System and method for determining risk factor for condition

Abstract

A method for determining risk factors for a condition includes generating first image data of an interior of a mouth of an individual. The first image data is related to one or more internal body features of the individual. The method further includes generating second image data of an exterior of the mouth of the individual. The second image data is related to one or more external body features of the individual. The method further includes determining a risk factor of the individual associated with the condition based at least in part on the first image data, the second image data, or both.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 398,831, filed on Aug. 17, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to systems and methods for determining risk factors for a medical condition, and more particularly, to systems and methods for determining risk factors for a medical condition based on internal and / or external body characteristics of an individual. Background Art

[0004] Many individuals suffer from sleep - related and / or breathing - related disorders, such as, for example, sleep - disordered breathing (SDB), which can include obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea (such as mixed apnea and hypopnea), respiratory effort - related arousals (RERA), and snoring. In some cases, these disorders manifest or are more pronounced when an individual is in a particular lying / sleeping position. These individuals may also suffer from other health conditions (which may be referred to as comorbidities), such as insomnia (e.g., difficulty falling asleep, frequent or long awakenings after initially falling asleep, and / or early awakening and inability to fall back asleep), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), Cheyne - Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), rapid - eye - movement (REM) behavior disorder (also known as RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, chest wall disorders.

[0005] These disorders are typically treated using a respiratory therapy system (e.g., a continuous positive airway pressure (CPAP) system) that delivers pressurized air to help prevent the airway of an individual from narrowing or collapsing during sleep. The pressurized air is delivered at least via a conduit of a respiratory therapy device coupled to the respiratory therapy system and a user interface worn by the individual. A variety of different internal and external body characteristics can affect the development and / or severity of these conditions, as well as the efficacy of treatment via a respiratory therapy system. Thus, it would be advantageous to be able to accurately monitor an individual's body characteristics and determine the risk of an individual developing any of these diseases. The present disclosure aims to address these and other problems. Summary of the Invention

[0006] According to some implementations of the present disclosure, a method for determining risk factors of an individual associated with a disorder includes generating first image data of the interior of the individual's mouth, the interior of the individual's throat, or both. The first image data is associated with one or more internal body characteristics of the individual. The method further includes determining, at least in part based on the first image data, the risk factors of the individual associated with the disorder. In some implementations, determining the risk factors may include determining whether the individual currently has the disorder, determining the likelihood that the individual will develop the disorder, or both. In some implementations, the method further includes generating second image data of the individual's head, the individual's neck, or both. The second image data is associated with one or more external body characteristics of the individual. In some implementations, the risk factors may be: based only on the first image data, based only on the second image data, based on both the first image data and the second image data, initially based on the first image data and then updated based on the second image data, initially based on the second image data and then updated based on the first image data, or based on the first image data and / or the second image data and additional image data.

[0007] According to some implementations of the present disclosure, a system for determining risk factors of an individual associated with a disorder includes an electronic interface, a memory, and a control system. The electronic interface is configured to receive and / or generate data associated with the individual. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to generate first image data of the interior of the individual's mouth, the interior of the individual's throat, or both. The first image data is associated with one or more internal body characteristics of the individual. The one or more processors are further configured to execute the machine-readable instructions to determine, at least in part based on the first image data, the risk factors of the individual. In some implementations, determining the risk factors may include determining whether the individual currently has the disorder, determining the likelihood that the individual will develop the disorder, or both. In some implementations, the one or more processors are further configured to execute the machine-readable instructions to generate second image data of the individual's head, the individual's neck, or both. The second image data is associated with one or more external body characteristics of the individual. In some implementations, the risk factors may be: based only on the first image data, based only on the second image data, based on both the first image data and the second image data, initially based on the first image data and then updated based on the second image data, initially based on the second image data and then updated based on the first image data, or based on the first image data and / or the second image data and additional image data.

[0008] The foregoing summary is not intended to represent every implementation or every aspect of the present disclosure. Additional features and advantages of the present disclosure will be apparent from the detailed description and the drawings set forth below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a functional block diagram of a system according to some implementations of the present disclosure;

[0010] Figure 2 is according to some embodiments of the present disclosure Figure 1 a perspective view of at least a portion of a system, a user, and a bed partner;

[0011] Figure 3 illustrates an exemplary timeline of a sleep session according to some embodiments of the present disclosure;

[0012] Figure 4 shows according to some implementations of the present disclosure related to Figure 3 an exemplary hypnogram related to a sleep session;

[0013] Figure 5A is a perspective view of an individual generating image data related to one or more internal body characteristics according to some implementations of the present disclosure;

[0014] Figure 5B is a perspective view of an individual generating image data related to one or more external body characteristics according to some implementations of the present disclosure;

[0015] Figure 6 is a flowchart of a method for determining risk factors according to some implementations of the present disclosure; and

[0016] Figure 7 is a diagram of five possible Mallampati score classes according to some implementations of the present disclosure.

