive margins in radiomics may improve predictive ability.

关键词： air pollution   particulate matter   machine learning   exposure modeling  

摘要： Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km x 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R-2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R-2 of 0.882. However, its ability to predict spatial variability was weaker, with a R-2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.

关键词： Breast cancer   machine learning   computer-aided diagnosis   histopathology  

摘要： Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer using histopathology images of conventional photomicroscopy. Cancer diagnosis is the identification of images as cancer or noncancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. In this article, different approaches to perform these necessary steps are reviewed. We find that most ML research for breast cancer diagnosis has been focused on deep learning. Based on inferences from the recent research activities, we discuss how ML methods can benefit conventional microscopy-based breast cancer diagnosis. Finally, we discuss the research gaps of ML approaches for the implementation in a real pathology environment and propose future research guidelines.