System and method for collecting fit data related to selected masks

Abstract

To provide a system and method to collect feedback data from a patient wearing an interface such as a mask when using a respiratory pressure therapy device such as a CPAP device.SOLUTION: A system includes a storage device including a facial image of the patient. An interface in communication with a respiratory pressure therapy device collects operational data from when the patient uses the interface. A patient interface 100 collects subjective patient input data from the patient in relation to the patient interface. An analysis module correlates a characteristic of the interface with the facial image data, operational data and subjective patient input data.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This application claims priority and the benefit of Australian Provisional Patent Application No. 2019904285 (filed on November 13, 2019) and U.S. Provisional Patent Application No. 63 / 072,914 (filed on August 31, 2020). Each of these documents is incorporated herein by reference in its entirety.

[0002] This disclosure is mainly related to a design incorporation mechanism for a respiratory disease treatment system, and more particularly, to a system that collects patient data related to the effectiveness of a mask of a pneumatic device for future designers.

Background Art

[0003] There is a range of respiratory diseases. Certain diseases can be characterized by certain incidences (e.g., apnea, hypopnea, and hyperventilation). Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) and is characterized by incidences such as the closure or obstruction of the upper airway during sleep. This is the result of a combination of an abnormally small upper airway and the normal loss of muscle tone in the tongue area, and the normal loss of the soft palate and posterior oropharyngeal wall during sleep. Due to such conditions, the breathing cessation of affected patients typically lasts for 30 to 120 seconds, and sometimes the breathing stops 200 to 300 times a night. As a result, excessive daytime sleepiness occurs, which can cause cardiovascular diseases and brain damage. This syndrome is a common disease, especially common in middle-aged overweight men, but patients have no awareness of the symptoms.

[0004] Other sleep-related diseases include Cheyne-Stokes respiration (CSR), obesity hypoventilation syndrome (OHS), and chronic obstructive pulmonary disease (COPD). COPD encompasses any of a group of lower airway diseases that have certain common characteristics. This includes an increase in resistance to the movement of air, an extension of the expiratory phase of breathing, and a decrease in normal elasticity in the lungs. Examples of COPD are emphysema and chronic bronchitis. The causes of COPD include chronic smoking (the first risk factor), occupational exposure, air pollution, and genetic factors.

[0005] Continuous positive airway pressure (CPAP) therapy is used in the treatment of obstructive sleep apnea (OSA). For example, by pushing the soft palate and tongue forward or backward towards the posterior oropharyngeal wall, the application of continuous positive airway pressure functions as an air splint, thereby preventing upper airway obstruction.

[0006] Non-invasive ventilation (NIV) assists patients with complete breathing and / or maintains adequate oxygen levels throughout the body by providing ventilatory support to the patient through the upper airway to perform some or all of the respiratory function. Ventilation support is provided through a patient interface. NIV is used to treat forms of CSR and respiratory failure such as OHS, COPD, and chest wall disorders. In some forms, it can improve the comfort and effectiveness of these treatments. Invasive ventilation (IV) provides ventilatory support to patients who are no longer able to breathe effectively on their own and may be provided using a tracheostomy tube.

[0007] The treatment system may include a respiratory pressure therapy (RPT) device, air circuit, humidifier, patient interface, and data management. The patient interface may be used to provide the wearer with an interface to the respiratory appliance, for example, by providing airflow to the airway inlet. Airflow may be provided via a mask to the nose and / or mouth, a tube to the mouth, or a tracheostomy tube to the patient's trachea. Depending on the therapy applied, the patient interface may also be used. The device may, for example, form a seal with the patient's facial area, thereby facilitating gas delivery at a pressure of sufficient dispersion along with the ambient pressure for therapeutic execution (e.g., at a positive pressure of approximately 10 cmH2O relative to the ambient pressure). In other forms of therapy, such as oxygen delivery, the patient interface may not include a seal sufficient to facilitate the delivery of gas to the airways at a positive pressure of approximately 10 cmH2O. Since treatment of respiratory illness by such therapy may be voluntary, such patients may choose not to adhere to the therapy if they notice any of the following about the device used to deliver the therapy: discomfort, difficulty of use, high cost, and / or lack of aesthetic appeal.

[0008] There are several challenges in designing patient interfaces. The face has a complex three-dimensional shape. The size and shape of the nose vary greatly from person to person. Because the head contains bone, cartilage, and soft tissue, different areas of the face respond differently to mechanical forces. That is, the jaw or mandible can move relative to other bones of the skull. The entire head can move throughout the respiratory treatment period.

[0009] Due to these challenges, some masks, especially when worn for extended periods or when the patient is unfamiliar with the system, are constricting, aesthetically undesirable, expensive, poorly fitting, difficult to use, and uncomfortable for one or more reasons. For example, pilot masks, personal protective equipment (e.g., filter masks), masks designed as part of a SCUBA mask, or masks used for administering anesthesia may be tolerable for their original purpose, but in such cases, they may be too uncomfortable to wear for extended periods (e.g., several hours). This discomfort can lead to decreased patient compliance with treatment, especially if the mask needs to be worn during sleep.

[0010] CPAP therapy is highly effective in treating certain respiratory conditions when the patient consents to the treatment. The availability of a patient interface enables the patient's participation in positive airway pressure therapy. When a patient is seeking a first-line or new patient interface as a replacement for an older interface, they often consult their durable medical device provider to determine a recommended interface size based on measurements of the patient's facial anatomy, which is often performed by the durable medical device provider. If the mask is uncomfortable or difficult to use, the patient may not consent to the treatment. Since patients are often advised to wash their masks regularly, if the mask is difficult to clean (e.g., difficult to assemble or disassemble), the patient may be unable to clean the mask, which can affect patient compliance. For pneumatic therapy to be effective, it is necessary not only to provide comfort to the patient while wearing the mask, but also to ensure a secure seal between the face and the mask to minimize air leakage.

[0011] As described above, patient interfaces can be provided to patients in a variety of forms (e.g., nasal masks, full-face / nasal-mouth masks (FFMs), or nasal pillow masks). Such patient interfaces are manufactured with a variety of dimensions to accommodate the anatomical features of a particular patient, in order to facilitate a comfortable interface that functions, for example, to provide positive pressure therapy. Such patient interface dimensions may be customized to accommodate the specific facial anatomical structure of a particular patient, or they may be designed to accommodate a group of individuals with anatomical structures that fall within a predefined spatial boundary or range. However, in some cases, masks may be provided in a variety of standard sizes, and it may be necessary to select the appropriate one from among them.

[0012] In this regard, sizing patient interfaces to suit the patient is often done by trained individuals (e.g., Durable Medical Equipment (DME) providers). (or physician). Typically, when a patient requires a patient interface for initiating or continuing positive airway pressure (PAD) therapy, they visit a trained individual at a service facility. At this facility, a series of measurements are taken to determine the appropriate patient interface size from standard sizes. The appropriate size means a specific combination of dimensions for certain features (e.g., the seal-forming structure of the patient interface) that provides adequate comfort and sealing for effective PAD therapy. Thus, sizing is not only laborious but also inconvenient. The inconvenience of taking time out of a busy schedule, and in some cases the need to travel long distances, is a barrier for many patients to receive new or replacement patient interfaces, and ultimately, a barrier to receiving treatment. As a result of this inconvenience, patients may not receive the necessary patient interface and may not be able to participate in PAD therapy. Nevertheless, selecting the optimal size is crucial for the quality and adherence to treatment. [Overview of the project] [Problems that the invention aims to solve]

[0013] Obtaining feedback from mask users is essential for future mask designers to improve the design interface. A system is needed to collect mask user feedback data in relation to stored facial dimension data across a wide user population. A system is also needed to correlate user feedback data with other data related to selected masks. [Means for solving the problem]

[0014] According to the disclosed system, an adaptable system is provided for collecting user feedback data related to masks used with RPT devices. This system combines facial image data with collected RPT behavior data and other data (e.g., subjective data from patient populations to assist in mask design).

[0015] One example of disclosure is a method for collecting data related to the patient interface for a respiratory pressure therapy device. Facial image data from the patient is correlated with the patient. Operational data of the respiratory therapy device used by the patient with the patient interface is collected. Subjective patient input data is collected from the patient in relation to the patient interface. Interface characteristics are correlated with facial image data, operation data, and subjective patient input data.

[0016] In another implementation of the method disclosed above, the patient interface is a mask. In another implementation, the respiratory pressure therapy device is one of a continuous positive airway (CPAP) device, a non-invasive ventilation (NIV) device, or an invasive ventilation device. In another implementation, facial image data is obtained from a mobile device containing an application for capturing images of the patient's face. In another implementation, the method further includes displaying the facial image along with an inserted image on the interface and collecting subjective data from the patient based on the location of the inserted image on the interface. In another implementation, subjective data is collected by displaying questions within the interface on the mobile device. In another implementation, the interface displays a sliding scale for the patient to input responses. In another implementation, the facial image data includes face height, nasal width, and nasal depth. In another implementation, the method includes adjusting the properties of the interface to avoid leakage. These properties are associated with contact between the face surface and the interface. In another implementation, the method includes adjusting the properties of the interface to improve comfort. These properties are associated with contact between the face surfaces. In another run, facial image data from a second patient similar to the patient, operational data from a respiratory therapy device used by the second patient, and subjective data input from the second patient are collected and correlated with the characteristics of the interface. It is used in the following process. In another run, facial image data, motion data, and subjective patient input data are collected from multiple patients, including the patient. Machine learning is applied to determine the types of motion data, subjective data, and facial image data correlated with characteristics, thereby adjusting the characteristics of the interface.

[0017] Another example of disclosure is a system having a control system which includes one or more processors and a memory for storing machine-readable instructions. The control system is linked to the memory. The above method is performed when a machine-executable instruction in the memory is executed by one of the processors of the control system.

[0018] Another example of disclosure is a system that communicates one or more displays to a user. The system includes a control system configured to perform one of the methods described above.

[0019] Another example of disclosure is a computer program product having instructions. When these instructions are executed by a computer, the computer performs one of the methods described above. In another execution of the exemplary computer program product, the computer program product is a non-temporary, computer-readable medium.

[0020] Another example of disclosure is a system for collecting feedback data from patients using an interface with a respiratory pressure therapy device. This system includes a storage device that stores the patient's facial images. When the patient uses the interface, a data communication interface communicates with the respiratory pressure therapy device to collect motion data from it. A patient data collection interface collects subjective patient input data from the patient in relation to the patient interface. An analysis module can operate to correlate the interface characteristics with facial image data, motion data, and subjective patient input data.

[0021] In other executions of the system disclosed above, the system includes a manufacturing system that produces interfaces based on design data. This analysis module modifies the design data based on correlated characteristics. In another execution, the patient interface is a mask. In another execution, the respiratory pressure therapy device is one of a continuous positive airway (CPAP) device, a non-invasive ventilation (NIV) device, or an invasive ventilation device. In another execution of the system, a mobile device runs an application that captures images of the patient's face. In another execution, the patient data collection interface displays the face image along with an inserted image on the interface and collects subjective data from the patient based on the location of the inserted image on the interface. In another execution, the collection of subjective data is performed by displaying questions within the interface on the mobile device. In another execution, the interface displays a sliding scale for the patient to input responses. In another execution, the facial image data includes face height, nasal width, and nasal depth. In another execution, the characteristics of the interface are modulated to avoid leakage. This characteristic is associated with the contact between the face surface and the interface. In another execution, the characteristics of the interface are modulated to improve comfort. This characteristic is associated with the contact between the facial surface and the interface. In another execution, the system includes a machine learning module. This machine learning module can operate to adjust the interface characteristics by determining motion data, subjective data, and facial image data from multiple patients correlated with the characteristics.

[0022] The above summary is not intended to illustrate each embodiment or aspect of the present disclosure. Rather, it merely illustrates some examples of novel aspects and features described herein. The above features and advantages, as well as other features and advantages of the present disclosure, are described in detail below in the accompanying drawings and appendices, as are representative embodiments and aspects for the execution of the invention. This becomes easily clear when read together with the patent claims.

[0023] The following description of the exemplary embodiments, taken in conjunction with the accompanying drawings, will enhance the understanding of the present disclosure.

