Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium

Abstract

Inhalable environmental toxicants can induce pulmonary malfunction resulting abnormal respiratory conditions. The traditional methods currently available to detect the respiratory condition of animals rely on differential pressure transducers and signal amplifiers. In comparison, current machine vision application requires little hardware. But it is unsuitable for respiratory condition tests of experimental animals reflecting respiratory toxicities of inhalable pollutants. In this study, we establish a new automatic method of machine vision analysis using a model that has mice inhaling aqueous aerosol with different concentrations of CdCl2 (0, 1, 3, 5 mM 2 h/day) for 7 days as simulant occupational exposure of inhalable Cd and analyze respiratory conditions such as respiratory rate, rhythm index, drive index and exchange index. Additionally, the models with different degrees of lung damage in mice are further tested and verified by the concentrations of cadmium accumulated in the lungs and the analyses on pulmonary porosity, fibrosis and inflammation. Machine vision analysis can identify the abnormal respiratory conditions of mice. Respiratory rate and rhythm index increase after exposure to cadmium. In the individuals with mild lung damage, respiratory drive index and exchange index in treatment group are higher than that in the control group, and in individuals with severe lung damage, these indices are similar to that of the control group. These abnormal respiratory conditions related to variable lung damage in mice demonstrate that the respiration is synchronously influenced by inhalable Cd and respiratory compensation according to normal physiological regulation, suggesting the present method is effective.