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Related Concept Videos

Physical Assessment of the Respiratory Tract II: Inspection01:27

Physical Assessment of the Respiratory Tract II: Inspection

Physical assessment of the respiratory tract through inspection is a crucial step in understanding the patient's respiratory health. It provides insights into the functioning of the respiratory system, the musculoskeletal structure, and even the patient's nutritional status. This comprehensive approach involves observing several vital aspects: chest configuration, breathing patterns, respiratory rates, skin color, and use of accessory muscles.
Chest Configuration
The chest configuration can...
Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

Assessment of Airway, Skin Color, and Use of Accessory Muscles

A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
Assessment of Respiration01:23

Assessment of Respiration

The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like asthma or COPD,...

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Related Experiment Videos

Enhancing breath-based diagnostics through eXplainable Artificial Intelligence.

Andrea Lo Sasso1,2,3, Nicola Amoroso2,4, Domenico Diacono2

  • 1Dipartimento Interuniversitario di Fisica "M. Merlin", Università degli Studi di Bari Aldo Moro, Bari, Italy.

Plos One
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Breath analysis shows promise for early disease detection, including lung cancer. Machine learning models identified key volatile organic compounds, advancing non-invasive diagnostics.

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Medical Diagnostics

Background:

  • Breath analysis offers a non-invasive method to assess metabolic states via volatile organic compounds (VOCs).
  • Early disease detection, particularly for lung cancer, remains a critical challenge in healthcare.

Purpose of the Study:

  • To investigate the efficacy of breath analysis for early detection of lung cancer, respiratory, and gastrointestinal diseases.
  • To implement and evaluate an artificial intelligence (AI) methodology for disease prediction using breath VOCs.
  • To identify key VOCs and enhance model interpretability using eXplainable AI (XAI).

Main Methods:

  • Utilized open-access datasets comprising breath analysis data.
  • Applied AI models to predict diagnostic labels, incorporating strategies to handle class imbalance.
  • Employed XAI techniques to analyze the influence of VOC abundances on model predictions.

Main Results:

  • Successfully developed and evaluated AI models for disease prediction from breath samples.
  • Identified specific VOCs as relevant biomarkers for different diseases across datasets.
  • Demonstrated the utility of XAI in pinpointing influential VOCs and improving model transparency.

Conclusions:

  • Breath analysis integrated with AI presents a robust framework for non-invasive disease diagnostics.
  • The methodology highlights the potential for advancing clinical decision-making through personalized breath-based biomarkers.
  • Further research is needed to address challenges in standardization, sensitivity, and sampling variability for clinical translation.