Brief Description of the Drawings

[0024] [Figure 1] The system shown in FIG. 1 shows a patient wearing a patient interface in the form of a full-face mask and receiving PAP therapy from an exemplary respiratory pressure therapy device. [Figure 2] A patient interface in the form of a nasal mask with a headgear according to one form of the present technology is shown. [Figure 3A] A front view of a face including some features of the surface anatomy structure. [Figure 3B] A side view of a head including some features of the surface anatomy structure. [Figure 3C] A bottom view of a nose with some features identified. [Figure 4A] A diagram of a respiratory pressure therapy device according to one form of the present technology. [Figure 4B] A schematic diagram of the pneumatic path of a respiratory pressure therapy device according to one aspect of the present technology. [Figure 4C] A schematic diagram of the electrical components of a respiratory pressure therapy device according to one aspect of the present technology. [Figure 5] A diagram of an exemplary system for collecting patient data related to a patient interface including a computing device. [Figure 6] A diagram of the components of a computing device used for capturing face data. [Figure 7A] An exemplary interface that may enable the generation of a face image for face data capture by a face scan. [Figure 7B] An exemplary interface showing a face mesh inserted onto the face image in FIG. 7A for face measurement data collection. [Figure 7C] An exemplary sleep position data collection interface. [Figure 7D] This is an exemplary sleep type data collection interface. [Figure 8A] This is an illustrative interface that allows current masks to be identified by nose shape or mouth / nose shape. [Figure 8B] This is an illustrative interface that allows for the identification of the mask's brand. [Figure 8C] This is an illustrative interface that allows for the identification of a specific mask model. [Figure 8D] This is an illustrative interface that allows you to specify the mask size. [Figure 9A] This is an exemplary image command interface that enables mask image capture. [Figure 9B] This is an image capture interface for capturing images of masks. [Figure 9C] This is a post-image capture interface for displaying captured images. [Figure 9D] This is an exemplary interface for deciding on the short-term use of a mask. [Figure 9E] This is an exemplary interface for deciding on the long-term use of masks. [Figure 10A] This is an illustrative interface that provides instructions to determine the location of mask discomfort. [Figure 10B] This is an illustrative interface that allows users to graphically select the location where mask-related discomfort occurs. [Figure 10C] This is an illustrative interface in Figure 10B after the user has selected an area of ​​discomfort. [Figure 10D] This is an illustrative interface that provides instructions to determine the location of the mask air leak. [Figure 10E] This is an illustrative interface that allows users to graphically select the location where a mask air leak is occurring. [Figure 10F] This is an illustrative interface in Figure 10B after the user has selected the area where mask leakage occurred. [Figure 11A] This is an exemplary slider interface for collecting subjective data regarding the effects of air leaks. [Figure 11B] This is an illustrative slider interface for collecting subjective data on patient satisfaction with masks. [Figure 11C] This is an exemplary interface for collecting patient demographic data. [Figure 11D] This is an illustrative diagram that may be inserted to collect information on discomfort and air leakage depending on the selected mask type. [Figure 12] This is a tree diagram of the feedback data collected in relation to the decision-making process for the mask design. [Figure 13] This is a flowchart illustrating the process of collecting feedback data from patients to determine mask characteristics. [Figure 14] This is a diagram of a system that generates a modified interface based on collected feedback data. [Modes for carrying out the invention]

[0025] This disclosure includes various variations and alternative forms. Several representative embodiments illustrated in the drawings are described in detail below herein. However, it should be understood that the present invention is not intended to be limited to any particular form disclosed, and rather this disclosure covers all variations, equivalents, and alternatives that fall within the spirit and scope of the invention as defined by the appended claims.

[0026] The present invention can be embodied in numerous different forms. Representative embodiments are shown in the drawings and are described in detail below in this specification. This disclosure is an example or illustration of the principles of this disclosure and is not intended to limit the broader aspects of this disclosure to the examples given. Therefore, elements or limitations disclosed in, for example, the sections “Abstract,” “Summary of the Invention,” and “Modes for Carrying Out the Invention,” but not expressed in the claims, should not be incorporated into the claims, individually or collectively, by suggestion, inference, or otherwise. In this specification, unless otherwise specified, singular nouns include plural nouns, and vice versa. The phrase “including” means “including but not limited to.” Furthermore, in this specification, approximation words such as “approximately,” “about,” “substantially,” and “around” may be used to mean, for example, “just,” “near,” “around,” “within 3-5%,” “within acceptable manufacturing tolerances,” or any logical combination thereof.

[0027] This disclosure relates to a system and method for collecting feedback data from a mask selected to fit a user of a respiratory pressure therapy device. The mask is sized based on facial data collected to fit the user. The user is presented with an interface for collecting feedback data about the sized mask. This data is analyzed to further refine the design of masks for similar patients based on elements (e.g., motion data, patient demographics, patient facial features).

[0028] Figure 1 shows a system including a patient 10 wearing a patient interface 100. This system takes the form of a full-face mask (FFM) and receives positive-pressure air supplied from a respiratory pressure therapy (RPT) device 40. The air from the RPT device 40 is humidified by a humidifier 60 and travels to the patient 10 along an air circuit 50.

[0029] Figure 2 shows a patient interface 100 according to one embodiment of the present technology. The patient interface 100 includes the following functional aspects: a seal-forming structure 160, a plenum chamber 120, a positioning and stabilizing structure 130, a ventilation section 140, a forehead support section 150, and a connection port 170 in one form for connection to the air circuit 50 in Figure 1. In several embodiments... The functional modes may be provided by one or more physical components. In some forms, one physical component may provide one or more functional modes. When in use, the seal-forming structure 160 is positioned to surround the entrance to the patient's airway in order to facilitate the supply of positive pressure air to the airway.

[0030] In one embodiment of this technology, the seal-forming structure 160 may provide a seal-forming surface and further provide a cushioning function. The seal-forming structure 160 according to this technology may be composed of a soft, flexible, and elastic material (e.g., silicone). In one embodiment, the seal-forming portion of the non-invasive patient interface 100 includes a pair of nasal puffs or nasal pillows. Each nasal puff or nasal pillow is configured and positioned to form a seal with each nostril of the patient's nose.

[0031] The nasal pillow according to this technology includes a frustum of a cone. At least a portion of the frustum of a cone forms a seal on the underside of the patient's nose, on the stalk, and on a flexible region above the underside of the frustum of a cone, connecting the frustum of a cone to the stalk. In addition, the structure to which the nasal pillow of this technology is connected includes a flexible region adjacent to the base of the stalk. The flexible region may function to facilitate a flexible connection structure. The flexible connection structure accommodates both the displacement and angle of the frustum of a cone and the mutual movement between the structure to which the nasal pillow is connected. For example, the frustum of a cone may be displaced axially toward the structure to which the stalk is connected.

[0032] In one embodiment, the non-invasive patient interface 100 includes a seal-forming portion that forms a seal on the upper lip region (i.e., the upper lip) of the patient's face. In another embodiment, the non-invasive patient interface 100 includes a seal-forming portion that forms a seal on the jaw region of the patient's face when in use.

[0033] Preferably, the plenum chamber 120 has edges that are shaped to be complementary to the surface contour of an average human face in the area where a seal is formed during use. During use, the peripheral edges of the plenum chamber 120 are positioned close to the adjacent surfaces of the face. Actual contact with the face is provided by the seal-forming structure 160. The seal-forming structure 160 may extend around the entire edge of the plenum chamber 120 during use.

[0034] Preferably, the seal-forming structure 160 of the patient interface 100 of this technology can be held in a sealed position by the positioning and stabilization structure 130 during use.

[0035] In one embodiment, the patient interface 100 includes a vent 140 configured and positioned to allow the expulsion of exhaled carbon dioxide. The vent 140 in one embodiment of the present invention includes a plurality of holes (for example, about 20 to about 80 holes, or about 40 to about 60 holes, or about 45 to about 55 holes).

[0036] Figure 3A shows a frontal view of a human face including the medial canthus, alae, nasolabial folds, upper and lower lips, upper and lower lip redness, and corners of the mouth. The mouth width, the sagittal plane dividing the head into left and right halves, and directional indicators are also shown. The directional indicators indicate radial medial / lateral and superior / inferior directions. Figure 3B is a lateral view of a human face including the glabella, selion, bridge of the nose, nasal tip, subphiltrum, upper and lower lips, supramenton, ala apex, and superior and inferior base of the ear. Directional indicators indicating superior / inferior and anterior / posterior directions are also shown. Figure 3C is a pedictal view of the nose including several features, such as the nasolabial folds, lower lip, upper lip redness, nostrils, subphiltrum, columella, nasal tip, and the main axis and sagittal plane of the nostrils.

[0037] The features of the human face shown in Figures 3A to 3C are described in more detail below.

[0038] Wing (Ala): The outer wall or the "wing" of each nostril (plural: alar)

[0039] Alare: The outermost point on the nasal ala.

[0040] Wing curvature (or nostril apex) point: The furthest point on the curved reference line of each wing, found at the fold formed by the joining of the wing and cheek.

[0041] Auricle: The entire visible part of the ear.

[0042] Columella: A piece of skin that separates the nostrils, extending from the tip of the nose to the upper lip.

[0043] Columella angle: The angle between a line drawn through the midpoint of the nostrils and a line drawn perpendicular to the Frankfurt horizontal, intersecting the subnasal point.

[0044] Glabella: Located in soft tissue, it is the most prominent point in the midline sagittal direction of the forehead.

[0045] Nostrils: Generally, ellipsoidal pterygoides form the entrance to the nasal cavity. The singular form of nostril (nares) is nostril (naris). These nostrils are separated by the nasal septum.

[0046] Nasolabial fold or groove: A fold or groove of skin that extends from each side of the nose to the corners of the mouth, separating the cheek from the upper lip.

[0047] Nasolabial angle: The angle between the columella and the upper lip, which intersects with the subnasal point.

[0048] Inferior basement point: The lowest point where the auricle attaches to the skin of the face.

[0049] Superior basement point: The highest point where the auricle attaches to the skin of the face.

[0050] Nasal tip: The most prominent point or tip of the nose, which can be seen in a lateral view of the rest of the head.

[0051] Philtrum: The midline groove extending from the lower boundary of the nasal septum to the upper part of the lip in the upper lip region.

[0052] Pogonion: The anterior midpoint of the jaw, located on soft tissue.

[0053] Nasal ridge: The nasal ridge is the midline elevation of the nose, extending from the therion to the nasal tip.

[0054] Sagittal plane: A vertical plane that extends from the front (front) to the back (back), dividing the main body into a right half and a left half.

[0055] Serion: The most concave point located on soft tissue within the region of the frontonasal suture.

[0056] Septal cartilage (nose): The nasal septum cartilage is part of the septum and divides the anterior part of the nasal cavity.

[0057] The lowest point of the nasal ala: This is a point on the lower periphery of the wing base, where the wing base joins the skin of the upper lip.

[0058] Subnasal point: Located on soft tissue, this is the point where the columella merges with the upper lip in the midline sagittal direction.

[0059] Splamenton: The most concave point in the midline of the lower lip, between the midpoint of the lower lip and the soft tissue pogonion. .

[0060] As explained below, there are several important dimensions from the face that can be used in selecting the size of a patient interface, such as mask 10 in Figure 1. In this example, there are three dimensions: face height, nasal width, and nasal depth. Line 3010 in Figures 3A and 3B represents face height. As can be seen from Figure 3B, face height is the distance from the serion to the supramenton. Line 3020 in Figure 3A represents nasal width between the left and right wing points. Line 3030 in Figure 3B represents nasal depth.

[0061] Figure 4A shows an exploded view of the components of an exemplary RPT device according to one aspect of the Art, which includes mechanical, pneumatic, and / or electrical components and is configured to perform one or more algorithms (e.g., any of the methods described herein, either entirely or in part). Figure 4B shows the lower part of the RPT device 40. Figure 4C is a schematic diagram of the electrical components of the RPT device 40 according to one aspect of the Art. Upstream and downstream directions are indicated with respect to the blower and the patient interface. Regardless of the actual flow direction at any particular moment, the blower is defined as being upstream of the patient interface and the patient interface as being downstream of the blower. Items placed in the pneumatic path between the blower and the patient interface are downstream of the blower and upstream of the patient interface. The RPT device 40 may be configured to generate an airflow delivered to the patient's airway for the treatment of one or more respiratory conditions, for example.

[0062] The RPT device 40 may have an external housing 4010. The external housing 4010 is formed by two parts (i.e., an upper part 4012 and a lower part 4014). Furthermore, the external housing 4010 may include one or more panels 4015. The RPT device 40 includes a chassis 4016 that supports one or more internal components of the RPT device 40. The RPT device 40 may include a handle 4018.

[0063] The pneumatic path of the pneumatic RPT device 40 may include one or more air path items (e.g., an inlet air filter 4112, an inlet muffler 4122, a pressure generator 4140 (e.g., a blower 4142) capable of supplying air at positive pressure, an outlet muffler 4124) and one or more transducers 4270 (e.g., a pressure sensor 4272, a flow sensor 4274, and a motor speed sensor 4276).

