Abstract: A mechanical ventilation device includes at least one electronic controller configured to receive imaging data related to a dimension of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with an associated mechanical ventilator; calculate a pressure value (Pl, DPl) of a chest of the patient based on at least the imaging data; and when the calculated pressure value (Pl, DPl) does not satisfy an acceptance criterion, at least one of output an alert indicative of the calculated pressure value (Pl, DPl) failing to satisfy the acceptance criterion; and output a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient.

Abstract: A diaphragm imaging device includes at least one electronic processor programmed to perform a diaphragm imaging method including receiving ultrasound imaging data of a diaphragm of a patient, the ultrasound imaging data being acquired by an associated ultrasound imaging probe with the probe at a plurality of different observable probe angles (?obs); for each observable probe angle, determining a corresponding apparent thickness (dI) of the diaphragm of the patient from the received ultrasound data acquired at that observable probe angle; and estimating a thickness (dD) of the diaphragm of the patient based at least on the apparent thicknesses (dI).

Abstract: A diaphragm measurement device includes a non-transitory storage medium storing a patient-specific registration model for referencing ultrasound imaging data to a reference frame. At least one electronic processor is programmed to perform a diaphragm measurement method including receiving ultrasound imaging data of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator; calculating a diaphragm thickness metric based on the received ultrasound imaging data of the diaphragm of the patient referenced to the reference frame using the patient-specific registration model; and displaying, on a display device, a representation of the calculated diaphragm thickness metric.

Abstract: A mechanical ventilation assistance device includes at least one electronic controller configured to receive positional data of a patient; determine a position of an imaging device configured to obtain imaging data of the patient based on the received positional data; and display, on a display device, a representation of the determined position of the imaging device.

Abstract: A diaphragm measurement device (1) includes at least one electronic processor (13) programmed to perform a diaphragm measurement method (100) including receiving ultrasound imaging data of a dimension of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator (2); receiving respiratory data of the patient during inspiration and expiration while the patient undergoes the mechanical ventilation therapy; calculating a diaphragm thickness metric based on the received ultrasound imaging data of the diaphragm of the patient and the received respiratory data; and displaying, on a display device (14), a representation (30) of the calculated diaphragm thickness metric.

Abstract: A diaphragm measurement system (1) includes at least one electronic processor (13) programmed to perform a diaphragm measurement method (100) including receiving ultrasound imaging data (24) of a diaphragm of a patient over a time period encompassing multiple breaths; receiving respiration data of the patient over the time period; calculating a diaphragm thickness metric based on the received ultrasound imaging data of the diaphragm and the received respiration data; and displaying, on a display device (14), a representation (30) of the calculated diaphragm thickness metric.

Abstract: The present disclosure pertains to a system and method for monitoring lung disease in a patient that is based on information that is derivable from home therapy devices including a ventilation therapy device and an oxygen saturation monitor such as a pulse oximeter. The measured parameters are used to model shunt fraction and a volume of dead space. Based on changes in gas exchange and results of a nitrogen washout test, a change in gas exchange impairment in the patient is observed and is then used to determine progression of a disease, identify a cause of the impairment, and/or to guide treatment of the patient.

Abstract: A non-transitory storage medium stores instructions readable and executable by at least one electronic processor to receive clinical data for a current patient (P); search a database of CT scans and/or patient-specific mechanical ventilation models for other patients using the clinical data for the current patient as a search criterion to identify a similar patient in the database and similar patient data (S) comprising a CT scan and/or a patient-specific mechanical ventilation model for the similar patient; determine a patient-specific mechanical ventilation model for the current patient based on the CT scan and/or patient-specific mechanical ventilation model for the similar patient; generate ventilator configuration data for mechanically ventilating the current patient based on the determined patient-specific mechanical ventilation model for the current patient.

Abstract: A medical device for treating an associated patient includes an electronic processing device configured to receive ventilation waveform data during mechanical ventilation of the associated patient and to perform a patient-ventilator asynchrony monitoring method including detecting initial patient-ventilator asynchrony events during a training period of the mechanical ventilation by analysis of measurements of the associated patient acquired during the training period; training a machine learning (ML) component to analyze ventilation waveform data to detect patient-ventilator asynchrony events using the ventilation waveform data received during the training period with labels indicating the initial patient-ventilator asynchrony events; applying the patient-specific ML component to the ventilation waveform data received after the training period to detect patient-ventilator asynchrony events occurring after the training period; and a display device configured to display an indication of patient-ventilator async

Abstract: A respiration monitoring device comprises an electronic controller configured to: receive an audio signal that is acoustically coupled with an airway of a patient receiving mechanical ventilation therapy from a mechanical ventilator; map the audio signal to one or more lung disease or injury condition categories; and at least one of: display the mapped one or more lung disease or injury condition categories on a display device; and determine a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient based at least on the mapped lung disease or injury condition categories and displaying the recommended adjustment on the display device.

