Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI
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ABSTRACT:
SUBMITTER: Chung Y
PROVIDER: S-EPMC8586978 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Bronchitis and pneumonia are the common respiratory diseases, of which pneumonia is the leading cause of mortality in pediatric patients worldwide and impose intense pressure on health care systems. This study aims to classify bronchitis and pneumonia in children by analyzing cough sounds. We propose a Classification Framework based on Cough Sounds (CFCS) to identify bronchitis and pneumonia in children. Our dataset includes cough sounds from 173 outpatients at the West China Second University Hospital, Sichuan University, Chengdu, China. We adopt aggregation operation to obtain patients' disease features because some cough chunks carry the disease information while others do not. In the stage of classification in our framework, we adopt Support Vector Machine (SVM) to classify the diseases due to the small scale of our dataset. Furthermore, we apply data augmentation to our dataset to enlarge the number of samples and then adopt Long Short-Term Memory Network (LSTM) to classify. After 45 random tests on RAW dataset, SVM achieves the best classification accuracy of 86.04% and standard deviation of 4.7%. The precision of bronchitis and pneumonia is 93.75% and 87.5%, and their recall is 88.24% and 93.33%. The AUC of SVM and LSTM classification models on the dataset with pitch-shifting data augmentation reach 0.92 and 0.93, respectively. Extensive experimental results show that CFCS can effectively classify children into bronchitis and pneumonia.
| S-EPMC9612535 | biostudies-literature
Project description:BackgroundLittle is known of the language healthcare professionals use to describe cough sounds. We aimed to examine how they describe cough sounds and to assess whether these descriptions suggested they appreciate the basic sound qualities (as assessed by acoustic analysis) and the underlying diagnosis of the patient coughing.Methods53 health professionals from two large respiratory tertiary referral centres were recruited; 22 doctors and 31 staff from professions allied to medicine. Participants listened to 9 sequences of spontaneous cough sounds from common respiratory diseases. For each cough they selected patient gender, the most appropriate descriptors and a diagnosis. Cluster analysis was performed to assess which cough sounds attracted similar descriptions.ResultsGender was correctly identified in 93% of cases. The presence or absence of mucus was correct in 76.1% and wheeze in 39.3% of cases. However, identifying clinical diagnosis from cough was poor at 34.0%. Cluster analysis showed coughs with the same acoustics properties rather than the same diagnoses attracted the same descriptions.ConclusionThese results suggest that healthcare professionals can recognise some of the qualities of cough sounds but are poor at making diagnoses from them. It remains to be seen whether in the future cough sound acoustics will provide useful clinical information and whether their study will lead to the development of useful new outcome measures in cough monitoring.
| S-EPMC1413549 | biostudies-literature
Project description:The diagnosis of acute respiratory diseases in children can be challenging, and no single objective diagnostic test exists for common pediatric respiratory diseases. Previous research has demonstrated that ResAppDx, a cough sound and symptom-based analysis algorithm, can identify common respiratory diseases at the point of care. We present the study protocol for SMARTCOUGH-C 2, a prospective diagnostic accuracy trial of a cough and symptom-based algorithm in a cohort of children presenting with acute respiratory diseases. The objective of the study is to assess the performance characteristics of the ResAppDx algorithm in the diagnosis of common pediatric acute respiratory diseases.
| S-EPMC7990113 | biostudies-literature
Project description:Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
| S-EPMC8950004 | biostudies-literature
Project description:BackgroundHospital-acquired pneumonia (HAP) is defined as radiologically confirmed pneumonia occurring ?48h after hospitalization, in non-intubated patients. Empirical treatment regimens use broad-spectrum antimicrobials.AimTo evaluate the accuracy of the diagnosis of HAP and to describe the demographic and microbiological features of patients with HAP.MethodsMedical and surgical inpatients receiving intravenous antimicrobials for a clinical diagnosis of HAP at a UK tertiary care hospital between April 2013 and 2014 were identified. Demographic and clinical details were recorded.FindingsA total of 166 adult patients with a clinical diagnosis of HAP were identified. Broad-spectrum antimicrobials were prescribed, primarily piperacillin-tazobactam (57.2%) and co-amoxiclav (12.5%). Sputum from 24.7% of patients was obtained for culture. Sixty-five percent of patients had radiological evidence of new/progressive infiltrate at the time of HAP treatment, therefore meeting HAP diagnostic criteria (2005 American Thoracic Society/Infectious Diseases Society of America guidelines). Radiologically confirmed HAP was associated with higher levels of inflammatory markers and sputum culture positivity. Previous surgery and/or endotracheal intubation were associated with radiologically confirmed HAP. A bacterial pathogen was identified from 17/35 sputum samples from radiologically confirmed HAP patients. These were Gram-negative bacilli (N = 11) or Staphylococcus aureus (N = 6). Gram-negative bacteria tended to be resistant to co-amoxiclav, but susceptible to ciprofloxacin, piperacillin-tazobactam and meropenem. Five of the six S. aureus isolates were meticillin susceptible and all were susceptible to doxycycline.ConclusionIn ward-level hospital practice 'HAP' is an over-used diagnosis that may be inaccurate in 35% of cases when objective radiological criteria are applied. Radiologically confirmed HAP represents a distinct clinical and microbiological phenotype. Potential risk factors were identified that could represent targets for preventive interventions.
