Lung Point of Care Ultrasound (POCUS) in Cardiorespiratory Physiotherapy and Respiratory Therapy Practices: Current Status and Future Directions.

Lung Point of Care Ultrasound (POCUS) strongly influences physiotherapy and respiratory therapy clinical decision-making in the intensive care unit (ICU). The uptake of Lung POCUS training by physiotherapists and respiratory therapists is low in some countries, often due to many barriers to its implementation. The safe and appropriate integration of Lung POCUS into physiotherapy and respiratory therapy clinical practice may be achieved by various means.

Use of Laryngeal Ultrasound to Observe Laryngeal Movements During Noninvasive Ventilation in Healthy Volunteers.

Background: Transnasal fiberoptic laryngoscopy (TFL) has revealed that laryngeal obstruction can hamper assisted ventilation. TFL may be considered invasive, and laryngeal ultrasound (US) could be a noninvasive alternative. The objective of this study was to investigate the feasibility of using laryngeal US to study laryngeal movements in healthy adult volunteers undergoing noninvasive ventilation (NIV) and to compare the observations with those of simultaneous TFL.

Appropriateness of respiratory physiotherapy positioning for acute lobar collapse.

Background: Positioning is an important physiotherapy treatment modality for the management of aeration loss associated with acute lobar atelectasis (ALA). Physiotherapists typically rely on lung auscultation and interpretation of chest x-ray (CXR) to inform treatment selection. These tools lack diagnostic accuracy, which could limit the ability of a physiotherapist to locate ALA and select an appropriate treatment position.

MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography.

Introduction/aims: Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.

Methods: Quadriceps complex (QC [rectus femoris and vastus intermedius]) and tibialis anterior (TA) muscle ultrasound images of healthy, intensive care unit, and/or lung cancer subjects were captured with portable devices.