Abstract: A mechanical ventilation device comprising at least one electronic controller is configured to receive images of lungs of a patient undergoing mechanical ventilation therapy with a mechanical ventilator, the images being acquired over time and having timestamps; process the images to generate timeline images at corresponding discrete time points; and display a timeline of the timeline images on a display device.

Abstract: A mechanical ventilation device comprising at least one electronic controller configured to receive imaging data and transpulmonary pressure data associated with a lung of a patient; perform deformable image registration of the inhalation image and the exhalation image to produce a relative compliance or elasticity map of the lungs; convert the relative compliance or elasticity map of the lungs to a quantitative compliance or elasticity map of the lungs based on the inhale transpulmonary pressure and the exhale transpulmonary pressure; and display the information relating to or derived from the quantitative compliance or elasticity map on a display device.

Abstract: A respiratory monitoring device includes an electronic controller configured to: analyze an audio signal triggered during inspiratory and expiratory phases of a patient receiving mechanical ventilation therapy from a mechanical ventilator, the audio signal being acoustically coupled into the airway of the patient, to determine resonant frequencies of the airway; determine a shift in the resonant frequencies between the inspiratory and expiratory phases to determine a presence of pendelluft inside of a lung of the patient; and output an indication of the presence of pendelluft.

Abstract: A mechanical ventilation device comprises at least one electronic controller configured to: receive ultrasound data related to a thickness of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator; calculate a diaphragm thickness metric based on at least the ultrasound data; and when the calculated diaphragm thickness metric does not satisfy an acceptance criterion, at least one of: output an alert indicative of the calculated diaphragm thickness metric failing to satisfy the acceptance criterion; and output a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient.

Abstract: An intubation assistance device includes an electronic controller configured to: generate a patient respiratory tract geometry model of at least a portion of a human respiratory tract by inputting one or more patient variables into a statistical shape model (SSM) of at least a portion of the human respiratory tract; select a recommended endotracheal tube (ETT) size by modeling at least one ETT model inserted into the patient respiratory tract geometry model to form a virtual fit model and estimating at least one fit parameter based on the virtual fit model; and display the recommended ETT size on a display device.

Abstract: An intubation assistance device includes an electronic controller configured to: identify, from one or more images of a patient, information about the patient including at least a diameter of a trachea and a length of an intubation pathway; determine a recommended ETT size including an ETT diameter and an ETT depth of insertion from the determined diameter of the trachea and the determined length of the intubation pathway; and display the recommended ETT size on a display device.

Abstract: A method for controlling a ventilator to provide variable volume (VV) with average volume assured pressure support (AVAPS), including: producing a VV target volume using a VV distribution function; producing a volume error Verror that is the difference between the VV target volume and a measured volume of the previous breath; scaling the volume error Verror; producing a VV target difference as the difference between VV target volume and the VV target volume of the previous breath; producing a modified volume error by adding the VV target difference to the scaled volume error Verror; producing a delta pressure support ?PS based upon the modified volume error and a dynamic compliance; and producing a current pressure support value based upon the delta pressure support ?PS and the pressure support value of the previous breath.

Abstract: The present disclosure pertains to a system and method for monitoring lung disease in a patient that is based on information that is derivable from home therapy devices including a ventilation therapy device and an oxygen saturation monitor such as a pulse oximeter. The measured parameters are used to model shunt fraction and a volume of dead space. Based on changes in gas exchange and results of a nitrogen washout test, a change in gas exchange impairment in the patient is observed and is then used to determine progression of a disease, identify a cause of the impairment, and/or to guide treatment of the patient.

Abstract: A system and method for determining a patient condition includes receiving sensor information over an observation period, generating one or more metrics based on the sensor information, comparing the one or metrics to reference information to determine deviation information, and generating a risk score indicating a probability that a patient is experiencing an COPD-related event based on the deviation information. The sensor information is received from one or more sensors at predetermined locations of a patient location.