Aerosol spirometer and method of using same

A machine learning-based system for analyzing lung function, the system comprising:

• an aerosol spirometer;
• and
• a computer with at least one processor and a non-transitory computer readable medium storing machine-readable instructions that cause the at least one processor to:
• receive a plurality of data points from the aerosol spirometer, the plurality of data points corresponding to aerosol particles that have been introduced by the aerosol spirometer into the lung of an individual for traversal therethrough;
• determine a time-based indication of at least one of aerosol deposition and aerosol penetration within the lung, wherein the time-based indication corrects for losses within an exhalation portion of a normal breathing cycle through a data-informed exhalation aerosol concentration profile that is based on a comparison of a distribution of the plurality of data points to an idealized exhalation aerosol concentration profile such that the idealized exhalation aerosol concentration profile becomes populated with at least a portion of additional data points such that an updated exhalation aerosol concentration profile is created;
• predict a lung condition based on a correlation between a plurality of symptoms indicative of a lung function of the lung to the updated exhalation aerosol concentration profile;
• and
• transmit the predicted lung condition to a user.

A method of performing an aerosol dispersion-based respiratory system test on a patient, the method comprising:

• configuring an aerosol spirometer to introduce an aerosol into the respiratory system;
• upon receipt within the aerosol spirometer of an inhalation portion of a normal breathing cycle from the patient, providing an aerosol pulse such that the aerosol pulse mixes with ambient air from the inhalation portion;
• detecting, with the aerosol spirometer, a plurality of aerosol particles contained within the inhalation portion;
• detecting, with the aerosol spirometer, a plurality of aerosol particles contained within the exhalation portion;
• calculating, using a machine learning model, aerosol dispersion transit times of the detected plurality of aerosol particles, wherein data points representative of the aerosol dispersion transit times corrects for losses within the exhalation portion of a normal breathing cycle through a data-informed exhalation aerosol concentration profile that is based on a comparison of a distribution of the plurality of data points to an idealized exhalation aerosol concentration profile such that the idealized exhalation aerosol concentration profile becomes populated with at least a portion of additional data points such that an updated exhalation aerosol concentration profile is created;
• determining a concentration distribution based on the updated exhalation aerosol concentration profile;
• transmitting a signal that corresponds to the concentration distribution to a processor-based controller;
• and
• determining whether the respiratory system is suffering from an adverse lung condition based on a correlation between a plurality of symptoms indicative of a lung function to the concentration distribution.