Detecting discomfort in infants through facial expressions

\u3cp\u3eOBJECTIVE: Detecting discomfort status of infants is particularly clinically relevant. Late treatment of discomfort infants can lead to adverse problems such as abnormal brain development, central nervous system damage and changes in responsiveness of the neuroendocrine and immune systems to stress at maturity. In this study, we exploit deep convolutional neural network (CNN) algorithms to address the problem of discomfort detection for infants by analyzing their facial expressions. APPROACH: A dataset of 55 videos about facial expressions, recorded from 24 infants, is used in our study. Given the limited available data for training, we employ a pre-trained CNN model, which is followed by fine-tuning the networks using a public dataset with labeled facial expressions (the shoulder-pain dataset). The CNNs are further refined with our data of infants. MAIN RESULTS: Using a two-fold cross-validation, we achieve an area under the curve (AUC) value of 0.96, which is substantially higher than the results without any pre-training steps (AUC  =  0.77). Our method also achieves better results than the existing method based on handcrafted features. By fusing individual frame results, the AUC is further improved from 0.96 to 0.98. SIGNIFICANCE: The proposed system has great potential for continuous discomfort and pain monitoring in clinical practice.\u3c/p\u3

Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth

Accurate segmentation of data, derived from intra-oral scans (IOS), is a crucial step in a computer-aided design (CAD) system for many clinical tasks, such as implantology and orthodontics in modern dentistry. In order to reach the highest possible quality, a segmentation model may process a point cloud derived from an IOS in its highest available spatial resolution, especially for performing a valid analysis in finely detailed regions such as the curvatures in border lines between two teeth. In this paper, we propose an end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS. By introducing a non-uniform resampling technique, our proposed model is trained and deployed on the highest available spatial resolution where it learns the local fine details along with the global coarse structure of IOS. Furthermore, the point-wise cross-entropy loss for semantic segmentation of a point cloud is an ill-posed problem, since the relative geometrical structures between the instances (e.g. the teeth) are not formulated. By training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch, we improve the segmentation results considerably. Hence, a heavy post-processing stage for relational and dependency modeling (e.g. iterative energy minimization of a constructed graph) is not required anymore. Our experiments show that the proposed approach improves the performance of our baseline network and outperforms the state-of-the-art networks by achieving 0.94 IOU score

Respiration monitoring for premature neonates in NICU

In this paper, we investigate an automated pipeline to estimate respiration signals from videos for premature infants in neonatal intensive care units (NICUs). Two flow estimation methods, namely the conventional optical flow- and deep learning-based flow estimation methods, were employed and compared to estimate pixel motion vectors between adjacent video frames. The respiratory signal is further extracted via motion factorization. The proposed methods were evaluated by comparing our automated extracted respiration signals to that extracted from chest impedance on videos of five premature infants. The overall average cross-correlation coefficients are 0.70 for the optical flow-based method and 0.74 for the deep flow-based method. The average root mean-squared errors are 6.10 and 4.55 for the optical flow- and the deep flow-based methods, respectively. The experimental results are promising for further investigation and clinical application of the video-based respiration monitoring method for infants in NICU

Automatic and continuous discomfort detection for premature infants in a NICU using video-based motion analysis

Frequent pain and discomfort in premature infants can lead to long-term adverse neurodevelopmental outcomes. Video-based monitoring is considered to be a promising contactless method for identification of discomfort moments. In this study, we propose a video-based method for automated detection of infant discomfort. The method is based on analyzing facial and body motion. Therefore, motion trajectories are estimated from frame to frame using optical flow. For each video segment, we further calculate the motion acceleration rate and extract 18 time- and frequency-domain features characterizing motion patterns. A support vector machine (SVM) classifier is then applied to video sequences to recognize infant status of comfort or discomfort. The method is evaluated using 183 video segments for 11 infants from 17 heel prick events. Experimental results show an AUC of 0.94 for discomfort detection and the average accuracy of 0.86 when combining all proposed features, which is promising for clinical use

Cancer detection in mass spectrometry imaging data by recurrent neural networks

Mass spectrometry imaging (MSI) reveals the localization of a broad scale of compounds ranging from metabolites to proteins in biological tissues. This makes MSI an attractive tool in biomedical research for studying diseases. Computer-aided diagnosis (CAD) systems facilitate the analysis of the molecular profile in tumor tissues to provide a distinctive fingerprint for finding biomarkers. In this paper, the performance of recurrent neural networks (RNNs) is studied on MSI data to exploit their learning capabilities for finding irregular patterns and dependencies in sequential data. In order to design a better CAD model for tumor detection/classification, several configurations of Long Short-Time Memory (LSTM) are examined. The proposed model consists of a 2-layer bidirectional LSTM, each containing 100 LSTM units. The proposed RNN model outperforms the state-of-the-art CNN model by 1.87% and 1.45% higher accuracy in mass spectra classification on lung and bladder cancer datasets with a sixfold faster training time

Queima de biomassa e efeitos sobre a saúde Biomass burning and its effects on health

A primeira idéia que se forma na mente das pessoas e do pesquisador é associar a poluição do ar aos grandes centros urbanos, com a imagem de poluentes sendo eliminados por veículos automotores ou pela chaminé de suas fábricas. Entretanto, uma parcela considerável da população do planeta convive com uma outra fonte de poluição, que atinge preferencialmente os países em desenvolvimento: a queima de biomassa. Este artigo tem como objetivo chamar a atenção do pneumologista, da comunidade e das autoridades para os riscos à saúde da população exposta a essa fonte geradora de poluentes, seja em ambientes internos, seja em ambientes abertos. O presente trabalho caracteriza as principais condições que levam à combustão de biomassa, como a literatura tem registrado os seus efeitos sobre a saúde humana, discutindo os mecanismos fisiopatológicos envolvidos, e finaliza com a apresentação de dois estudos recentes que enfatizam a importância da queima de um tipo específico de biomassa, a palha da cana-de-açúcar, prática comum no interior do Brasil, e sua interferência no perfil de morbidade respiratória da população exposta.<br>The first thought that comes to mind concerning air pollution is related to urban centers where automotive exhausts and the industrial chimneys are the most important sources of atmospheric pollutants. However a significant portion of the earth’s population is exposed to still another source of air pollution, the burning of biomass that primarily affects developing countries. This review article calls the attention of lung specialists, public authorities and the community in general to the health risks entailed in the burning of biomass, be it indoors or outdoors to which the population is exposed. This review describes the main conditions that lead to the burning of biomass and how the literature has recorded its effects on human health discussing the psychopathological mechanisms. Finally two recent studies are presented that emphasize an important type of biomass burning that of the sugar cane straw. This is a common practice in several regions of Brazil changing the respiratory morbidity standards of the population exposed