[0017] While the present disclosure admits of various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that this is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure will cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims. DETAILED DESCRIPTION

[0018] The present disclosure is described with reference to the accompanying drawings, in which like or equivalent elements are denoted by the same reference numerals throughout all the drawings. The drawings are not drawn to scale and are for illustrative purposes only. Several aspects of the present disclosure are described below with reference to example applications for illustration.

[0019] Many individuals suffer from sleep-related and / or breathing disorders, such as sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, respiratory effort-related arousals (RERA), snoring, Cheyne-Stokes respiration (CSR), hypoventilation, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), neuromuscular disease (NMD), and chest wall disorders.

[0020] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events including occlusion or obstruction of the upper airway during sleep caused by a combination of an abnormally small upper airway and loss of normal muscle tone in the regions of the tongue, soft palate, and posterior oropharyngeal wall. More generally, apnea generally refers to a cessation of breathing caused by an air blockage (obstructive sleep apnea) or a cessation of respiratory function (commonly referred to as central sleep apnea). CSA occurs when the brain temporarily stops sending signals to the muscles that control breathing. Typically, during an obstructive sleep apnea event, an individual will stop breathing for about 15 seconds to about 30 seconds.

[0021] Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is typically characterized by slow or shallow breathing caused by a narrowed airway rather than airway blockage. Hyperpnea is typically characterized by an increase in the depth and / or rate of breathing. Hypercapnia is typically characterized by an elevation or excess of carbon dioxide in the bloodstream, usually caused by hypoventilation.

[0022] Respiratory effort-related arousals (RERA) events are typically characterized by increased respiratory effort lasting ten seconds or longer, resulting in a microarousal from sleep, and which do not meet the criteria for an apnea or hypopnea event. RERA is defined as a respiratory sequence characterized by increased respiratory effort resulting in a microarousal from sleep but not meeting the criteria for an apnea or hypopnea. These events meet the following criteria: (1) a pattern of gradually more negative esophageal pressure, terminated by a sudden change in pressure to a lower negative level and a microarousal, and (2) the event lasts 10 seconds or longer. In some embodiments, a nasal cannula / pressure transducer system is sufficient and reliable in the detection of RERA. A RERA detector can be based on an actual flow signal derived from a respiratory therapy device. For example, a flow restriction metric can be determined based on the flow signal. A microarousal metric can then be derived based on the flow restriction metric and a metric of a sudden increase in ventilation volume. One such method is described in WO2008 / 138040 and U.S. Patent No. 9,358,353, both assigned to ResMed Ltd., the disclosures of each of which are hereby incorporated herein by reference in their entirety.

[0023] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of the patient's respiratory controller, in which there is an alternating cycle of waxing and waning ventilation called the CSR cycle. CSR is characterized by repetitive deoxygenation and reoxygenation of arterial blood.

[0024] Obesity hypoventilation syndrome (OHS) is defined as the combination of severe obesity and chronic hypercapnia while awake, in the absence of other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and daytime hypersomnolence.

[0025] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower airway diseases that share certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of respiration, and loss of normal elasticity of the lungs. COPD encompasses a group of lower airway diseases that share certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of respiration, and loss of normal elasticity of the lungs.

[0026] Neuromuscular diseases (NMD) encompass many disorders and diseases that directly or indirectly impair muscle function through intrinsic muscle pathology or neuropathology. Chest wall disorders are a group of thoracic deformities that result in an inefficient coupling between the respiratory muscles and the thoracic cage.

[0027] These and other disorders are characterized by specific events that occur when an individual sleeps (e.g., snoring, apnea, hypopnea, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, seizure, convulsion, or any combination thereof).

[0028] The apnea-hypopnea index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total number of hours of sleep in the sleep session. The event can be, for example, an apnea that lasts at least 10 seconds. An AHI less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indication of mild sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered an indication of moderate sleep apnea. An AHI greater than or equal to 30 is considered an indication of severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. When the AHI is normal, or when the AHI is normal or mild, sleep apnea can be considered "controlled". The AHI can also be used in combination with the oxygen desaturation level to indicate the severity of obstructive sleep apnea. As will be understood, a sleep session as described herein can alternatively be referred to as a treatment session (during which an individual can receive respiratory therapy), or can include or consist of a treatment session.

[0029] Reference Figure 1 , which illustrates a system 10 according to some embodiments of the present disclosure. The system 10 may include a respiratory therapy system 100, a control system 200, a memory device 204, and one or more sensors 210. The system 10 may additionally or alternatively include a user device 260, an activity tracker 270, and a blood pressure device 280. The system 10 can be used to analyze data from an individual and determine risk factors associated with a condition for that individual.

[0030] The respiratory therapy system 100 includes a respiratory pressure therapy (RPT) device 110 (referred to herein as the respiratory therapy device 110), a user interface 120 (also referred to as a mask or patient interface), a conduit 140 (also referred to as a tube or air circuit), a display device 150, and a humidifier 160. Respiratory pressure therapy refers to the application of supplying air to the inlet of a user's airway at a controlled target pressure that is nominally positive relative to the atmosphere (e.g., as opposed to negative pressure therapies such as tank ventilators or cuirasses) throughout the user's respiratory cycle. The respiratory therapy system 100 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0031] The respiratory therapy system 100 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an auto-titrating positive airway pressure system (APAP), a bilevel or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the air pressure delivered to the user based on, for example, respiratory data related to the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0032] As Figure 2 shown, the respiratory therapy system 100 can be used to treat the user 20. In this example, the user 20 of the respiratory therapy system 100 and the bed partner 30 are located on the bed 40 and lying on the mattress 42. The user interface 120 can be worn by the user 20 during a sleep session. The respiratory therapy system 100 generally helps to increase the air pressure in the throat of the user 20 to help prevent the airway from closing and / or narrowing during sleep. The respiratory therapy device 110 can be positioned directly adjacent to the nightstand 44 of the bed 40 as Figure 2 shown, or more generally, on any surface or structure that is typically adjacent to the bed 40 and / or the user 20.

[0033] Return reference Figure 1 , the respiratory therapy device 110 is generally used to generate pressurized air delivered to the user (e.g., using one or more motors driving one or more compressors). In some embodiments, the respiratory therapy device 110 generates a continuous and constant air pressure delivered to the user. In other embodiments, the respiratory therapy device 110 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In other embodiments, the respiratory therapy device 110 generates various different air pressures within a predetermined range. For example, the respiratory therapy device 110 may deliver at least about 6 cmH 2 O, at least about 10 cmH 2 O, at least about 20 cmH 2 O, about 6 cmH 2 O to about 10 cmH 2 O, about 7 cmH 2 O to about 12 cmH 2 O, etc. The respiratory therapy device 110 may also deliver pressurized air at a predetermined flow rate, such as from about -20 L / minute to about 150 L / minute, while maintaining a positive pressure (relative to ambient pressure).

[0034] The respiratory therapy device 110 includes a housing 112, a blower motor 114, an air inlet 116, and an air outlet 118. The blower motor 114 is at least partially disposed or integrated within the housing 112. The blower motor 114 draws air (e.g., atmospheric air) from the exterior of the housing 112 via the air inlet 116 and causes the pressurized air to flow through the humidifier 160 and through the air outlet 118. In some implementations, the air inlet 116 and / or the air outlet 118 include a lid movable between a closed position and an open position (e.g., to prevent or inhibit air flow through the air inlet 116 or the air outlet 118). The housing 112 may also include a vent to allow air to reach the air inlet 116 through the housing 112. As described below, the conduit 140 is coupled to the air outlet 118 of the respiratory therapy device 110.

[0035] The user interface 120 engages a portion of the user's face and delivers pressurized air from the respiratory therapy device 110 to the user's airway to help prevent the airway from narrowing and / or collapsing during sleep. This can also increase the user's oxygen intake during sleep. Generally, the user interface 120 engages the user's face such that the pressurized air is delivered to the user's airway via the user's mouth, the user's nose, or both the user's mouth and nose. The respiratory therapy device 110, the user interface 120, and the conduit 140 together form an air pathway that is fluidly coupled to the user's airway. The pressurized air also increases the user's oxygen intake during sleep. Depending on the therapy to be applied, the user interface 120 can, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure that is sufficiently different from ambient pressure (e.g., at a positive pressure of about 10 cm H 2 O) to effect the therapy. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery of the gas supply to the airway at a positive pressure of about 10 cmH 2 O.

