Automated Detection of Acute Respiratory Distress Using Temporal Visual Information

The Pediatric Intensive Care Unit (PICU) receives critically ill patients with shortness of breath and poor body oxygenation. Various respiratory parameters, such as respiratory rate, oxygen saturation level, and heart rate, are continuously monitored to timely adapt their management. With the advancement in technology, measurements of most parameters are carried out by medical instruments. However, some crucial parameters are still measured via visual examination, particularly the assessment of chest deformation, which is vital in assessing acute respiratory distress (ARD) conditions. However, visual examination is subjective and intermittent, prone to human error, and challenging to monitor patients round the clock. This subjectivity becomes problematic, especially in areas with a shortage of specialists, such as remote locations, developing countries, or during pandemics. In this paper, we propose an automated acute respiratory distress condition detection system, to address challenges associated with visual examination. The proposed approach utilizes a high-definition camera to capture patient temporal visual information and employs advanced deep-learning models to detect ARD condition. In order to test the feasibility, we collected video data of 153 patients, including both with and without ARD in the PICU. As the deep learning models require substantial amounts of data, and collecting data in the medical domain, particularly in the PICU, poses challenges. To overcome data limited problem, we utilized the problem-specific information, opted transfer learning and data augmentation techniques. Additionally, we compute baseline results of various video analysis algorithms for ARD detection task. Experimental results illustrate that the deep learning base video analysis algorithms have the potential to automate the visual examination process for the ARD detection task, by achieving an accuracy of 0.82, precision of 0.80, recall of 0.89, and $F_{1}$ score of 0.84.