Visualizing and Quantifying Thoraco-Abdominal Asynchrony in Children From Motion Point Clouds: A Pilot Study

Abstract

In a situation of respiratory failure (RF), patients show signs of increased work of breathing leading to the involvement of accessory respiratory muscles and a desynchronization between rib cage and abdomen known as thoraco-abdominal asynchrony (TAA). Proper assessment of these signs requires sufficiently skilled and trained medical staff. However, human assessment is subjective and is practically impossible to audit. A new non-contact method is proposed for TAA visualization and quantification, in children with RF. The surface variations are analyzed by calculating the 3-dimensional motion of the thorax and abdomen regions during the breathing process. A high-fidelity mannequin was used to simulate thoraco-abdominal asynchrony. The proposed system uses depth information recorded by an RGB-D (Red Green Blue-Depth) camera. Furthermore, surface displacement was calculated in four simulated modes from the normal to the severe TAA mode. Respiratory rates were also calculated based on the analysis of the surface movements. The proposed method was compared to a highly precise laser-ranging system with 1 mm resolution. The resulting root mean square deviation (RMSD) showed an error of 1.78 ml in normal mode, 2.83 mm in mild mode, 2.23 mm in severe mode and 2.34 mm in irregular mode. The results showed a high correlation between the two methods in estimating the retraction distance and respiratory rate (ρ >0.985).

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

“…Indeed, the thorax shape is does not reflect the real inspired/expired volume. While new approaches have been suggested for chest wall spatial motion quantification in critical ill children [ 160 ], there is still a lack of methods and techniques to better assess the physiological parameters in patients with acute respiratory failure.…”

“…Many contactless optical systems, based on RGB-D cameras have been previously described by our group in [ 32 , 46 , 91 , 160 ]. The experiments were performed for simulated breathing scenarios and for a mechanically ventilated 4-month and 20-day old female, weighing 6.6 kg [ 91 ].…”

“…In an era where both the computation speed and deep neural networks are expanding in a tremendously fast pace, high dimensional unveiled patterns are expected to be leveraged in the extraction of human respiration [ 242 ], detecting diseases [ 243 , 244 ], classify apnea events [ 245 ], and score illness gravity [ 160 , 246 ]. This direction has recently started gaining attention by researchers [ 242 , 243 , 244 , 245 , 246 ].…”

“…Such devices are unlocking the next level computer vision applications. In combination with their high accuracy and sufficiently high spatiotemporal resolutions [ 240 , 248 ], 3D cameras have greatly simplified the task of human breath detection, which have given rise to new possibilities for respiratory parameters estimation [ 6 , 48 , 91 , 122 , 160 ].…”

“…Even if some of these barriers were overcome, it is very important to develop and document more synthetic databases. Simulated data are possible and easy to acquire, and results based on them are important to report [ 46 , 160 ].…”