AIMC Topic: Auscultation

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Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.

IEEE reviews in biomedical engineering
Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The us...

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.

Scientific reports
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first a...

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

IEEE journal of biomedical and health informatics
This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature ext...

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach.

Journal of biological physics
The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet ...

Hemopneumothorax detection through the process of artificial evolution - a feasibility study.

Military Medical Research
BACKGROUND: Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techn...

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

BMC pulmonary medicine
BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretati...

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes.

Respiratory research
BACKGROUND: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilitie...

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning.

Sensors (Basel, Switzerland)
Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, minute changes over time cannot be recorded based only on subjective observations. In this study, we s...

Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.

Scientific reports
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervi...

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

European journal of pediatrics
Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethosc...