WebLog Journal of Pulmonology and Respiratory Medicine

We report a 4‑year‑old girl with recurrent respiratory tract infections and multiple episodes of pneumonia. Despite repeated hospital admissions, the underlying cause remained elusive until severe fecal retention was identified as a contributing factor. Further evaluation revealed previously unrecognized hypoganglionosis. Imaging demonstrated massive fecal loading with diaphragmatic elevation. Rectal biopsies confirmed reduced ganglion cell density consistent with hypoganglionosis. The…

Background: Patient–ventilator asynchrony (PVA) is a common complication in mechanically ventilated patients, leading to increased morbidity, prolonged ventilation, and respiratory muscle fatigue. Early detection is critical. Artificial intelligence (AI) systems, including convolutional neural networks (CNNs) and machine learning algorithms, have been applied to automate detection.

Objective: To systematically review current evidence on AI-based…

IntroductionVentilator asynchrony is a frequent yet under-recognized problem in mechanically ventilated patients. Failure to detect asynchrony early may lead to prolonged ventilation, respiratory muscle weakness, and delayed rehabilitation. Recent advances in artificial intelligence have introduced smart camera and waveform-based systems capable of detecting subtle asynchrony patterns in real time Artificial Intelligence and Smart Monitoring Systems.