| S-EPMC7172606 | biostudies-literature
Project description:Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.
| S-EPMC7196900 | biostudies-literature
Project description:Previous work has demonstrated that adults are capable of learning patterned relationships among adjacent syllables or tones in continuous sequences but not among nonadjacent syllables. However, adults are capable of learning patterned relationships among nonadjacent elements (segments or tones) if those elements are perceptually similar. The present study significantly broadens the scope of this previous work by demonstrating that adults are capable of encoding the same types of structure among unfamiliar nonlinguistic and nonmusical elements but only after much more extensive exposure. We presented participants with continuous streams of nonlinguistic noises and tested their ability to recognize patterned relationships. Participants learned the patterns among noises within adjacent groups but not within nonadjacent groups unless a perceptual similarity cue was added. This result provides evidence both that statistical learning mechanisms empower adults to extract structure from nonlinguistic and nonmusical elements and that perceptual similarity eases constraints on nonadjacent pattern learning. Supplemental materials for this article can be downloaded from pbr.psychonomic-journals.org/content/supplemental.
| S-EPMC2799498 | biostudies-literature
Project description:This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.A binary classifier was employed to identify pneumonia from a dataset of multiple types of clinical notes created for 426 patients during their stay in the intensive care unit. For this purpose, three types of features were considered: (1) word n-grams, (2) Unified Medical Language System (UMLS) concepts, and (3) assertion values associated with pneumonia expressions. System performance was greatly increased by a feature selection approach which uses statistical significance testing to rank features based on their association with the two categories of pneumonia identification.Besides testing our system on the entire cohort of 426 patients (unrestricted dataset), we also used a smaller subset of 236 patients (restricted dataset). The performance of the system was compared with the results of a baseline previously proposed for these two datasets. The best results achieved by the system (85.71 and 81.67 F1-measure) are significantly better than the baseline results (50.70 and 49.10 F1-measure) on the restricted and unrestricted datasets, respectively.Using a statistical feature selection approach that allows the feature extractor to consider only the most informative features from the feature space significantly improves the performance over a baseline that uses all the features from the same feature space. Extracting the assertion value for pneumonia expressions further improves the system performance.
| S-EPMC3422830 | biostudies-other
Project description:Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently, AI has also found use in enhancing the efficiency of molecular simulations, wherein AI derived slow modes are used to accelerate the simulation in targeted ways. However, while typical fields where AI is used are characterized by a plethora of data, molecular simulations, per construction, suffer from limited sampling and thus limited data. As such, the use of AI in molecular simulations can suffer from a dangerous situation where the AI-optimization could get stuck in spurious regimes, leading to incorrect characterization of the reaction coordinate (RC) for the problem at hand. When such an incorrect RC is then used to perform additional simulations, one could start to deviate progressively from the ground truth. To deal with this problem of spurious AI-solutions, here, we report a novel and automated algorithm using ideas from statistical mechanics. It is based on the notion that a more reliable AI-solution will be one that maximizes the timescale separation between slow and fast processes. To learn this timescale separation even from limited data, we use a maximum caliber-based framework. We show the applicability of this automatic protocol for three classic benchmark problems, namely, the conformational dynamics of a model peptide, ligand-unbinding from a protein, and folding/unfolding energy landscape of the C-terminal domain of protein G. We believe that our work will lead to increased and robust use of trustworthy AI in molecular simulations of complex systems.
| S-EPMC7863682 | biostudies-literature
Project description:Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists' expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.
| S-EPMC7924641 | biostudies-literature