[0036] The user interface 120 can include, for example, a gasket 122, a frame 124, a headgear 126, a connector 128, and one or more vents 130. The gasket 122 and the frame 124 define the volume of a space surrounding the user's mouth and / or nose. When the respiratory therapy system 100 is in use, this volume space receives pressurized air (e.g., from the respiratory therapy device 110 via the conduit 140) to enter the user's airway. The headgear 126 is generally used to help position and / or stabilize the user interface 120 on a portion of the user (e.g., the face) and, together with the gasket 122 (which can include, for example, silicone, plastic, foam, etc.), helps provide a substantially airtight seal between the user interface 120 and the user 20. In some embodiments, the headgear 126 includes one or more straps (e.g., including hook-and-loop fasteners). The connector 128 is generally used to couple (e.g., connect and fluidly couple) the conduit 140 to the gasket 122 and / or the frame 124. Alternatively, the conduit 140 can be directly coupled to the gasket 122 and / or the frame 124 without the connector 128. One or more vents 130 can be used to allow carbon dioxide and other gases exhaled by the user 20 to escape. The user interface 120 can generally include any suitable number of vents (e.g., one, two, five, ten, etc.).

[0037] As Figure 2As shown, in some embodiments, the user interface 120 is a face mask (e.g., a full-face mask) that covers at least a portion of the nose and mouth of the user 20. Alternatively, the user interface 120 can be a nasal mask that supplies air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. In other implementations, the user interface 120 includes a mouthpiece (e.g., a night guard mouthpiece molded to fit the user's teeth, a mandibular repositioning device, etc.).

[0038] Return reference Figure 1 , the conduit 140 (also referred to as an air circuit or tube) allows air to flow between components of the respiratory therapy system 100, such as between the respiratory therapy device 110 and the user interface 120. In some embodiments, there may be separate branches for the inhalation and exhalation conduits. In other implementations, a single limb conduit is used for both inhalation and exhalation.

[0039] The conduit 140 includes a first end coupled to the air outlet 118 of the respiratory therapy device 110. A variety of techniques (e.g., press-fit connection, snap-fit connection, threaded connection, etc.) can be used to couple the first end to the air outlet 118 of the respiratory therapy device 110. In some implementations, the conduit 140 includes one or more heating elements that heat the pressurized air flowing through the conduit 140 (e.g., heating the air to a predetermined temperature or within a predetermined temperature range). Such heating elements can be connected to the conduit 140 and / or embedded within the conduit. In such an implementation, the first end can include electrical contacts that are electrically coupled to the respiratory therapy device 110 to power one or more heating elements of the conduit 140. For example, the electrical contacts can be electrically coupled to the electrical contacts of the air outlet 118 of the respiratory therapy device 110. In this example, the electrical contacts of the conduit 140 can be male connectors, while the electrical contacts of the air outlet 118 can be female connectors, or alternatively, the opposite configuration can be used.

[0040] The display device 150 is generally used to display images including still images, video images, or both and / or information about the respiratory therapy device 110. For example, the display device 150 can provide information about the status of the respiratory therapy device 110 (e.g., whether the respiratory therapy device 110 is on / off, the pressure of the air delivered by the respiratory therapy device 110, the temperature of the air delivered by the respiratory therapy device 110, etc.) and / or other information (e.g., sleep score and / or therapy score (also referred to as myAir TMScores, such as those described in WO 2016 / 061629 and U.S. Patent Publication No. 2017 / 0311879, which are hereby incorporated by reference in their entirety herein), the current date / time, personal information of the user 20, etc.). In some embodiments, the display device 150 serves as a human-machine interface (HMI), which includes a graphical user interface (GUI) configured to display images as an input interface. The display device 150 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory therapy device 110.

[0041] The humidifier 160 is coupled to or integrated in the respiratory therapy device 110 and includes a reservoir 162 for storing water, which can be used to humidify the pressurized air delivered from the respiratory therapy device 110. The humidifier 160 includes one or more heating elements 164 to heat the water in the reservoir to generate water vapor. The humidifier 160 can be fluidly coupled to the water vapor inlet of the air passage between the blower motor 114 and the air outlet 118, or can be formed in line with the air passage between the blower motor 114 and the air outlet 118. For example, air flows through the blower motor 114 from the air inlet 116 before leaving the respiratory therapy device 110 via the air outlet 118, and then flows through the humidifier 160.

[0042] Although the respiratory therapy system 100 is described herein as including each of the respiratory therapy device 110, the user interface 120, the conduit 140, the display device 150, and the humidifier 160, according to implementations of the present disclosure, more or fewer components may be included in the respiratory therapy system. For example, a first alternative respiratory therapy system includes the respiratory therapy device 110, the user interface 120, and the conduit 140. As another example, a second alternative system includes the respiratory therapy device 110, the user interface 120, the conduit 140, and the display device 150. Thus, various respiratory therapy systems can be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0043] The control system 200 includes one or more processors 202 (hereinafter referred to as the processor 202). The control system 200 is generally used to control (e.g., actuate) the various components of the system 10 and / or analyze data obtained and / or generated by the components of the system 10. The processor 202 can be a general-purpose processor or a dedicated processor or a microprocessor. Although in Figure 1A processor 202 is illustrated, but control system 200 may include any number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located in a single housing or remotely from each other. Control system 200 (or any other control system) or a portion of control system 200, such as processor 202 (or any other processor or one or more portions of any other control system), may be used to perform one or more steps of any of the methods described and / or claimed herein. Control system 200 may be coupled to the housing of user device 260 and / or located, for example, within the housing of the user device, within a portion of respiratory therapy system 100 (e.g., respiratory therapy device 110), and / or within the housing of one or more of sensors 210. Control system 200 may be centralized (within one such housing) or decentralized (within two or more physically distinct such housings). In such embodiments that include two or more housings that include control system 200, the housings may be located close to and / or remotely from each other.

[0044] Memory device 204 stores machine-readable instructions executable by processor 202 of control system 200. Memory device 204 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard disk drive, a solid state drive, a flash memory device, etc. Although one memory device 204 is illustrated in Figure 1 system 10 may include any suitable number of memory devices 204 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). Memory device 204 may be coupled to and / or located within the housing of respiratory therapy device 110 of respiratory therapy system 100, within the housing of user device 260, within the housing of one or more of sensors 210, or any combination thereof. Similar to control system 200, memory device 204 may be centralized (within one such housing) or decentralized (within two or more physically distinct such housings). Thus, although control system 200 and memory device 204 are illustrated as separate components in the Figure 1 block diagram, they may be components of some other components of system 10, such as user device 260, respiratory therapy device 110, etc.

[0045] In some embodiments, the memory device 204 stores user profiles related to a user. The user profiles may include, for example, demographic information related to the user, biometric information related to the user, medical information related to the user, self-reported user feedback, sleep parameters related to the user (e.g., sleep-related parameters recorded from one or more earlier sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, user's gender, user's race, user's geographical location, relationship status, family history of insomnia or sleep apnea, user's employment status, user's education status, user's socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions related to the user, the user's medication use, or both. The medical information data may also include multiple sleep latency test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported user feedback may include information indicating self-reported subjective sleep scores (e.g., poor, average, excellent), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, life events recently experienced by the user, or any combination thereof.

[0046] As described herein, the processor 202 and / or the memory device 204 may receive data (e.g., physiological data and / or audio data) from one or more sensors 210 such that the data is stored in the memory device 204 and / or analyzed by the processor 202. The processor 202 and / or the memory device 204 may communicate with one or more sensors 210 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, via a cellular network, etc.). In some embodiments, the system 10 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. Such components may be coupled to or integrated into the housing of the control system 200 (e.g., in the same housing as the processor 202 and / or the memory device 204) or the user device 260.

[0047] One or more sensors 210 include a pressure sensor 212, a flow sensor 214, a temperature sensor 216, a motion sensor 218, a microphone 220, a speaker 222, a radio frequency (RF) receiver 226, an RF transmitter 228, a camera 232, an infrared (IR) sensor 234, a photoplethysmogram (PPG) sensor 236, an electrocardiogram (ECG) sensor 238, an electroencephalogram (EEG) sensor 240, a capacitance sensor 242, a force sensor 244, a strain gauge sensor 246, an electromyogram (EMG) sensor 248, an oxygen sensor 250, an analyte sensor 252, a humidity sensor 254, a light detection and ranging (LiDAR) sensor 256, or any combination thereof. Generally, each sensor of the one or more sensors 210 is configured to output sensor data that is received and stored in the memory device 204 or one or more other memory devices.

[0048] Although the one or more sensors 210 are shown and described as including each of a pressure sensor 212, a flow sensor 214, a temperature sensor 216, a motion sensor 218, a microphone 220, a speaker 222, an RF receiver 226, an RF transmitter 228, a camera 232, an IR sensor 234, a PPG sensor 236, an ECG sensor 238, an EEG sensor 240, a capacitance sensor 242, a force sensor 244, a strain gauge sensor 246, an EMG sensor 248, an oxygen sensor 250, an analyte sensor 252, a humidity sensor 254, and a LiDAR sensor 256, more generally, the one or more sensors 210 can include any combination and any number of each of the sensors described and / or shown herein.

[0049] As described herein, the system 10 can generally be used to generate physiological data related to a user (e.g., a user of the respiratory therapy system 100) during a sleep session. The physiological data can be analyzed to generate one or more sleep-related parameters, which can include any parameter, measurement, etc., related to the user during the sleep session. One or more sleep-related parameters that can be determined for the user 20 during a sleep session include, for example, an apnea-hypopnea index (AHI) score, a sleep score, a flow signal, a respiratory signal, a respiratory rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the number of events per hour, an event pattern, a stage, a pressure setting of the respiratory therapy device 110, a heart rate, a heart rate variability, the movement of the user 20, a temperature, EEG activity, EMG activity, a microarousal, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0050] One or more sensors 210 can be used to generate, for example, physiological data, audio data, or both. The control system 200 can use the physiological data generated by one or more of the sensors 210 to determine a sleep-wake signal and one or more sleep-related parameters associated with the user 20 during a sleep session. The sleep-wake signal can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-arousals, or different sleep stages, such as, for example, the rapid eye movement (REM) stage, the first non-REM stage (commonly referred to as "N1"), the second non-REM stage (commonly referred to as "N2"), the third non-REM stage (commonly referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages based on physiological data generated by one or more sensors, such as one or more sensors 210, are described, for example, in WO 2014 / 047310, U.S. Patent Publication No. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, WO 2019 / 122414, and U.S. Patent Publication No. 2020 / 0383580, each of which is hereby incorporated by reference in its entirety.

[0051] In some embodiments, the sleep-wake signal described herein can be timestamped to indicate the time the user enters the bed, the time the user leaves the bed, the time the user attempts to fall asleep, etc. The sleep-wake signal can be measured by one or more sensors 210 during a sleep session at a predetermined sampling rate, such as one sample per second, one sample per 30 seconds, one sample per minute, etc. In some embodiments, the sleep-wake signal can also indicate a respiratory signal, a respiratory rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the number of events per hour, an event pattern, a pressure setting of the respiratory therapy device 110, or any combination thereof during the sleep session. Events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 120), restless legs, sleep disorders, choking, increased heart rate, dyspnea, asthma attack, seizure, convulsion, or any combination thereof. One or more sleep-related parameters that can be determined for the user during a sleep session based on the sleep-wake signal include, for example, total bed time, total sleep time, sleep onset latency, wake after sleep onset parameter, sleep efficiency, fragmentation index, or any combination thereof. As described in further detail herein, the physiological data and / or sleep-related parameters can be analyzed to determine one or more sleep-related scores.

[0052] The physiological data and / or audio data generated by one or more sensors 210 can also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal generally indicates the user's respiration / breathing during the sleep session. The respiratory signal can be indicative of and / or analyzed to determine (e.g., using control system 200) one or more sleep-related parameters such as respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, the pattern of events, sleep state, sleep stage, apnea-hypopnea index (AHI), the pressure setting of the respiratory therapy device 110, or any combination thereof. One or more events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from user interface 120), coughing, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, seizure, seizure, increased blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some sleep-related parameters can be non-physiological parameters. Other types of physiological and / or non-physiological parameters can also be determined based on data from one or more sensors 210 or based on other types of data.

[0053] The pressure sensor 212 outputs pressure data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. In some embodiments, the pressure sensor 212 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) of the user of the respiratory therapy system 100 and / or the ambient pressure. In such embodiments, the pressure sensor 212 can be coupled to or integrated within the respiratory therapy device 110. The pressure sensor 212 can be, for example, a capacitance sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0054] The flow sensor 214 outputs flow data, which can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. Examples of flow sensors (such as, for example, the flow sensor 214) are described in International Publication No. WO 2012 / 012835 and U.S. Patent No. 10,328,219, which are hereby incorporated by reference in their entirety. In some embodiments, the flow sensor 214 is used to determine the air flow from the respiratory therapy device 110, the air flow through the conduit 140, the air flow through the user interface 120, or any combination thereof. In such embodiments, the flow sensor 214 can be coupled to the respiratory therapy device 110, the user interface 120, or the conduit 140 or integrated in the respiratory therapy device, the user interface, or the conduit. The flow sensor 214 can be a mass flow sensor, such as, for example, a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof. In some embodiments, the flow sensor 214 is configured to measure ventilation flow (e.g., intentional "leakage"), unintentional leakage (e.g., mouth leakage and / or mask leakage), patient flow (e.g., air entering and / or leaving the lungs), or any combination thereof. In some embodiments, the flow data can be analyzed to determine the user's cardiac oscillations. In some examples, the pressure sensor 212 can be used to determine the user's blood pressure.

[0055] The temperature sensor 216 outputs temperature data, which can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. In some implementations, the temperature sensor 216 generates temperature data that indicates the core body temperature of the user 20, the skin temperature of the user 20, the temperature of the air flowing from the respiratory therapy device 110 and / or through the conduit 140, the temperature in the user interface 120, the ambient temperature, or any combination thereof. The temperature sensor 216 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0056] The motion sensor 218 outputs motion data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. The motion sensor 218 can be used to detect the movement of the user 20 during a sleep session, and / or to detect the movement of any component in the components of the respiratory therapy system 100, such as the respiratory therapy device 110, the user interface 120, or the conduit 140. The motion sensor 218 can include one or more inertial sensors, such as an accelerometer, a gyroscope, and a magnetometer. In some implementations, the motion sensor 218 can include an acoustic sensor (such as the acoustic sensor 224 discussed herein) and / or an RF sensor (such as the RF sensor 230 discussed herein), which can generate motion data, as further discussed herein. In such implementations, the motion sensor 218, the acoustic sensor, and / or the RF sensor can be arranged in a portable device, such as the user device 260 or the portable device 550 discussed herein. Additionally, although Figure 1 and Figure 2 the respiratory therapy device 110 is shown as including its own display device 150, in some implementations, the respiratory therapy device 110 may not include its own display device, as discussed herein. In some embodiments, the motion sensor 218 alternatively or additionally generates one or more signals representing the body movement of the user, and a signal representing the sleep state of the user can be obtained based on the one or more signals (e.g., via the breathing movement of the user). In some embodiments, the motion data from the motion sensor 218 can be combined with additional data from another sensor in the sensors 210 to determine the sleep state of the user.

[0057] The microphone 220 outputs sound and / or audio data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. The audio data generated by the microphone 220 can be reproduced as one or more sounds (e.g., the sound from the user 20) during a sleep session. The audio data from the microphone 220 can also be used to identify (e.g., using the control system 200) the events experienced by the user during the sleep session, as described in further detail herein. The microphone 220 can be coupled to the respiratory therapy device 110, the user interface 120, the conduit 140, or the user device 260 or integrated therein. The microphone 220 can be coupled to a wearable device (such as a smart watch, smart glasses, headphones, or earplugs) or other head-wearable device or integrated therein. In some embodiments, the system 10 includes a plurality of microphones (e.g., two or more microphones and / or a microphone array with beamforming), such that the sound data generated by each microphone in the plurality of microphones can be used to distinguish the sound data generated by another microphone in the plurality of microphones.

[0058] The speaker 222 outputs sound waves audible to a user of the system 10 (e.g., Figure 2 user 20). The speaker 222 can be used, for example, as an alarm clock or to play an alert or message to the user 20 (e.g., in response to an event). In some embodiments, the speaker 222 can be used to convey audio data generated by the microphone 220 to the user. The speaker 222 can be coupled to or integrated in the respiratory therapy device 110, the user interface 120, the conduit 140, or the user device 260, and / or can be coupled to or integrated in a wearable device (such as a smartwatch, smart glasses, headphones, or earbuds) or other head-wearable device.

[0059] The microphone 220 and the speaker 222 can be used as separate devices. In some implementations, the microphone 220 and the speaker 222 can be combined into an acoustic sensor 224 (e.g., a sonar sensor), as described, for example, in WO 2018 / 050913, WO2020 / 104465, U.S. Patent Application Publication No. 2022 / 0007965, each of which is hereby incorporated by reference in its entirety. In such embodiments, the speaker 222 generates or emits sound waves at a predetermined interval, and the microphone 220 detects the reflections of the emitted sound waves from the speaker 222. The sound waves generated or emitted by the speaker 222 have a frequency inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the user 20 or the bed partner 30. Based at least in part on data from the microphone 220 and / or the speaker 222, the control system 200 can determine the position of the user 20 and / or one or more of the sleep-related parameters described herein, such as a respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, pattern of events, sleep state, sleep stage, pressure setting of the respiratory therapy device 110, or any combination thereof. In such a context, a sonar sensor can be understood to involve active acoustic sensing such as generating and / or sending ultrasonic and / or low-frequency ultrasonic sensing signals through air (e.g., in a frequency range such as about 17 kHz to 23 kHz, 18 kHz to 22 kHz, or 17 kHz to 18 kHz).