[0064] One or more of the air passage items may be housed within a removable, integrated structure called a pneumatic block 4020. The pneumatic block 4020 may be housed within an external housing 4010. In one embodiment, the pneumatic block 4020 is supported by or formed as part of the chassis 4016.

[0065] The RPT device 40 may have a power supply 4210, one or more input devices 4220, a central controller 4230, a pressure generator 4140, a data communication interface 4280, and one or more output devices 4290. The therapeutic device may be provided with a separate controller. The electrical components 4200 may be mounted on a single printed circuit board assembly (PCBA) 4202. In one alternative configuration, the RPT device 40 may include more than one PCBA 4202. Other components such as one or more protection circuits 4250, transducers 4270, a data communication interface 4280, and memory devices may also be mounted on the PCBA 4202.

[0066] An RPT device may include one or more of the following components in a single unit. In one alternative configuration, one or more of the following components may be arranged as separate units.

[0067] An RPT device according to one embodiment of this technology may include an air filter 4110 or a plurality of air filters 4110. In one embodiment, an inlet air filter 4112 is located at the beginning of the upstream pneumatic path of a pressure generator 4140. In one embodiment, an outlet air filter 4114 (e.g., antimicrobial factor) is located between the outlet of a pneumatic block 4020 and the patient interface 100.

[0068] An RPT device according to one embodiment of this technology may include a muffler 4120 or a plurality of mufflers 4120. In one embodiment of this technology, the inlet muffler 4122 is located above the pressure generator 4140 in the pneumatic path. In one embodiment of this technology, the outlet muffler 4124 is located between the pressure generator 4140 and the patient interface 100 in Figure 1 in the pneumatic path.

[0069] In one embodiment of this technology, a pressure generator 4140 that generates an airflow or supply at positive pressure is a controllable blower 4142. For example, the blower 4142 may include a brushless DC motor 4144 having one or more impellers. The impellers may be positioned within a volute. The blower can deliver the air supply at a speed of, for example, up to about 120 liters / minute at a positive pressure in the range of about 4 cmH2O to about 20 cmH2O, or in other embodiments up to about 30 cmH2O. The blower may be described in any one of the following patents or patent applications, which are incorporated herein by reference: U.S. Patent No. 7,866,944, U.S. Patent No. 8,638,014, U.S. Patent No. 8,636,479 and PCT Patent Application Publication WO2013 / 020167.

[0070] The pressure generator 4140 is under the control of the treatment device controller 4240. In other embodiments, the pressure generator 4140 may be a piston-driven pump, a pressure regulator connected to a high-pressure source (e.g., a pressurized air reservoir), or a bellows.

[0071] An air circuit 4170 according to one aspect of this technology is a conduit or tube constructed and arranged so that a pressurized airflow moves between two components (e.g., a humidifier 60 and a patient interface 100) during use. In detail, the air circuit 4170 may be in fluid communication with the outlet of the humidifier 60 and the plenum chamber 120 of the patient interface 100.

[0072] In one embodiment of this technology, an anti-spillback valve 4160 may be positioned between the humidifier 60 and the pneumatic block 4020. The anti-spillback valve is constructed and positioned to reduce the risk of water flowing upstream from the humidifier 60 (for example, to the blower motor 4144).

[0073] The power supply 4210 may be located inside or outside the external housing 4010 of the RPT device 40. In one embodiment of this technology, the power supply 4210 supplies power only to the RPT device 40. In another embodiment of this technology, power is supplied from the power supply 4210 to both the RPT device 40 and the humidifier 60.

[0074] The RT system may comprise one or more transducers (sensors) 4270 configured to measure one or more of any number of parameters relating to the RT system, its patient, and / or its environment. The transducers may be configured to produce output signals representing one or more parameters that the transducers are configured to measure.

[0075] This output signal may be one or more of any number of other signals known in the art, such as electrical signals, magnetic signals, mechanical signals, visual signals, optical signals, and sound signals.

[0076] Transducers can be integrated with other components of the RT system, with one exemplary configuration being that the transducer is built into the RPT device. Transducers can also be substantially "standalone" components of the RT system, with an exemplary configuration being that the transducer is external to the RPT device.

[0077] A transducer may be configured to transmit its output signal to one or more components of the RT system, such as an RPT device, a local external device, or a remote external device. External transducers may be located, for example, in a patient interface or an external computing device such as a smartphone. External transducers may be located, for example, on an air path or form part of an air path (e.g., a patient interface).

[0078] One or more transducers 4270 may be constructed and positioned to generate signals that describe the characteristics of air (e.g., flow rate, pressure, or temperature). The air may be airflow from the RPT device to the patient, airflow from the patient to the atmosphere, ambient air, or other. The signals may represent the nature of the airflow at a particular point, such as airflow in the pneumatic path between the RPT device and the patient. In one embodiment of this technology, one or more transducers 4270 may be positioned upstream and / or downstream of the pressure generator 60.

[0079] According to one aspect of this technology, one or more transducers 4270 are equipped with a pressure sensor located in fluid communication with a pneumatic path. One example of a suitable pressure sensor is a transducer from the HONEYWELL ASDX series. Another suitable pressure sensor is a transducer from the GENERAL ELECTRIC NPA series. In one embodiment, the pressure sensor is located in an air circuit 4170 adjacent to the outlet of the humidifier 60.

[0080] The microphone pressure sensor 4278 is configured to generate an acoustic signal representing a change in pressure within the air circuit 4170. The acoustic signal from the microphone 4278 may be received by the central controller 4230 for acoustic processing and analysis, as comprised of one or more of the algorithms described below. The microphone 4278 may be directly exposed to the air path to enhance its sensitivity to sound, or it may be enclosed behind a thin layer of flexible membrane material. This membrane may serve to protect the microphone 4278 from heat and / or moisture.

[0081] Data from transducers 4270, such as pressure sensor 4272, flow sensor 4274, motor speed sensor 4276, and microphone 4278, may be periodically collected by the central controller 4230. Such data is primarily related to the operating state of the RPT device 40. In this example, the central controller 4230 encodes such data from the sensors in a dedicated data format. Data encoding may be performed in a standardized data format.

[0082] In one embodiment of this technology, the RPT device 40 includes one or more input devices 4220 in the form of buttons, switches, or dials to enable human interaction with the device. The buttons, switches, or dials may be physical or software devices accessible via a touchscreen. The buttons, switches, or dials may, in one embodiment, be physically connected to an external housing 4010, or in another embodiment, be wirelessly connected to a receiver electrically connected to a central controller 4230. In one embodiment, the input devices 4220 may be constructed and arranged to enable human selection of values ​​and / or menu options.

[0083] In one embodiment of this technology, the central controller 4230 controls the RPT device 40. One or more suitable processors. Suitable processors may include x86 INTEL processors, which are processors based on the ARM® Cortex®-M processor from ARM Holdings (e.g., S®32 series microcontrollers from ST Microelectronics). In certain alternative forms of this technology, a 32-bit RISC CPU (e.g., the STR9 series macrocontroller from ST Microelectronics) or a 16-bit RISC CPU (e.g., processors from the MSP430 family of macrocontrollers manufactured by Texas Instruments) may also be suitable. In one form of this technology, the central controller 4230 is a dedicated electronic circuit. In one form, the central controller 4230 is an application-specific integrated circuit. In another form, the central controller 4230 includes discrete electronic components. The central controller 4230 may be configured to receive input signals (one or more) from one or more transducers 4270, one or more input devices 4220, and a humidifier 60.

[0084] The central controller 4230 may be configured to provide output signals (one or more) to one or more of the output devices 4290, the treatment device controller 4240, the data communication interface 4280, and the humidifier 60.

[0085] In some forms of this technology, the central controller 4230 is configured to embody one or more methods described herein (e.g., one or more algorithms expressed as computer programs recorded on a non-temporary computer-readable recording medium in internal memory). In some forms of this technology, the central controller 4230 may be integrated with the RPT device 40. However, in some forms of this technology, some methods may be performed by a remotely located device (e.g., a mobile computing device). For example, the remotely located device may determine the control settings of the ventilator or detect respiratory-related events by analyzing recorded data (e.g., from any of the sensors described herein). As described above, all data and operations from external sources or the central controller 4230 are often proprietary to the manufacturer of the RPT device 40. Therefore, data from sensors and any other further operational data are often inaccessible from any other device.

[0086] In one embodiment of this technology, a data communication interface is provided and connected to a central controller 4230. The data communication interface may be connectable to a remote external communication network and / or a local external communication network. The remote external communication network may be connectable to a remote external device (e.g., a server or database). The local external communication network may be connectable to a local external device (e.g., a mobile device or a health monitoring device). Therefore, the local external communication network may be used by either the RPT device 40 or a mobile device for the purpose of collecting data from other devices.

[0087] In one embodiment, the data communication interface is part of the central controller 4230. In another embodiment, the data communication interface 4280 is separate from the central controller 4230 and may include an integrated circuit or processor. In one embodiment, the remote external communication network is the Internet. The data communication interface may use wired communication (e.g., via Ethernet® or optical fiber) or wireless protocols (e.g., CDMA, GSM®, 2G, 3G, 4G / LTE Cat-M, NB-IoT, 5G New Radio, satellite, beyond 5G) to connect to the Internet. In one embodiment, the local external communication network 4284 uses one or more communication standards (e.g., Bluetooth® or consumer infrared protocol).

[0088] As shown in Figure 4C, the exemplary RPT device 40 includes an integrated sensor and communication electronics. For older RPT devices, retrofitting may be possible with a sensor module that may include communication electronics for transmitting collected data. Such a sensor module can be attached to the RPT device and transmit operational data to the remote analysis engine 130.

[0089] In some implementations of the disclosed acoustic analysis techniques, cepstrum analysis may be performed based on an audio signal from a sensor (e.g., audio sensor 4278). The audio signal may reflect the user's physiological state (e.g., sleep or breathing) as well as RPT operating data. The cepstrum can be considered, for example, the inverse Fourier transform of the logarithmic spectrum of the forward Fourier transform of the decibel spectrum. This operation allows for a substantial conversion of the impulse response function (IRF) and the convolution of the sound source into an additive operation, which then allows for easy consideration of the sound source or removal of the sound source to separate the IRF data for analysis. For detailed information on cepstrum analysis techniques, please refer to the following scientific publications: "The Cepstrum: A Guide to Processing" (Childers et al., Proceedings of the IEEE, Vol. 65, No. 10, Oct 1977) and Randall RB, Frequency Analysis, Copenhagen: Bruel & Kjaer, p. 344 (1977, revised ed. 1987). The application of cepstrum analysis to respiratory treatment system component characteristics is described in PCT publication WO2010 / 091462 (title: "Acoustic Detection for Respiratory Treatment Apparatus"). This entire publication is referenced herein.

[0090] As described above, respiratory therapy systems typically include an RPT device, humidifier, air delivery conduit, and patient interface (e.g., components shown in Figure 1). A variety of different forms of patient interfaces can be used with a given RPT device (e.g., nasal pillow, nasal prongs, nasal mask, nasal & mouth (oral-nasal) mask, or full-face mask). Furthermore, different forms of air delivery conduits can be used. To improve control of therapeutic delivery to the patient interface, the measurement or estimation of therapeutic parameters (e.g., pressure and airflow in the patient interface) may be analyzed. In older systems, the type of component the patient is using may be determined as follows to determine the optimal interface for the patient. Some RPT devices include a menu system that allows the patient to select the type of system component (e.g., the patient interface being used) (e.g., brand, form, model). Once the patient enters the component type, the RPT device can select appropriate operating parameters for the flow generator that best work with the selected component. Data collected by the RPT device may be used to evaluate the effectiveness of a particular selected component (e.g., the patient interface in pressurized air delivery to the patient).

[0091] The analytical methods included in this technology enable the separation of acoustic mask reflections from other system noise and responses (for example, fan noise). As a result, it becomes possible to identify differences between acoustic reflections from different masks (which are often determined by the shape, composition, and material of the mask), potentially allowing for the identification of different masks without user or patient intervention.

[0092] As an exemplary method for identifying the mask, the output audio signal y(t) generated by microphone 4278 is sampled at least at the Nyquist rate (e.g., 20 kHz), the cepstrum is calculated from the sampled output signal, and then the cepstrum is... There is a method for separating the ram's reflective component from the cepstrum's input signal component. The cepstrum's reflective component includes acoustic reflections from the input audio signal's mask and is therefore called the mask's "acoustic signature" or "mask signature." The acoustic signature is then compared to a predetermined database of previously measured acoustic signatures obtained from a predefined database or a system containing known masks. Optionally, certain criteria are set for determining appropriate similarity. In one exemplary embodiment, the above comparison may be completed based on a single maximum data peak in the cross-correlation between the measured and stored acoustic signatures. However, this approach may also be improved by comparing several data peaks, or these comparisons may be completed for a unique set of extracted cepstrum features.

[0093] Subsequently, according to this technology, the data associated with the reflective component can be compared with similar data from previously identified masked reflective components (e.g., those contained in the memory or database of the masked reflective component).

[0094] As described above, the RPT device 40 can provide patient interface type data and operational data. By correlating operational data with mask type and patient-related data, it may be possible to determine whether a particular mask type is effective. For example, operational data may reflect both the duration of use of the RPT device 40 and whether or not there is therapeutic effectiveness due to its use. The patient interface type can be correlated with the level of patient compliance or therapeutic effectiveness, as determined from the operational data collected by the RPT device 40. By using the correlated data, an effective interface can be more effectively determined for new patients requiring respiratory therapy from similar RPT devices. This selection is further aided by combining it with facial dimensions obtained from a facial scan of the new patient.

[0095] Therefore, this technology enables patients to obtain patient interfaces such as masks more quickly and easily by integrating data collected from RPT device use in relation to different masks from the patient population (including individual patient facial features determined by the scanning process). The scanning process allows patients to quickly measure the anatomical structure of their own face from the comfort of their home using a computing device (e.g., a desktop computer, tablet, smartphone, or other mobile device). The computing device can then receive recommendations for appropriate patient interface sizes after analysis of the patient's facial dimensions, as well as types and data from the general patient population related to different interfaces. Facial data collection may also be performed by other methods, such as pre-stored facial images. Such facial data is stored and correlated with patient-related information and operational data from the RPT device.

[0096] In this example, an application downloadable from the manufacturer or a third-party server to a smartphone or tablet equipped with an integrated camera may be used for facial data collection. Once activated, the application may provide visual and / or audio commands. Following the commands, the user (i.e., the patient) may stand in front of a mirror and press the camera button on the user interface. The activated process then takes a series of photographs of the user's face and, for example, within about two seconds, may obtain facial dimensions for interface selection (based on the processor's photo analysis). After mask selection and commencement of use with the RPT40, such an application may be used to collect feedback from the user.

[0097] The user / patient may capture one or a series of images of their facial structure. Instructions provided by an application stored on a computer-readable medium are For example, when performed by a processor, various facial landmarks in the image are detected, the distances between these landmarks are measured and scaled, these distances are compared to data records, and an appropriate patient interface size is recommended. Thus, consumer-facing automated devices may enable highly accurate patient interface selection, for example, at home, and customers may be able to determine the size (without the presence of a trained partner).

[0098] The exemplary system 200 shown in Figure 5 may be used to collect patient interface feedback data from a patient. System 200 may also include automated facial feature measurement and patient interface selection. System 200 may primarily consist of one or more of a server 210, a communication network 220, and a computing device 230. The server 210 and the computing device 230 may communicate via the communication network 220. The communication network 220 may be a wired network 222, a wireless network 224, or a wired network with a wireless link 226. In some versions, the server 210 may communicate unidirectionally with the computing device 230 by providing information to the computing device 230, and vice versa. In other embodiments, the server 210 and the computing device 230 may share information and / or processing tasks. System 200 may be implemented to enable automated purchasing of a patient interface (e.g., mask 100 in Figure 1), where the process may include an automated sizing process, which is described in more detail herein. For example, after performing a mask selection process, a customer may be able to order a mask online. According to this mask selection process, the appropriate mask size is automatically identified by combining image analysis of the customer's facial features with behavioral data from other masks, as well as RPT behavioral data from a patient population using masks of different types and sizes. After the mask is used by the patient, the system 200 continuously collects feedback data.

[0099] Server 210 and / or computing device 230 may also communicate with a respiratory therapy device (e.g., RPT250 similar to RPT40 shown in Figure 1). In this example, RPT device 250 may provide feedback related to mask use by collecting motion data in relation to patient use, mask leakage, and other relevant data. Data from RPT device 250 is collected and correlated with the patient's individual patient data using RPT device 250 in patient database 260. Patient interface database 270 contains data on different types and sizes of interfaces (e.g., masks) that a new patient may have access to. Patient interface database 270 may also contain acoustic signature data for each type of mask. This acoustic signature data may enable the determination of the mask type from the voice data collected from the respiratory therapy device. A mask analysis engine run by server 210 is used to correlate and determine effective mask sizes and shapes from individual facial dimension data with corresponding effectiveness from motion data (covering the entire patient population) collected by RPT device 250. For example, evidence of effective fitting includes minimal leakage detection, maximum adherence to the treatment plan (e.g., mask-on and mask-off times, and frequency of on and off events), number of apneas per night, AHI level, and pressure settings used on the patient's device or specified pressure settings. This data can be correlated with the new patient's facial dimension data. As described below, the server 210 collects data from multiple patients stored in database 260 and corresponding mask size type data stored in database 270 to select the optimal mask that best fits the scanned facial dimension data collected from the new patient, and the mask that achieved the best performance data for patients with similar facial dimensions, sleep behavior data, and demographic data to the new patient. Such data is used when the patient uses the RPT device 250. This is supplemented by additional feedback in the form of subjective data entered by the user, and operational data from the RPT device 250 associated with the patient interface or mask.

[0100] The computing device 230 may be a desktop or laptop computer 232 or a mobile device (e.g., a smartphone 234 or a tablet 236). Figure 6 shows a typical architecture 300 of the computing device 230. The computing device 230 may include one or more processors 310. The computing device 230 may also include a display interface 320, a user control / input interface 331, a sensor 340 and / or a sensor interface for one or more sensors, an inertial measurement unit (IMU) 342 and a non-volatile memory / data storage device 350.

[0101] The sensor 340 may be one or more cameras (e.g., CCD charge-coupled elements or active pixel sensors) and be integrated with the computing device 230 (e.g., located in a smartphone or laptop). Alternatively, if the computing device 230 is a desktop computer, the device 230 may include a sensor interface for connection to an external camera (e.g., a webcam 233 shown in Figure 5). Other exemplary sensors that may be used to assist the methods described herein may be integrated with the computing device or located outside the computing device, and include, for example, a stereo camera for three-dimensional image capture or a photodetector capable of detecting reflected light from a laser or strobe / structure light source.

[0102] The user control / input interface 331 allows the user to provide commands or respond to prompts or instructions provided to the user. This could be, for example, a touch panel, keyboard, mouse, microphone, and / or speaker.

[0103] The display interface 320 may include a monitor, LCD panel, etc., for displaying prompts, output information (e.g., face measurement or interface size recommendations), and other information (e.g., capture displays, as further detailed below).

[0104] The memory / data storage device 350 may be the internal memory of the computing device (e.g., RAM, flash memory, or ROM). In some embodiments, the memory / data storage device 350 may be external memory linked to the computing device 230 (e.g., an SD card, server, USB flash drive, or optical disc). In other embodiments, the memory / data storage device 350 may be a combination of external and internal memory. The memory / data storage device 350 includes stored data 354 and processor control instructions 352 that instruct the processor 310 to perform a specific task. The stored data 354 may include data received by the sensor 340 (e.g., captured images) and other data provided as a component part of the application. The processor control instructions 352 may be provided as a component part of the application.

[0105] As described above, facial images can be captured by a mobile computing device (e.g., a smartphone 234). A suitable application running on computing device 230 or server 210 can obtain relevant three-dimensional facial data to assist in appropriate mask selection. Any suitable facial scanning method can be used in this application. An example of such an application is given below: the Capture (StandardCyborg) (https: Applications from StandardCyborg (https: / / www.standardcyborg.com / ), Scandy Pro (https: / / www.scandy.co / products / scandy-pro), Beauty3D application from Qianxun3d (http: / / www.qianxun3d.com / scanpage), Unre 3D FaceApp (http: / / www.unre.ai / index.php?route=ios / detail), and Bellus3D (https: / / www.bellus3d.com / ). The detailed process of face scanning includes the technology disclosed in WO2017000031, which is incorporated herein by reference.

[0106] One such application is an application for measuring facial features and / or collecting patient data 360, which is downloadable to a mobile device (e.g., a smartphone 234 and / or a tablet 236). Application 360 may also collect facial features and patient data already being used in order to improve feedback collection from such masks. Application 360, which can be stored on a computer-readable medium (e.g., memory / data storage device 350), includes programmed instructions for causing the processor 310 to perform specific tasks related to measuring facial features and / or sizing the patient interface. This application also includes data that can be processed by algorithms of an automated method. Examples of such data include data records, reference features, and correction factors, which are further detailed below.

[0107] When application 360 is executed by processor 310, the patient's facial features are measured using two-dimensional or three-dimensional images, and based on the obtained measurements, an appropriate patient interface size and type is selected from, for example, a group of standard sizes. The method can be primarily characterized as comprising the following three or four distinct phases: a pre-capture phase, a capture phase, a post-capture image processing phase, and a comparison and output phase.

[0108] In some cases, an application for face feature measurement may control the processor 310 to output a visual display containing a reference feature on the display interface 320. The user may position this feature adjacent to their own face feature, for example, by moving the camera. Then, if certain conditions (e.g., alignment conditions) are met, the processor may capture one or more images of the face feature and save them in association with the reference feature. This may be done using a mirror, which reflects the displayed reference feature and the user's face back to the camera. The application then controls the processor 310 to identify a specific face feature in the image and measure the distance between them. Next, image analysis processing may convert the face feature measurement, which may be a pixel count, into a standard mask measurement based on the reference feature using a scaling factor. Such a value may be, for example, a value expressed in a reference unit (e.g., meters or inches) and a unit suitable for determining the mask size.

[0109] Further correction factors may be applied to these measurements. Facial feature measurements can be compared with data records that include measurement ranges corresponding to different patient interface sizes suited to specific patient interface configurations (e.g., nasal masks and FFMs). A recommended size may then be selected and output to the user / patient as a recommendation based on the comparison(s). Such a process can be comfortably and easily performed at any suitable user location. The application can perform this method within seconds. In one example, the application performs this method in real time.

[0110] In the pre-capture phase, the processor 310, in particular, assists the user in establishing conditions suitable for capturing one or more images for resizing. Some of these conditions include, for example, proper lighting and camera orientation, as well as motion blur (caused by the computing device 230, which is unstable because it is being held by hand).

[0111] A user can easily download an application for automatic measurement and sizing on the computing device 230 from a server (e.g., a third-party application storage server) to their computing device 230. Once downloaded, such an application can be stored in the computing device's internal non-volatile memory (e.g., RAM or flash memory). The computing device 230 is preferably a mobile device (e.g., a smartphone 234 or tablet 236).

[0112] When a user launches the application, the processor 310 may prompt the patient via the display interface 320 to provide specific information (e.g., age, gender, weight, and height). However, the processor 310 may also prompt the user to input this information at any time (e.g., after measuring the user's facial features and after the user has used a mask with the RPT). The processor 310 may also present a tutorial. This tutorial may be provided using voice and / or visually (e.g., provided by the application to help the user understand their role in the process). The prompts may also require information about the patient interface type (e.g., nose or full face) and the type of device the patient interface is targeting. In the pre-capture phase, the application may also extrapolate patient-specific information based on information already collected by the user, for example, after receiving a captured image of the user's face and based on machine learning techniques or artificial intelligence. Other information may also be collected through the interface, as described below.

[0113] The user may be indicated as ready to proceed by user input or a prompt via the user control / input interface 331. When the user is ready to proceed, the processor 310 activates the sensor 340 as instructed by the processor control instruction 352. The sensor 340 is preferably a camera facing forward on the mobile device and located on the same side of the mobile device as the display interface 320. The camera is typically configured to capture a two-dimensional image. Mobile devices that capture two-dimensional images are ubiquitous. This technology leverages this ubiquity to avoid the burden on the user of acquiring specialized equipment.

[0114] Almost simultaneously with the activation of the sensor / camera 340, the processor 310 presents the capture display on the display interface 320 as instructed by the application. The capture display may include a live action preview of the camera, a reference feature, a target setting box, and one or more status indicators, or any combination thereof. In this example, the reference feature is displayed in the center of the display interface and has a width corresponding to the width of the display interface 320. The vertical position of the reference feature may be such that the upper edge of the reference feature is adjacent to the top edge of the display interface 320 or the lower edge of the reference feature is adjacent to the bottom edge of the display interface 320. Part of the display interface 320 displays the camera live action preview 324. The camera live action preview 324 typically shows the user's face feature captured in real time by the sensor / camera 340 (when the user is in the correct position and orientation).

