Convolutional neural network for breathing phase detection in lung sounds

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73-0.88) than expiration (0.63-0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings

Semantic annotation in ubiquitous healthcare skills-based learning environments

This paper describes initial work on developing a semantic annotation system for the augmentation of skills-based learning for Healthcare. Scenario driven skills-based learning takes place in an augmented hospital ward simulation involving a patient simulator known as SimMan. The semantic annotation software enables real-time annotations of these simulations for debriefing of the students, student self study and better analysis of the learning approaches of mentors. A description of the developed system is provided with initial findings and future directions for the work.<br/

Sons respiratórios computorizados em crianças com infeção respiratória do trato inferior : um estudo comparativo

Mestrado em FisioterapiaBackground: Lower respiratory tract infections (LRTI) are the main cause of health burden in the first years of age. To enhance the diagnosis and monitoring of infants with LRTI, researchers have been trying to use the large advantages of conventional auscultation. Computerised respiratory sound analysis (CORSA) is a simple method to detect and characterise Normal Respiratory Sounds (NRS) and Adventitious Respiratory Sounds (ARS). However, if this measure is to be used in the paediatric population, reference values have to be established first. Aim: To compare and characterise NRS and ARS in healthy infants and infants with LRTI. Methods: A cross-sectional descriptive-comparative study was conducted in three institutions. Infants were diagnosed by the paediatrician as presenting or not presenting an LRTI, healthy volunteers were recruited from the institutions. Socio-demographic, anthropometric and cardio-respiratory parameters were collected. Respiratory sounds were recorded with a digital stethoscope. Frequency at maximum intensity (Fmax), maximum intensity (Imax) and mean intensity (Imean) over the whole frequency range were collected to characterise NRS. Location, mean number, type, duration and frequency were collected to characterise ARS. All analysis was performed per breathing phase (i.e., inspiration and expiration). Results: Forty nine infants enrolled in this study: 25 healthy infants (G1) and 24 infants with LRTI. Inspiratory Fmax (G1: M 116.1 Hz IQR [107.2-132.4] vs G2: M 118.9Hz IQR [113.2-128.7], p=0.244) and expiratory frequencies (G1: M 107.3Hz IQR [102.9-116.9] vs G2: M 112.6Hz IQR [106.6-122.6], p= 0.083) slightly higher than their healthy peers. Wheeze occupation rate was statistically significantly different between groups in inspiration (G1: M 0 IQR [0-0.1] vs G2: M 0.2 IQR [0-5.2] p= 0.032) and expiration (G1: M 0 IQR [0-1.9] vs G2: M 1.5 IQR [0.2-6.7] p= 0.015), being the infants with LRTI the ones presenting more wheezes. Conclusion: Computerised respiratory sounds in healthy infants and infants with LRTI presented differences. The main findings indicated that NRS have Fmax higher in infants with LRTI than in healthy infant and Wh% was the characteristic that differ the most between infant with LRTI and healthy infant.Enquadramento: As infeções respiratórias do trato inferior (IRTI) constituem o principal problema de saúde nos primeiros anos de vida das crianças. Desta forma, a investigação tem-se focado no desenvolvimento de medidas objetivas para o diagnóstico de IRTI, utilizando essencialmente as vantagens da auscultação convencional incorporadas numa análise computorizada e automática. Contudo, apesar da análise computorizada de sons respiratórios ser um método simples de deteção e caraterização dos sons respiratórios normais (SRN) e adventícios (SRA), desconhecem-se quais os valores de referência dos sons respiratórios em crianças, o que limita a sua aplicação na prática clínica Objetivos: Caraterizar e comparar os SRN e os SRA em crianças saudáveis e com IRTI. Métodos: Estudo descritivo, comparativo e transversal realizado em três instituições. Eram elegíveis crianças diagnosticadas pelo pediatra com IRTI e voluntários para crianças saudáveis. Foram recolhidos dados sócio demográficos, antropométricos e parâmetros cardiorrespiratórios. Os sons respiratórios foram registados com um estetoscópio digital. Foram analisados diversos parâmetros para os SRN: a frequência na intensidade máxima (Fmax), a intensidade máxima (Imax) e a média da intensidade ao longo de toda a faixa de frequência (Imean). Nos SRA foram analisados: a taxa de ocupação por wheezes (Wh%), a média wheezes (Wh), o número e o tipo Wh, a frequência e a localização Wh por região; o número crackles (Cr), o tipo e a frequência Cr, a duração da deflexão inicial, da maior deflexão e dos dois ciclos de deflexão dos Cr. Todos estes dados foram analisados por fase do ciclo respiratório (i.e., inspiração e expiração). Resultados: Quarenta e nove crianças foram incluídas neste estudo: 25 saudáveis (G1) e 24 com IRTI (G2). A Fmax inspiratória (G1: M 116,1 Hz IQR [107,2-132,4] vs G2: M 118.9Hz IQR [113,2-128,7], p = 0,244) e expiratória (G1: M 107.3Hz IQR [102,9-116,9] vs G2: M 112.6Hz IQR [106,6-122,6], p = 0,083) foi superior nas crianças com IRTI relativamente às crianças saudáveis. A Wh% foi significativamente superior nas crianças com IRTI, relativamente às crianças saudáveis na inspiração (G1: M 0 IQR [0-0,1] vs G2: M 0,2 IQR [0-5,2] p = 0,032) e na expiração (G1: M 0 IQR [0-1,9] vs G2: M 1,5 IQR [0,2-6,7] p = 0,015). Conclusão: Os sons respiratórios computorizados de crianças saudáveis e com IRTI apresentam diferenças. Os principais resultados indicam que os sons respiratórios normais apresentam uma Fmax maior em crianças com IRTI do que em saudáveis e que Wh% é a característica que mais difere entre os dois grupos

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying advanced peripheral interfacing technologies. Here, we present a microscale implantable device – the nanoclip – for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high-resolution recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation can achieve a high degree of functionally-relevant modulatory specificity within the small scale of the device. These results highlight the potential of new microscale design and fabrication techniques for the realization of viable implantable devices for long-term peripheral interfacing.https://www.biorxiv.org/node/801468.fullFirst author draf

ANIMA: Association Network Integration for Multiscale Analysis

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of datapoints on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we show, using a network-based data integration method using clinical phenotype and microarray data as inputs, that we can reconstruct multiple features (or endophenotypes) of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behavior in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets

Modularization for the Cell Ontology

One of the premises of the OBO Foundry is that development of an orthogonal set of ontologies will increase domain expert contributions and logical interoperability, and decrease maintenance workload. For these reasons, the Cell Ontology (CL) is being re-engineered. This process requires the extraction of sub-modules from existing OBO ontologies, which presents a number of practical engineering challenges. These extracted modules may be intended to cover a narrow or a broad set of species. In addition, applications and resources that make use of the Cell Ontology have particular modularization requirements, such as the ability to extract custom subsets or unions of the Cell Ontology with other OBO ontologies. These extracted modules may be intended to cover a narrow or a broad set of species, which presents unique complications.&#xd;&#xa;&#xd;&#xa;We discuss some of these requirements, and present our progress towards a customizable simple-to-use modularization tool that leverages existing OWL-based tools and opens up their use for the CL and other ontologies

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain