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Common Respiratory Disorders01:31

Common Respiratory Disorders

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Respiratory disorders, a prevalent health concern globally, are generally divided into two primary categories: upper and lower respiratory tract disorders. The categorization is based on the area of the respiratory system they affect.
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The respiratory system is responsible for the intake of oxygen and the expulsion of carbon dioxide from the body. Respiratory volumes describe the volume of air in the lungs at different phases of the respiratory cycle. Tidal volume is the air breathed in and out during normal, quiet breathing. Inspiratory reserve volume is the air that can be forcefully inspired beyond the tidal volume. In contrast, expiratory reserve volume refers to the air that can be expelled from the lungs after a normal...
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Assessing the respiratory rate and rhythm for a complete minute is crucial for evaluating the breathing pattern. Even a minor increase in the patient's average respiratory rate, by as little as three to five breaths per minute, is an early and vital indicator of respiratory distress. Patients with a respiratory rate exceeding twenty-four breaths per minute require close monitoring to determine the physiological alterations. This careful observation is essential for prompt recognition and...
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Respiratory assessment is a cornerstone of nursing assessments, crucial for the early detection of patient deterioration. This evaluation transcends routine procedures, representing a critical skill nurses must master to ensure optimal patient care.
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Multimodal Knowledge-Infused VLM for Respiratory Disease Prediction and Clinical Report Generation.

Mukhlis Raza, Saied Salem, Hyunwook Kwon

    IEEE Journal of Biomedical and Health Informatics
    |December 11, 2025
    PubMed
    Summary

    This study introduces a new computer-aided diagnosis (CAD) system for respiratory diseases using large language models (LLM) and retrieval-augmented generation (RAG). The system accurately diagnoses conditions and generates structured radiology reports quickly.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Medical Imaging Analysis
    • Natural Language Processing for Healthcare

    Background:

    • Accurate computer-aided diagnosis (CAD) for respiratory diseases is challenging due to complex, multimodal medical data.
    • Existing CAD systems often lack sophisticated retrieval strategies, limiting diagnostic reliability and interpretability.
    • Multimodal data integration and advanced natural language processing are crucial for improving CAD systems.

    Purpose of the Study:

    • To develop a novel end-to-end CAD framework leveraging large language models (LLM) and retrieval-augmented generation (RAG).
    • To enhance multimodal retrieval and structured radiology report generation for improved diagnostic accuracy and interpretability.
    • To introduce and evaluate three novel RAG approaches (CAF, CLAF, DGF) for medical report generation.

    Main Methods:

    • Utilized a dedicated vision encoder for view type identification (AP, LV) and multi-label disease classification (14 categories).
    • Employed Grad-CAM for visual interpretability and an advanced text encoder for processing unstructured medical reports.
    • Implemented three novel RAG techniques (Caption-Augmented Fusion, CLIP-Aligned Fusion, Domain-Grounded Fusion) for enhanced retrieval and report generation.

    Main Results:

    • The proposed CAD system accurately diagnoses respiratory conditions and generates structured radiology reports.
    • The system achieved a rapid processing time of 21.39 seconds for diagnosis and report generation.
    • Performance was rigorously evaluated using lexical, semantic, and clinical metrics, with validation by expert radiologists.

    Conclusions:

    • The novel CAD framework demonstrates significant potential for accurate and interpretable diagnosis of respiratory diseases.
    • The integration of LLM with advanced RAG techniques offers a promising approach for multimodal medical data analysis.
    • The system's speed and accuracy suggest strong suitability for real-world clinical applications and modern medical practice.