[0115] The reference feature is a feature known to the (predetermined) computing device 230 and provides a reference frame that enables the processor 310 to scale the captured image. The reference feature may preferably be a feature other than the user's face or anatomical features. Thus, during the image processing phase, the reference feature assists the processor 310 in determining when specific alignment conditions are met (e.g., during the pre-capture phase). The reference feature may be a Quick Response (QR) code® or a known model or marker and may provide the processor 310 with specific information (e.g., scaling information, orientation, and / or any other desired information that can be optionally determined from the structure of the QR code®). The QR code® may have a square or rectangular shape. When displayed on the display interface 320, the reference feature has predetermined dimensions (e.g., in millimeters or centimeters). Dimensional values ​​may be coded into the application and communicated to the processor 310 at an appropriate time. The actual dimensions of the reference feature 326 may vary depending on the computing device. In some versions, the application may be configured to be specific to the computing device model; that is, the dimensions of the reference feature 326 are known when displayed on a particular model. However, in other embodiments, the application may instruct the processor 310 to obtain specific information (e.g., display size and / or zoom characteristics) from the device 230. As a result, the processor 310 can calculate the real-world / actual dimensions of the reference feature as it would appear on the display interface 320 via scaling. Nevertheless, the actual dimensions of the reference feature as it would appear on the display interface 320 of such a computing device are often known before post-capture image processing.

[0116] Along with the reference features, the target setting box may be displayed on the display interface 320. The target setting box allows the user to align specific components within the capture display 322 in the target setting box. This is desirable for successful image capture.

[0117] The status indicator provides the user with information about the process status. As a result, it helps the user ensure that major adjustments to the sensor / camera positioning are made before image capture is complete.

[0118] Therefore, when the user holds the display interface 320 parallel to the face feature to be measured and places the user display interface 320 against a mirror or other reflective surface, the reference feature is displayed enlarged and overlaid on the real-time image seen by the camera / sensor 340 and reflected by the mirror. This reference feature can be fixed in the upper neighborhood of the display interface 320. Because this reference feature is displayed enlarged in this way, at least partially, the sensor 340 can clearly see the reference feature and the processor 310 can easily identify the feature. In addition, since the reference feature can be overlaid on the live view of the user's face, it helps to avoid user confusion.

[0119] Instructions from the processor 310 to the user may be given via the display interface 320 (through the speaker of the computing device 230) as audible instructions to position the position display interface 320 within the plane of the face feature to be measured, or they may be given prior to the tutorial. For example, the user may position the display interface 320 facing forward and The display interface 320 can be commanded to be positioned below the user's chin, opposite the user's chin, or adjacent to the user's chin, aligned with the specific facial feature being measured. For example, the display interface 320 can be positioned while maintaining surface alignment with the serion and suprementon. If the final captured image is two-dimensional, surface alignment helps ensure that the scale of the reference feature 326 is applied equally to the facial feature measurement. In this regard, the distance between the mirror and both the user's facial feature and the display is approximately the same.

[0120] Once the user is positioned in front of the mirror and the display interface 320, which includes a reference feature, is roughly positioned while maintaining plane alignment with the face feature to be measured, the processor 310 checks for specific conditions to help ensure sufficient alignment. One exemplary condition that may be established by the application is, as described above, that the entire reference feature must be detected within the target setting box 328 for the process to proceed. If the processor 310 detects that the entire reference feature is not positioned within the target setting box, the processor 310 may prohibit or delay image capture. The user can then maintain planarity by moving their face along with the display interface 320 until the reference feature is positioned within the target setting box, as shown in the live action preview. This helps to ensure optimal alignment of the face feature and the display interface 320 with respect to the mirror for image capture.

[0121] Once the processor 310 has detected the entire reference feature within the target setting box, the processor 310 may read the computing device's IMU 342 for device tilt angle detection. The IMU 342 may include, for example, an accelerometer or a gyroscope. Thus, the processor 310 may be able to verify that the device tilt is within an appropriate range by performing an evaluation of the device tilt (for example, by comparison with one or more thresholds). For example, if it is determined that the computing device 230 and (the resulting) display interface 320 and user face features are tilted in any direction within approximately ±5 degrees, the process may proceed to the capture phase. In other embodiments, the tilt angle to be counted may be within approximately ±10 degrees, ±7 degrees, ±3 degrees, or ±1 degree. If excessive tilt is detected, a warning message may be displayed or an audio alert may be provided to correct the undesirable tilt. This is particularly useful in assisting the user in avoiding or reducing excessive tilt (especially in the front-to-back direction), as failure to correct excessive tilt can lead to measurement errors due to an inappropriate aspect ratio of the captured reference image.

[0122] Once the alignment is determined by the processor 310 as controlled by the application, the processor 310 proceeds to the capture phase. The capture phase is preferably performed automatically when the alignment parameters and other optional preconditions are met. However, in some embodiments, the user may initiate the capture in response to a prompt to do so.

[0123] When image capture begins, the processor 310 captures multiple images (preferably more than one) via the sensor 340. For example, the number of images captured by the processor 310 via the sensor 340 may be approximately 5 to 20, 10 to 20, or 10 to 15. The number of images captured may be based on time. In other words, the number of images captured may be based on the number of images of a predetermined resolution that can be captured by the sensor 340 at a predetermined time interval. For example, if the number of images that the sensor 340 can capture per second at a predetermined resolution is 40, If the predetermined time interval during capture is 1 second, the sensor 340 captures 40 images for processing by the processor 310. The number of images may be defined by the user, determined by the server 210 based on artificial intelligence or machine learning of the detected environmental conditions, or based on the intended accuracy target. For example, if high accuracy is required, a larger number of captured images may be needed. While it is preferable to capture multiple images for processing, a single image is also possible and can be useful for enabling high-precision measurements. However, using more than one image allows for average measurement. As a result, errors / mismatches may be reduced and accuracy may be improved. These images may be stored by the processor 310 in the stored data 354 of the memory / data storage device 350 for post-capture processing.

[0124] After image capture is complete, the image is processed by the processor 310 to detect or identify facial features / landmarks and measure the distance between them. The resulting measurements can be used to recommend an appropriate patient interface size. Alternatively, this processing may be performed by the server 210 that receives the transmitted captured image and / or on the user's computing device (e.g., a smartphone). The processing may also be performed by a combination of the processor 310 and the server 210. In one example, the recommended patient interface size may be based primarily on the user's nose width. In another example, the recommended patient interface size may be based on the user's mouth and / or nose dimensions.

[0125] A processor 310, controlled by the application, retrieves one or more captured images from the stored data 354. These images are then extracted by the processor 310 for the identification of each pixel, including the two-dimensional captured images. The processor 310 then detects specific pre-specified face features within the pixel arrangement configuration.

[0126] Detection by the processor 310 may be performed using edge detection (e.g., Canny, Prewitt, Sobel, or Robert edge detection). These edge detection techniques / algorithms help identify the location of specific facial features within a pixel arrangement configuration that corresponds to the actual facial features of a patient presented for image capture. For example, according to the edge detection technique, the user's face in the image may first be identified, and the location of pixels in the image corresponding to specific facial features (e.g., eyes and their boundaries, mouth and its corners, left and right wings, selions, splamentons, glabella, and left and right nasolabial folds) may also be identified. Next, the processor 310 may mark, tag, or save the specific pixel location(s) of each of these facial features. Alternatively, if such detection by the processor 310 / server 210 is unsuccessful, pre-specified facial features may be manually detected, marked, tagged, or saved by a human operator. These captured images are visually accessible through the user interface of the processor 310 / server 210.

[0127] Once the pixel coordinates of these facial features are identified, the application controls the processor 310 to measure the pixel distance between specific identified features. For example, this distance may be determined primarily by the number of pixels in each feature and may include scaling. For instance, the pixel height of a face may be determined by determining the pixel width of the nose and / or the pixel width between the selion and splamenton by measuring between the left and right wings. Other examples include the pixel distance between the eyes, the pixel distance between the corners of the mouth, and the pixel distance between the left and right nasolabial folds, which may provide further measurement data for specific structures such as the mouth. Further distances between facial features may be measured. In this example, the dimensions of specific faces are used for the patient interface selection process.

[0128] After pixel measurements of pre-specified facial features are obtained, anthropometric correction factors (one or more) may be applied to these measurements. It should be understood that the application of these correction factors may occur before or after the application of scaling factors, as described below. The anthropometric correction factors allow for correction of errors that may occur in the automated process (which may occur consistently across patients). In other words, without correction factors, using the automated process alone may yield consistent results across patients, but could lead to a certain amount of incorrectly sized patient interfaces. Correction factors can be empirically extracted from population studies, and their formation helps to obtain results closer to true measurements, reducing or eliminating incorrect sizing. As measurement and sizing data for each patient are communicated from each computing device to the server 210, the accuracy of these correction factors can be refined or improved over time. Further processing of such data at the server 210 may further improve the correction factors. The anthropometric correction factors may also vary depending on the form of the patient interface. For example, the correction factor for a particular patient looking for an FFM may differ from the correction factor for a patient looking for a nasal mask. Such a correction factor may be derived from tracking mask purchases (e.g., by monitoring mask returns and determining the size difference between replacement masks and returned masks).

[0129] To apply facial feature measurements to sizing a patient interface, these measurements may be scaled from pixel units to other values ​​that actually reflect the distances between patient facial features, such as those presented for image capture, regardless of whether they are corrected or uncorrected by anthropometric correction factors. A reference feature may be used to obtain the scaling value(s). Thus, the processor 310 also determines the dimensions of the reference feature (e.g., measurement of the pixel width and / or pixel height (x and y) of the entire reference feature (e.g., pixel count)). More detailed measurements of the pixel dimensions of a number of rectangles / points, including the QR code® reference feature and / or the pixel area occupied by the reference feature and its components, may also be determined. Thus, each rectangle or point of the QR code® reference feature may be measured in pixel units to determine a scaling factor based on the pixel measurement of each point, and then the average of all measured rectangles or points may be calculated, resulting in improved accuracy of the scaling factor compared to a single measurement of the overall size of the QR code® reference feature. However, regardless of how the reference features are measured, it should be understood that these measurements can be used to scale the pixel measurements of the reference features to the corresponding reference features of known dimensions.

[0130] After the reference feature measurements are performed by the processor 310, a scaling factor is calculated by the processor 310, as controlled by the application. The pixel measurements of the reference feature are related to the known corresponding dimensions of the reference feature (e.g., the reference feature 326, as displayed by the display interface 320 for image capture, for obtaining the transformation or scaling factor). Such a scaling factor may take the form of length / pixel or area / pixel A2. In other words, a known dimension(s) may be divided by the corresponding pixel measurement(s) (e.g., count(s)).

[0131] Next, the processor 310 applies the scaling factor to the face feature measurements (pixel counts) to convert the measurements from pixel units to other units so that they reflect the distances between the actual face features of the patient that are suitable for mask sizing. This conversion typically involves, for example, multiplying the scaling factor by the pixel counts of the distance(s) between the face features relevant to mask sizing.

[0132] These measurement and calculation steps for both face features and base features continue until the face feature measurements for each image in the set are scaled and / or corrected. Repeat step P for each captured image.

[0133] Next, the collected and scaled measurements of a set of images are arbitrarily averaged by the processor 310 to obtain final measurements of the anatomical structure of the patient's face. Such measurements may reflect the distances between the patient's facial features.

[0134] In the comparison and output phase, the results from the post-capture image processing phase may be output (displayed) directly to the subject, or they may be compared with the data recording(s) to obtain an automated recommendation for the patient interface size.

[0135] After all measurements have been determined, the results (e.g., the average) may be displayed to the user via the display interface 320 by the processor 310. In one embodiment, this may terminate the automated process. The user / patient may record these measurements for further use by the user.

[0136] Alternatively, the final measurements may be automatically or by user command transferred from the computing device 230 to the server 210 via the communication network 220. The server 210 or a person on the server side may perform further processing and analysis to determine an appropriate patient interface and patient interface size.

[0137] In a further embodiment, the final face feature measurement, which reflects the distances between the patient's actual face features, is compared by the processor 310 with, for example, patient interface size data during data recording. This data recording may be part of an application for automated measurement of face features and sizing of the patient interface. An example of this data recording is a lookup table accessible from the processor 310, which may contain patient interface sizes corresponding to distances / values ​​of a certain range of face features. Multiple tables may be included in the data recording, many of which may correspond to specific forms of patient interfaces and / or specific models of patient interfaces provided by manufacturers.

[0138] An exemplary process for selecting a patient interface involves identifying key landmarks from facial images captured by the method described above. In this example, the initial correlation to a potential interface includes facial landmarks (e.g., facial height, nasal width, and nasal depth, as indicated by lines 3010, 3020, and 3030 in Figures 3A and 3B). These three facial landmark measurements are collected by the application to assist in selecting a suitable mask size, for example, through a lookup table or the table described above.

