A classification framework for identifying bronchitis and pneumonia in children based on a small-scale cough sounds dataset

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

BACKGROUND

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential.

OBJECTIVE

This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats.

METHODS

A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence).

RESULTS

The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation.

CONCLUSIONS

To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Automated Cough Sound Analysis for Detecting Childhood Pneumonia

Pneumonia is one of the leading causes of death in children. Prompt diagnosis and treatment can help prevent these deaths, particularly in resource poor regions where deaths due to pneumonia are highest. Clinical symptom-based screening of childhood pneumonia yields excessive false positives, highlighting the necessity for additional rapid diagnostic tests. Cough is a prevalent symptom of acute respiratory illnesses and the sound of a cough can indicate the underlying pathological changes resulting from respiratory infections. In this study, we propose a fully automated approach to evaluate cough sounds to distinguish pneumonia from other acute respiratory diseases in children. The proposed method involves cough sound denoising, cough sound segmentation, and cough sound classification. The denoising algorithm utilizes multi-conditional spectral mapping with a multilayer perceptron network while the segmentation algorithm detects cough sounds directly from the denoised audio waveform. From the segmented cough signal, we extract various handcrafted features and feature embeddings from a pretrained deep learning network. A multilayer perceptron is trained on the combined feature set for detecting pneumonia. The method we propose is evaluated using a dataset comprising cough sounds from 173 children diagnosed with either pneumonia or other acute respiratory diseases. On average, the denoising algorithm improved the signal-to-noise ratio by 44%. Furthermore, a sensitivity and specificity of 91% and 86%, respectively, is achieved in cough segmentation and 82% and 71%, respectively, in detecting childhood pneumonia using cough sounds alone. This demonstrates its potential as a rapid diagnostic tool, such as using smartphone technology.

Detecting Childhood Pneumonia Using Handcrafted and Deep Learning Cough Sound Features and Multilayer Perceptron. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083528 DOI: 10.1109/embc40787.2023.10340477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Pneumonia is one of the leading causes of morbidity and mortality in children. This is especially true in resource poor regions lacking diagnostic facilities, bringing about the need for rapid diagnostic tests for pneumonia. Cough is a common symptom of acute respiratory diseases, including pneumonia, and the sound of cough can be indicative of the pathological variations caused by respiratory infections. As such, in this paper we study objective cough sound evaluation for differentiating between pneumonia and other acute respiratory diseases. We use a dataset of 491 cough sounds from 173 children diagnosed either as having pneumonia or other acute respiratory diseases. We extract features which describe the temporal, spectral, and cepstral characteristics of the cough sound. These features are combined with feature embeddings from a pretrained deep learning network and used to train a multilayer perceptron for classification. The proposed method achieves a sensitivity and specificity of 84% and 73% respectively in differentiating between pneumonia and other acute respiratory diseases using cough sounds alone.

The use of fluorescence laparoscopy in the resection of splenic tissue replantation in the right lobe of the liver: A case report and literature review

BACKGROUND

Ectopic replantation and regeneration of splenic tissue fragments following splenic trauma or splenectomy is known as replantation of splenic tissue. It typically takes place in the abdominal cavity, however, splenic tissue replantation in the liver is extremely rare and difficult to diagnose. It is often misdiagnosed as a liver tumor and removed.

CASE PRESENTATION

We present the case of a patient with a history of traumatic splenectomy 15 years prior to the replantation of splenic tissue in the liver. A 4 cm mass in the liver was found during the most recent physical examination, and a computed tomography scan indicated the possibility of a malignant tumor. The tumor was then removed using fluorescence laparoscopy.

CONCLUSION

There is a possibility of intrahepatic replantation of splenic tissue in patients who have had a splenectomy in the past, have recently discovered an intrahepatic space-occupying lesion, and do not have any high-risk factors for liver cancer. Unnecessary surgery can be avoided if 99mTc-labeled red blood cells imaging using mass puncture or radionuclide examination provides a clear preoperative diagnosis. Globally, there are no reports of the use of fluorescence laparoscopy in resecting replanted splenic tissue in the liver. Specifically, in the current case, there was no indocyanine green uptake in the mass, and only a small amount was found in the normally functioning liver tissue surrounding the tumor.