[0139] As described above, after mask selection or detailed manufacturing of a mask for a particular patient, operational data for each RPT may be collected for a large population of patients. This may include usage data based on when each patient operates the RPT. Thus, compliance data (e.g., the length and frequency of time a patient uses the RPT over a given period) can be determined from the collected operational data. Leakage data can be determined from the operational data (e.g., analysis of flow rate or pressure data). Mask switching data can be derived using acoustic signal analysis to determine if a mask has been switched. The RPT may be operable to determine the mask type based on a combination of an internal or external voice sensor (e.g., microphone 4278 in Figure 4B) and cepstrum analysis as described above. Alternatively, for older masks, the operational data can be used to determine the mask type through correlation between collected acoustic data and the acoustic signature of known masks.

[0140] In this example, the collection of patient input feedback data may be done via a user application running on a computing device 230 or a smartphone 234. The user application may be part of a user application 360 that instructs the user to obtain facial landmark features or a separate application. This may also include subjective data obtained through a questionnaire. This questionnaire may include: questions for collecting data on comfort preferences, whether the patient is a mouth breather or a nasal breather (e.g., "Is your mouth dry when you wake up?"), and preferences for mask materials (e.g., silicone, foam, fabric, gel). For example, the collection of patient input may be done through the patient's responses via the user application to subjective questions related to the comfort of the patient interface. Other questions may relate to relevant user behavior (e.g., sleep characteristics). For example, questions that may be included in the subjective questions are: Is your mouth dry when you wake up?, Are you a mouth breather?, or What are your comfort preferences? Examples of such sleep information include sleep duration, the user's sleep pattern, and external factors (e.g., temperature, stress factors). Subjective data may be simple numerical ratings of comfort or more detailed responses. Such subjective data may be collected from a graphical interface. For example, to select leaks from the interface, the user can be asked to select a graphic portion of the selected interface, and this data can be collected. The collected patient input data can be assigned to the patient database 260 in Figure 6. Subjective input data from patients can be used as feedback on mask design and as features to be referenced in the future. Other subjective data may be collected in relation to the patient's psychological safety. For example, input can be collected by asking questions (e.g., whether the patient feels claustrophobic with a particular mask, or whether the patient feels psychologically comfortable when wearing the mask with a roommate).

[0141] Other data sources may collect data outside of RPT usage that can be correlated with specific masks. Examples of such data include patient demographic data (e.g., age, sex, or location); and AHI severity, which indicates the level of sleep apnea experienced by the patient. Other data could include the prescribed pressure setting for new patients using the RPT device.

[0142] After mask selection, system 200 continues to collect behavioral data from RPT250. The collected data is added to databases 260 and 270. Feedback from new patients can be used to refine recommendations for improving mask selection. For example, if behavioral data determines that the recommended mask has a high level of leakage, a different type of mask can be recommended to the patient. Through the feedback loop, the selection algorithm can be refined to learn specific facial geometry characteristics that may be optimal for a particular mask. This correlation can be used to refine mask recommendations for new patients with that facial geometry. Thus, the collected data and correlated mask type data can enable further updates to the criteria for mask selection and design. Therefore, this system can provide further insights for improving mask selection or design for patients.

[0143] In addition to mask selection, this system may also enable analysis of mask selection in relation to the effectiveness and adherence to respiratory therapy. Such additional data will allow for data-driven optimization of respiratory therapy through a feedback loop.

[0144] The application of machine learning may enable the provision of correlations between mask types / characteristics and improved compliance with respiratory therapy. These correlations can be used for selecting or designing characteristics for novel mask designs. Such machine learning may be performed by server 210. The mask analysis algorithm is trained based on favorable operating results and outputs (e.g., patient demographics, mask size and type, and subjective data collected from patients). Machine learning can be performed using data sets. Using machine learning, it may be possible to discover correlations between desired mask characteristics and predictive inputs (e.g., facial dimensions, patient demographics, behavioral data from RPT devices, and environmental conditions). Techniques such as neural networks, clustering, or conventional regression techniques may be employed in machine learning. Using test data, different types of machine learning algorithms can be tested to determine which has the best accuracy in relation to correlation prediction.

[0145] The model for selecting the optimal interface can be continuously updated with new input data from the system shown in Figure 5. Therefore, increasing the use of the analysis platform can lead to improved model accuracy.

[0146] As described above, one part of the system in Figure 5 relates to recommending interfaces for patients using RPT. A second function of the system is the feedback data collection process. This process collects data for future mask design or adjustment. After a recommended mask is provided to the patient and the patient uses that mask for a certain period (e.g., 2 days, 2 weeks, or another period), the system may monitor RPT usage and collect other data. Based on this collected data, if mask performance is not at a high level (determined from unfavorable data indicating leakage, reduced compliance, or unsatisfactory feedback), the system may re-evaluate the mask selection and update the database 260 and machine learning algorithms with the patient's results. The system may then recommend a new mask that is suitable for the newly collected data. For example, if a relatively high leakage flow rate is determined from acoustic signature or other sensor data, the patient's mouth may be open during REM sleep. In that case, it may indicate that a different type of interface (e.g., a full-face mask) is needed (instead of the initially selected nasal-only or smaller full-face mask).

[0147] This system may also be able to adjust recommendations in response to satisfactory follow-up data. For example, if behavioral data indicates no leakage from the selected full-face mask, the routine may recommend trying a smaller mask. To maximize compliance, trade-offs between style, material, variation, and correlation with patient preference may be used, and follow-up recommendations may be possible. Trade-offs for individual patients may be determined through an input tree displayed to the patient from the application. For example, if a patient indicates skin irritation as a problem from the potential problems menu, an illustration of the location of potential irritation on the facial image may be displayed to collect data from the patient as specific irritation sites. This specific data may allow for improved correlation to the optimal mask for that particular patient.

[0148] This process enables the collection of feedback data and its correlation with facial feature data, providing mask designers with data for other mask designs. Mask-related feedback information may be collected as part of another application run by application 360 or a computing device (e.g., computing device 230 or mobile device 234 in Figure 5) that collects facial data.

[0149] Application 360 may collect initial patient information and provide security measures to protect the data (e.g., password settings). After the patient sets up the application and correlates Application 360 with the identification information of a specific patient, the application may collect feedback data.

[0150] If facial data has already been collected for the patient, Application 360 will then... The process proceeds to data collection. If no previously collected facial data exists for the patient, application 360 provides the patient with options for facial data collection. In Figure 7A, a facial image 710 is displayed on the application interface 700. The patient may capture a facial image 710 in the same manner as the facial scan process described above for mask selection. After the facial image 710 is displayed, a facial mesh may be generated. The second interface 720, shown in Figure 7B, displays the facial mesh 712 generated from the facial image 710. Next, facial data may be derived from the facial mesh 710 and stored, and sent to the server 210 for storage in the database 260 in Figure 5.

[0151] Application 360 collects all relevant data from patients through other displayed interfaces for characterization of mask design. The sleep data collection interface 730, shown in Figure 7C, enables the collection of subjective patient data related to sleep quality. This data can be collected and correlated with objective sleep data or relevant behavioral data collected by the RPT described above. Interface 730 includes a question 732 related to sleep position. Interface 730 includes options for the user to select (e.g., back selection 734, abdominal selection 736, and side-lying selection 738). If the user is unsure of their choice, they can select the "I don't know" option 740. Thus, Interface 730 collects sleep position data correlated with a specific user.

[0152] Another sleep interface 750, shown in Figure 7D, is displayed to collect sleep type data. Interface 750 includes a question 752 about sleep type. Interface 750 includes choices for the user, including the options “light sleep” 754, “moderate sleep depth” 756, and “deep sleep” 758. If the user is unsure of their choice, they may select the “I don't know” option 760. Thus, Interface 750 collects sleep type data correlated with a specific user.

[0153] Application 360 also collects information about the mask currently being used by the patient. Figure 8A shows interface 800, which includes the selection of a nasal-only mask or a nasal and oral mask. Thus, the user selects either a nasal-only mask (selection 802) or a nasal and oral mask (selection 804). In this example, selections 802 and 804 include useful illustrations to describe the mask types. This selection determines further interfaces specific to the manufacturer and mask model. After the patient has made their selection, the mask brand selection interface 810 shown in Figure 8B is displayed, allowing the patient to select a mask brand. Interface 810 includes selections for manufacturer A (selection 812), manufacturer B (selection 814), manufacturer C (selection 816), and manufacturer D (selection 818). The application provides access to all mask models for each of manufacturers A-D, and this information is provided to subsequent interfaces, allowing the selection of a specific mask from the selected manufacturer A-D.

[0154] Figure 8C shows interface 820 for selecting a mask model. The selection in interface 820 is determined by the manufacturer selected in interface 810 in Figure 8B. Interface 820 lists the selections for each applicable model provided by the manufacturer (e.g., selections 822, 824, 826, and 828).

[0155] After selecting a mask model, application 360 displays an interface 830 for determining the mask cushion size, as shown in Figure 8D. Interface 830 includes an illustrated image 832 of the mask model selected from interface 820 in Figure 8C. Illustrated image 832 shows the user where to determine the mask size. Face 830 includes small size option 834, medium size option 836, and large size option 838.

[0156] Alternatively, application 360 may be programmed to perform mask identification by analyzing the obtained illustrated image of the mask and comparing this image with identification data associated with a known mask model. The instruction interface 900 shown in Figure 9A provides options for obtaining automatic visual identification of the mask. The photographic interface 910 shown in Figure 9B enables the capture of an image 912 of the mask. The photographic interface 920 shown in Figure 9C shows the captured image 912 of the mask.

[0157] Application 360 also includes an interface for collecting data to determine which mask is being used. The short-term use interface 930 in Figure 9D determines whether a mask is for one-time use by displaying a question 932 about mask usage in the last 30 days. Interface 930 may display a diagram of the mask model previously selected by the user. The user may select "Yes" 934 or no selection 936 (indicating that another mask is being used). Application 360 may also provide input for collecting data about previous masks.

[0158] The long-term use interface 940 shown in Figure 9E determines whether the mask is unique by displaying a question 942 related to mask usage. Interface 940 may display an illustration of the mask model previously selected by the user. The user may select "Yes" 944 or no selection 946 (indicating that other masks are being used).

[0159] Application 360 can also determine comfort feedback data from the patient. Interface 1000, shown in Figure 10A, determines whether there is any discomfort from the mask. Question 1002 is displayed, asking the patient if they feel any discomfort from the mask. The patient can choose either option 1004 ("No") or option 1006 ("Yes").

[0160] If the patient selects option 1006 "yes", the visual discomfort identification interface 1010 is displayed as shown in Figure 10B. Interface 1010 displays an image 1020 of the face features. Image 1020 may be selected from a comprehensive facial image depending on the patient's gender or other characteristics. Alternatively, image 1020 may be an individual's facial image, in which case it may be stored on a portable computing device (if acquired by application 320) or accessed from a database of previously acquired facial images of the patient. A mask-shaped location grid 1022 is overlaid on the patient's facial image 1020. A selection list 1024 is displayed. The selection list 1024 describes, for example, the following five potential discomfort areas: a) the bridge of the nose, b) the upper part of the nose, c) the lower part / corner of the nose, d) the sides / corner of the mouth, and e) the chin / lower lip. The location grid 1022 includes lines defining five regions 1030, 1032, 1034, 1036, and 1038 corresponding to regions described in List 1024, in order to assist the patient in identifying areas of discomfort. These regions 1030, 1032, 1034, 1036, and 1038 each represent a contact area between the face and the mask. The user may select one or more areas of discomfort from List 1024. The list of discomforts and the corresponding regions in the grid 1022 are then highlighted. In this example, the location mask is specific to a full-face mask, but other types of masks (e.g., cradle-type masks) may have different areas of discomfort that are specific to the type of mask, along with different location grids 1022.

[0161] Figure 10C shows an example of interface 1010 when the patient selects an area of ​​discomfort. In this example, the patient selects the upper nasal area from list 1024. Therefore, this selection The selected option is highlighted. Region 1032 of grid 1022, which indicates the area above the nose, is also highlighted, thereby indicating the area of ​​discomfort relative to the face image 1020.

[0162] Application 360 can also determine leakage feedback data from the patient. Interface 1050, shown in Figure 10D, determines whether there is any air leakage at all in the seal between the mask and the face. Question 1052 is displayed to ask the patient if they have experienced any leakage from the mask. The patient can select either the "No" option 1054 or the "Yes" option 1056.

[0163] If the patient selects option 1056 "yes", the visual discomfort identification interface 1060 is displayed as shown in Figure 10E. The interface 1060 displays the patient's image 1070. The image 1070 may be stored on a portable computing device if acquired by application 320, or it may be accessed from a database of previously acquired facial images of the patient. A mask shape location grid 1072 is overlaid on the patient's facial image 1070. A selection list 1074 is displayed describing the following five potential air leakage areas: a) nasal bridge, b) upper nasal side, c) lower nasal side / corner, d) sides / corner of the mouth, and e) chin / lower lip. Location grid 1072 helps the patient discover leakage between the mask and their face in areas 1080, 1082, 1084, 1086, and 1088 (indicating areas of contact between the face and the mask) by including lines defining the five corresponding areas 1080, 1082, 1084, 1086, and 1088 described above in list 1074. The user may select one or more discomfort areas from list 1074. The discomfort list is then highlighted along with the corresponding areas in grid 1072.

[0164] Figure 10F shows an example of interface 1060 when a patient selects an area of ​​discomfort. In this example, the patient selects "supranu nasal" from list 1074. Therefore, this selection is highlighted. Region 1082 of grid 1072 is also highlighted, so this area of ​​discomfort is displayed relative to the face image 1070.

[0165] The interface 1100 shown in Figure 11A collects subjective feedback data from the patient about the effects of air leaks. Interface 1100 is displayed when the user selects the "yes" option 1056 from interface 1050 in Figure 10E. A question 1102 included in interface 1100 asks the patient to express the level of discomfort caused by air leaks using a numerical scale input. Interface 1100 includes a scale 1104 ranging from 0 (not bothersome) to 10 (very bothersome). The patient may select a slider 1106. The slider 1106 displays a numerical input from the scale, as shown in the image of interface 1100'. Other similar interfaces may be provided for other questions (e.g., questions related to discomfort in a specific area or region on the face).

[0166] The interface 1150, shown in Figure 11B, collects subjective feedback data from patients regarding their satisfaction with their current mask. A question 1152 included in the interface 1150 asks patients to numerically rate a particular mask model to determine if they would recommend it. The interface 1150 includes a scale 1154 ranging from 0 (unlikely) to 10 (very likely). Patients may select a slider 1156, which displays numerical input from the scale, as shown in the image of interface 1150'.

[0167] Figure 11C shows an exemplary interface 1160 that may be displayed for collecting patient demographic data. Interface 1160 includes an age selection field 1162 and a gender selection field. This includes field 1164 and race field 1166. Therefore, patients can input their age, sex, and race data using fields 1162, 1164, and 1166. This data may be collected to support the analysis of mask designs related to patient demographics.

[0168] Figure 11D is an illustrative schematic diagram inserted to collect information about discomfort and air leakage depending on the selection of mask type in the generated interface, and is used for determining discomfort, for example, as shown in Figure 10B, or for determining leakage, for example, as shown in Figure 10C. The series of five different overlay illustrations 1170, 1172, 1174, 1776, and 1178 shown in Figure 11D represent different mask shapes. For example, overlay illustrations 1170, 1176, and 1178 represent different types of nose-only masks. Overlay illustrations 1172 and 1174 represent different types of masks and nose masks. Based on the user's mask selection, the appropriate illustrations 1170, 1172, 1174, 1776, and 1178 are inserted.

[0169] Figure 12 shows a dendrogram 1200 of data collected through the exemplary application 360 described herein. Input 1210 requests the user to select the option that best describes their situation. Input 1210 may include users 1212 of vendor-manufactured masks, users 1214 of third-party-manufactured masks, and users 1216 who currently do not have a mask. If the user identifies as either a vendor-manufactured or third-party-manufactured mask user, input 1220 determines whether the mask covers the nose or both nose and mouth. Next, data related to the sleep position 1222 is collected during data collection. If the user indicates that they do not currently have a mask in use, the routine directly collects data related to the sleep position 1222. Input 1224 collects data on whether the user has difficulty raising both arms above their head. Input 1226 determines whether the user has difficulty breathing through their nose. Input 1228 determines whether the user is experiencing nasal dryness. Input 1230 determines whether the user has claustrophobia. Input 1232 determines whether the user uses facial cream at night.

[0170] Since different genders result in different facial features, input 1234 determines the user's gender. The user can select male or female. If the user declines to answer, input 1236 determines the user's gender at birth. The user can answer male, female, or decline to answer. If the user answers male to input 1234 or 1236, input 1238 determines whether the user has hair on their face. The routine then presents a set of general gender-related questions, including: input 1240 (related to facial or skin sensitivity), input 1242 (whether the user wears glasses), input 1244 (whether the user has a bedmate), input 1246 (whether the user cares about how they look when wearing mask 1248), and input 1250 (whether the user has difficulty falling asleep). As described above, the collected data can be used to recommend or select a mask that is appropriate for the user. The collected data can also be categorized or correlated with other user-specific data, which may provide guidelines for designing novel masks relevant to specific patient subgroups or general patient populations.

[0171] For example, patient input data may indicate mask leakage in a specific area (e.g., the upper part of the nose). This leakage can be confirmed through operational data from the RPT device. This data can then be correlated with facial data related to the upper part of the nose. Analysis can be performed to modify the mask dimensions by lengthening the mask edge that interfaces with the upper part of the nose to minimize leakage. As another example, patient input data may indicate that the patient feels discomfort when the mask is placed on the upper part of the nose. This data can then be correlated with facial data related to the upper part of the nose. This data can be correlated with relevant facial data. Analysis may allow for changes to mask dimensions, such as shortening the mask edge interface with the upper part of the nose to minimize leakage. Of course, such data may be provided to healthcare providers to recommend different sizes or types of masks to reduce discomfort or leakage. Alternatively, this data may be used to modify a patient's initially chosen mask to one that is individually tailored to the patient.

[0172] The patient data collected from the above application and further data (e.g., operational data from the RPT device 250 in Figure 5) can be reused or processed in data processing steps to assist in designing an improved interface for the user. A scanned mirror image of the face (surface topography) can be used to generate interfaces such as masks. However, such patient interfaces may not always be ideal, because certain areas of the sealing region on the face may require different levels of sealing force, be more sensitive to high headgear pressure, or have a higher potential for leakage due to the complex geometry of the face. Taking these performance and comfort-related details into consideration in further processing leads to a more optimal mask design.

[0173] In one example, if direct measurement of the deformed geometry is not possible or the deformed geometry is unavailable, a representation of the deformed geometry can be attempted and provided using relaxed state geometry data obtained from relaxed state data collection. Simulation software can be used in post-processing of relaxed data to simulate the deformed state. A non-limiting example of suitable simulation software is ANSYS, which converts state geometry data from "relaxed" to "deformed" in relaxed data.

[0174] By acquiring further facial images, it may be possible to determine the relaxed and deformed states of the facial geometry. By obtaining both the relaxed and deformed states of the geometry data, it may be possible to calculate approximate pressure values ​​(in terms of the pressures generated in the simulation) between the patient interface contact area and the patient's face using finite element software (e.g., ANSYS). Alternatively, pressure data may be collected separately via pressure mapping. Therefore, it may be possible to estimate the deformed geometry and generated pressure from the collection of relaxed state data.

[0175] Measurement data, whether geometric or pressure data, can enable the identification and treatment of areas or features on a patient's face that require special consideration during specific feature processing. Optionally, data from any combination of measurement sources can yield a comprehensive model containing both geometric and pressure datasets, further achieving the objective of providing comfort, effectiveness, and compliance in design.

[0176] The face is not a static surface; rather, it adapts and changes in response to external conditions (e.g., forces from the patient interface, air pressure on the face, and gravity). Taking these interactions into account provides the additional benefit of providing the patient with optimal seal and comfort. Three examples illustrate this process.

[0177] Firstly, since users wearing these patient interfaces experience CPAP pressure, this knowledge can be used to improve the comfort and sealing of the patient interface. By using simulation software in conjunction with known properties (e.g., soft tissue properties or elastic modulus), it may be possible to help predict facial surface deformation at specific air pressures within the patient interface.

[0178] For populations associated with any of the following facial locations, tissue characteristics may be known and can be collected: supragnathia, glabella, nasal root, nasal tip, philtrum, upper lip margin, lower lip margin, labial sulcus, mental prominence, mandibular base, anterior prominence, supraorbital, lateral glabella, lateral nose, infraorbital, lower cheek, lateral nostril, nasolabial ridge, supra canina, sub canina, ant. mental tubercle, lateral orbital midpoint, supragnate, zygomatic bone, lateral, supra-M2, midmasset muscle, occlusal line, sub-M2, gonion, and intermediate mandibular angle.

[0179] For example, soft tissue thickness is known from an anthropometric database for at least one of the following facial features: e.g., nasian, nasal tip, philtrum, labial sulcus, chin prominence, infraorbital, lower cheek, lateral nostrils, nasolabial ridge, supra canina, and subcanina. Specific locations (e.g., infraorbital, lower cheek, lateral nostrils, nasolabial ridge, supra canina, and subcanina) are located on both sides of the face.

[0180] Known tissue properties at any one or more of these locations may include any one or more of the following: soft tissue thickness, force-based modulus data, deflection, modulus and thickness, soft tissue thickness ratio information, and body mass index (BMI).

[0181] Secondly, the skin surface on the patient's face undergoes significant deformation when the CPAP patient interface is strapped onto the face. Using initial 3D measurements of the head and facial geometry in a relaxed state, it may be possible to predict surface changes using the aforementioned skin / soft tissue properties and simulation software. Such techniques are iterative optimization processes and can be used in conjunction with the design process.

[0182] Thirdly, when in a sleeping position, the skin surface may shift due to gravity. Predicting these changes using knowledge of skin and soft tissue properties, as well as simulation software, can help design more robust, comfortable, and high-performance patient interfaces for various sleeping positions. Data related to the geometric changes from upright to supine can be collected and utilized from one or more target areas of the face (e.g., nasian, nasal tip, philtrum, labial sulcus, infraorbital, lateral nostrils, nasolabial ridge, supra canina, and sub canina).

[0183] Finite element analysis (FEA) software (e.g., ANSYS) can be used to calculate approximate pressure values ​​between an interface contact area and a user's face. In one embodiment, possible inputs include face geometry in "relaxed" and "deformed" states, and properties of various locations on the face (e.g., measured elastic modulus, or substructures (with known properties such as stiffness)). Using such inputs, it may be possible to construct a finite element (FE) model of the face, which can then be used to predict one or more responses of the face to an input (e.g., deformation or load). For example, the FE model of the face can be used to predict the face shape at a given pressure level (e.g., 15 cmH2O) in the patient interface. In some embodiments, the FE model may further include a patient interface model or a part thereof (e.g., a cushion), which includes cushion geometry and its properties (e.g., mechanical properties such as the elastic modulus). Such models can predict cushion deformation (for example, when an internal load is applied to the cushion due to CPAP pressure) and the resulting interaction between the cushion and the face (e.g., load / pressure between them, facial deformation). More specifically, by using the distance changes at each point between the relaxed and deformed states, along with the corresponding tissue properties, it is possible to predict the pressure generated at a given point (e.g., the cheekbone).

[0184] Certain areas or features on the face may require special consideration. Identifying and adjusting these features can improve the overall comfort of the interface. Based on the data acquisition and estimation techniques described above, appropriate features can be applied to the custom patient interface.

[0185] In addition to the pressure sensitivity, pressure compliance, shear sensitivity, and shear compliance indicators described above, special consideration may be given to facial hair, hairstyle, and extreme facial landmarks (e.g., prominent nasal bridge, sunken cheeks). As used herein, “shear sensitivity” refers to the shear sensation felt by the patient, and “shear compliance” refers to the level to which the patient’s skin moves smoothly with or is compliant with shear.

[0186] The feedback data collection routine shown in Figure 13 may be executed over a specific period of time (one or more) after the patient's initial selection of the interface. For example, a follow-up routine may be executed over the first two days of using the interface and RPT. The flowchart in Figure 13 shows an exemplary machine-readable instruction to collect and analyze feedback data to select interface characteristics for respiratory pressure therapy optimized for different patient types. In this example, the machine-readable instruction includes an algorithm executed by: (a) a processor, (b) a controller, and / or (c) one or more other suitable processing devices (one or more). The algorithm may be embedded in software stored on a tangible medium (e.g., flash memory, CD-ROM, floppy disk, hard drive, digital video (versatile) disk (DVD) or other memory device). However, those skilled in the art will understand that the entire algorithm and / or parts thereof may be executed by devices other than processors and / or embedded in firmware or dedicated hardware in a well-known manner (for example, this may be done by application-specific integrated circuits [ASICs], programmable logic devices [PLDs], field-programmable logic devices [FPLDs], field-programmable gate arrays [FPGAs], or discrete logic). For example, some or all of the interface components may be executed by software, hardware, and / or firmware. Also, some or all of the machine-readable instructions shown in the flowchart may be executed manually. Furthermore, although the exemplary algorithm is described with reference to the flowchart shown in Figure 13, those skilled in the art will readily understand that many other methods may be used to execute the exemplary machine-readable instructions. For example, the order in which blocks are executed may be changed, and / or some of the described blocks may be modified, removed, or combined.

[0187] As described below, the routine in Figure 13 can provide recommendations for design changes to different interface characteristics (e.g., facial regions and contact regions). This data can enable continuous updating of an exemplary machine learning-driven correlation engine.

[0188] In the routine, it is first determined whether facial data for the patient has been collected (1310). If facial data has not been collected, the routine application 360 is launched and a request is made to perform a facial scan of the user using the mobile device running the aforementioned application (for example, mobile device 234 (1312) in Figure 5).

[0189] After collecting facial image data (1312) or if facial data has already been saved from the previous scan, the routine accesses the collected operational data from the RPT (1314) (for one set period (e.g., 2 days of use)). Of course, any other appropriate period longer or shorter than 2 days can be used as the period for collecting operational data and other related data from the RPT. It may also be used in this way. For example, the system in Figure 2 can collect compliant objective data from two days of use (e.g., usage time or leak data from RPT250).

[0190] In addition, subjective feedback data (e.g., seal, comfort, general likes and dislikes) can be collected from the interface of a user application 360 run by a computing device 230 (1316). As described above, subjective data can be collected through an interface that provides questions to the patient. Thus, subjective data may include responses related to questions about discomfort or leakage and psychological safety (e.g., whether the patient is psychologically comfortable with the mask). Other data may be collected based on the visual representation of the mask in relation to a facial image.

[0191] Next, the routine correlates objective and subjective data with the selected mask type and patient facial scan data (1318). If the results are good, the routine determines that the behavioral data indicates high compliance, low leakage, and good subjective outcome data from the patient (1320). The routine then updates the database and learning algorithm with the correlated data as successful mask properties or features (1322). If the results are poor, the routine also analyzes the correlated data to determine if the results can be improved by adjusting the mask properties (1324). The routine then proposes modifications to the properties according to this analysis (1326). For example, the routine might propose thickening the part of the mask that connects to the nose to avoid detection or reporting of leakage. The routine then saves the results by updating the database and learning algorithm (1322).

[0192] The exemplary generation system 1400 shown in Figure 14 generates a modified interface based on data collected from the data acquisition system 200 in Figure 5. The server 210 provides the analysis module 1420 with operational data collected from a population of RPT devices 1410 and subjective data collected from a population of patients 1412 by the application 230.

[0193] The analysis module 1420 includes access to an interface database 270 containing data related to masks of different models from one or more different manufacturers. The analysis module 1420 may include machine learning routines that provide suggestions for changes to interface properties or features in a particular patient or interface used by one subgroup of a patient population. For example, novel properties of a mask design may be obtained by inputting collected behavioral and patient input data along with facial image data into the analysis module 1420. Manufacturing data (e.g., CAD / CAM files of existing mask designs) is stored in the database 1430. The modified design is generated by the analysis module and communicated to the manufacturing system 1440 to generate a mask with changes such as dimensions, sizing, and materials. In this example, the manufacturing system 1440 may include tooling machines, molding machines, 3D printing systems, etc., for mask generation.

[0194] To make the manufacturing of custom components more efficient than additive manufacturing, prototyping of molding tools in manufacturing system 1440 (e.g., 3D printing) can be performed rapidly based on modified changes. In some cases, high-speed 3D printing for tooling can improve the cost-effectiveness of small-volume manufacturing methods. Flexible tools made of aluminum and / or thermoplastic are also possible. Flexible tools enable small-volume molding and are more cost-effective than steel tools.

[0195] Rigid tooling may be used when manufacturing custom components. Rigid tooling may be used when a suitable amount of interface is generated based on collected feedback data. Tooling may be preferable in some cases. Hard tools can be constructed from various grades of steel or other materials used during the forming / machining processes. The manufacturing process may also include the use of any combination of fast prototypes, soft tools, and hard tools for the creation of any of the patient interface components. The structure of the tool can also differ, for example, in the tool itself using one or all of the types of tooling. That is, half of the tool that can define the more comprehensive features of a part may be constructed from hard tooling, and the remaining half of the tool that defines the custom component may be constructed from fast prototypes or soft tooling. Combinations of hard tooling or soft tooling are also possible.

[0196] In the case of interfaces having interfaces within the same component, other manufacturing techniques may include multi-shot injection molding. For example, a patient interface cushion may contain different materials or different soft grades of material in different areas of the patient interface. Thermoforming (e.g., vacuum forming) may also be used, in which case, for example, a plastic sheet may be heated, and these sheets may be vacuum-drawn onto a tool mold and then cooled until they take the shape of the mold. This is a viable option for molding components of custom nostril covers. In yet another form, a material that can be malleable initially may be used for the production of a customized patient interface frame (or any other suitable component (e.g., headgear or a part of it (e.g., a rigidizer))). A patient “male” mold may be produced using one or more of the techniques described herein, and by placing malleable “template” components on this “male” mold, the shape of a component suitable for the patient can be produced. Then, by “curing” the customized components, these components may be prevented from becoming malleable. Examples of such materials include thermosetting polymers that are initially malleable until a certain temperature is reached (and then irreversibly harden), or thermoplastics (also called thermoplastics) that become malleable above a certain temperature. Custom fabric weaving / knitting / forming may also be used. This technique is similar to a 3D printing process, except that it uses yarn instead of plastic. By weaving the structure of textile components, it is possible to create any 3D shape that is ideal for custom headgear production.

[0197] As used in this application, terms such as “component,” “module,” and “system” generally refer to computer-related entities that are either hardware (e.g., circuits), a combination of hardware and software, software, or entities relating to an operating machine having one or more specific functions. For example, a component may be, but is not limited to, a process executed on a processor (e.g., a digital signal processor), a processor, an object, an executable file, an execution thread, a program, and / or a computer. For example, both a controller and an application running on the controller may be components. One or more components may reside within a process and / or an execution thread, and some components may be localized on one computer or distributed across two or more computers. Furthermore, a “device” may take the form of specially designed hardware, generalized hardware specialized by the execution of software that enables the performance of a specific function, software stored on a computer-readable medium, or a combination thereof.

[0198] The terms used herein are for the sole purpose of describing specific embodiments and do not limit the invention. The singular forms “a,” “an,” and “the” used herein are intended to include the plural form unless otherwise evident from the context. Furthermore, where the forms for carrying out the invention and the claims use “include,” “have,” or their conjugations, these terms are used as well as the term “equip.” It is intended to be comprehensive.

[0199] One or more further executions and / or claims of the present disclosure can be formed by combining one or more elements, aspects, steps or parts thereof from any one or more of the following claims 1 to 28 with one or more elements, aspects, steps or parts thereof from any one or more of the other claims 1 to 28 or any combination thereof.

[0200] While this disclosure has been described with reference to one or more specific embodiments or executions, those skilled in the art will recognize that numerous modifications are possible without departing from the intent and scope of this disclosure. Each of these executions and its explicit modifications is intended to fall within the intent and scope of this disclosure. Further or alternative executions in accordance with aspects of this disclosure may also incorporate any number of features from any of the executions described herein (e.g., those in the alternative executions described below).

Claims

1. A method for collecting data related to the patient interface for a respiratory pressure therapy device, An application running on a mobile device equipped with a camera instructs the patient to capture an image of the patient's face via video or audio instructions. Correlating facial image data obtained from the facial image of the aforementioned patient with the aforementioned patient, The system displays a graphical interface that includes graphics allowing the patient to select the type of patient interface. In relation to the selected type of patient interface, the collection of patient input data entered by the patient in response to questions displayed on the mobile device, Methods that include...

2. The method according to claim 1, further comprising collecting operational data of a respiratory therapy device used by the patient in conjunction with the patient interface.

3. This further includes correlating the characteristics of the patient interface with the facial image data, the motion data, and the patient input data. The method of claim 2, wherein the aforementioned characteristics define the physical characteristics of the patient interface.

4. The method according to any one of claims 1 to 3, wherein the instructions are given by the display of the mobile device, by voice instructions from the speaker of the mobile device, or by a tutorial video played on the display of the mobile device.

5. The method according to any one of claims 1 to 4, wherein the patient interface is a mask.

6. The method according to any one of claims 1 to 5, wherein the respiratory pressure therapy device is one of a continuous positive airway (CPAP) device, a non-invasive ventilation (NIV) device, or an invasive ventilation device.

7. The method according to any one of claims 1 to 6, further comprising displaying a facial image together with an inserted image of the patient interface, and collecting patient input data from the patient based on the patient selecting from a list of locations indicated in the inserted image of the patient interface.

8. The method according to claim 7, wherein the inserted image includes lines that demarcate multiple regions of the patient interface.

9. The method according to claim 8, wherein the patient input data includes data relating to comfort or leakage for each region of the inserted image.

10. The method according to any one of claims 1 to 9, wherein the patient input data is collected by displaying questions within an interface on a mobile device.

11. The method according to claim 10, wherein the interface on the mobile device displays a sliding scale for inputting the patient's responses.

12. The method according to any one of claims 1 to 11, further comprising the step of collecting additional patient input data, including at least one of sleep position, sleep type, mask model, or cushion size.

13. The method according to any one of claims 1 to 12, wherein the step of displaying a graphical interface includes displaying a graphical image of the patient interface and comparing the graphical image with identification data relating to a known mask model.

14. The method according to any one of claims 1 to 13, wherein the facial image data is used to identify facial height, nasal width, and nasal depth that correlate with the patient.

15. The method according to any one of claims 3 to 14, further comprising adjusting the properties of the patient interface manufactured based on the adjusted properties in order to avoid leakage, wherein the properties are associated with the contact between the patient interface and the facial surface of the patient.

16. The method according to any one of claims 3 to 15, further comprising adjusting the characteristics of the patient interface for improved comfort, wherein the characteristics are associated with contact with the patient's facial surface.

17. The method further includes collecting facial image data from a second patient similar to the aforementioned patient, operational data from a respiratory therapy device used by the second patient, and patient input data from the second patient. The method according to any one of claims 1 to 16, wherein facial image data from the second patient, operating data of a respiratory therapy device used by the second patient, and patient input data from the second patient are used to correlate the characteristics of the patient interface.

18. Collect facial image data, motion data, and patient input data obtained from multiple patients, including the aforementioned patient, as a training set. The method according to any one of claims 1 to 17, further comprising adjusting the characteristics of the patient interface to achieve the desired result, based on determining motion data, patient input data, and facial image data that correlate with a desired result related to a characteristic using machine learning learned in the training set, and outputting the characteristic.

19. A mobile device for collecting data related to the patient interface for a respiratory pressure therapy device, A camera for capturing images of the patient's face, A storage device that stores the facial image of the aforementioned patient, A processor that runs an application configured to generate a patient data collection interface for collecting patient input data entered by the patient in relation to the patient interface, A data communication interface for communicating facial image data of the captured facial image and the patient input data, A mobile device equipped with these features.

20. Further includes an analysis module that runs on the aforementioned processor, The analysis module is configured to correlate the characteristics of the patient interface with the facial image data and the patient input data. The mobile device according to claim 19, wherein the aforementioned characteristics define the physical characteristics of the patient interface.

21. The mobile device according to claim 19 or 20, wherein the patient data collection interface displays a facial image together with an inserted image of the patient interface, and collects patient input data from the patient based on the patient selecting from a list of locations indicated in the inserted image of the patient interface.

22. The mobile device according to claim 21, wherein the inserted image includes lines that demarcate multiple areas of the patient interface.

23. The mobile device according to claim 22, wherein the patient input data includes data relating to comfort or leakage for each region of the inserted image.

24. The mobile device according to any one of claims 19 to 23, wherein the patient input data is collected by displaying questions within an interface on the mobile device.

25. The mobile device according to claim 24, wherein the interface on the mobile device displays a sliding scale for inputting the patient's responses.

26. The mobile device according to any one of claims 19 to 25, wherein a graphical interface including a graphical image of the patient interface is compared with identification data relating to a known mask model to identify the patient interface.

27. The application running on the mobile device generates instructions for the patient to capture a facial image, The mobile device according to any one of claims 19 to 26, wherein the instructions are given by the display of the mobile device, by voice instructions from the speaker of the mobile device, or by a tutorial video played on the display of the mobile device.

Images