[0060] In some embodiments, the sensor 210 includes: (i) a first microphone that is the same as or similar to the microphone 220 and is integrated in the acoustic sensor 224; and (ii) a second microphone that is the same as or similar to the microphone 220 but is separate and distinct from the first microphone integrated in the acoustic sensor 224.

[0061] The RF transmitter 228 generates and / or transmits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signal, short wave signal, etc.). The RF receiver 226 detects the reflection of the radio waves transmitted from the RF transmitter 228, and this data can be analyzed by the control system 200 to determine the user's position and / or one or more of the sleep-related parameters described herein. The RF receiver (RF receiver 226 and RF transmitter 228 or another RF pair) can also be used for wireless communication between the control system 200, the respiratory therapy device 110, one or more sensors 210, the user device 260, or any combination thereof. Although the RF receiver 226 and the RF transmitter 228 are shown as separate and distinct elements in Figure 1 , in some embodiments, the RF receiver 226 and the RF transmitter 228 are combined as part of an RF sensor 230 (e.g., a RADAR sensor). In some such embodiments, the RF sensor 230 includes control circuitry. The format of the RF communication can be Wi-Fi, Bluetooth, etc.

[0062] In some embodiments, the RF sensor 230 is part of a mesh system. An example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh routers, and mesh gateways, each of which can be mobile / removable or fixed. In such embodiments, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points), each of the one or more satellites including an RF sensor that is the same as or similar to the RF sensor 230. The Wi-Fi router and the satellites communicate continuously with each other using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals between the router and the satellites (e.g., differences in received signal strength) that are caused by a moving object or person partially blocking the signal. The motion data can indicate motion, respiration, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0063] The camera 232 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the memory device 204. The image data from the camera 232 can be used by the control system 200 to determine one or more of the sleep-related parameters described herein, such as one or more events (e.g., periodic limb movements or restless leg syndrome), respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, pattern of events, sleep state, sleep stage, or any combination thereof. Additionally, the image data from the camera 232 can be used, for example, to identify the location of the user, to determine the movement of the user's chest, to determine the airflow at the user's mouth and / or nose, to determine the time the user enters the bed, and to determine the time the user leaves the bed. In some implementations, the camera 232 includes a wide-angle lens or a fish-eye lens.

[0064] The IR sensor 234 outputs IR image data that can be reproduced as one or more IR images (e.g., still images, video images, or both) that can be stored in the memory device 204. The IR data from the IR sensor 234 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the user 20 and / or the movement of the user 20. The IR sensor 234 can also be used in combination with the camera 232 when measuring the presence, location, and / or movement of the user 20. For example, the IR sensor 234 can detect IR light having a wavelength between approximately 700 nm and approximately 1 mm, while the camera 232 can detect visible light having a wavelength between approximately 380 nm and approximately 740 nm.

[0065] The PPG sensor 236 outputs physiological data related to the user 20 that can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 236 can be worn by the user 20, embedded in clothing and / or fabric worn by the user 20, embedded in the user interface 120 and / or its associated headgear (e.g., a band, etc.) and / or coupled to the user interface and / or its associated headgear (e.g., a band, etc.).

[0066] The ECG sensor 238 outputs physiological data related to the electrical activity of the heart of the user 20. In some embodiments, the ECG sensor 238 includes one or more electrodes positioned on or around a portion of the user 20 during a sleep session. The physiological data from the ECG sensor 238 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0067] The EEG sensor 240 outputs physiological data related to the electrical activity of the brain of the user 20. In some embodiments, the EEG sensor 240 includes one or more electrodes positioned on or around the scalp of the user 20 during a sleep session. The physiological data from the EEG sensor 240 can be used, for example, to determine the sleep state and / or sleep stage of the user 20 at any given time during the sleep session. In some embodiments, the EEG sensor 240 can be integrated in the user interface 120, a related headgear (e.g., a band, etc.), a headband, or other head-mounted sensor device, etc.

[0068] The outputs of the capacitance sensor 242, the force sensor 244, and the strain gauge sensor 246 are data that can be stored in the memory device 204 and used / analyzed by the control system 200 to determine, for example, one or more of the sleep-related parameters described herein. The EMG sensor 248 outputs physiological data related to the electrical activity generated by one or more muscles. The oxygen sensor 250 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in the conduit 140 or at the user interface 120). The oxygen sensor 250 can be, for example, an ultrasonic oxygen sensor, an electro-chemical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., SpO 2 sensor), or any combination thereof.

[0069] The analyte sensor 252 can be used to detect the presence of analytes in the exhaled breath of the user 20. The data output by the analyte sensor 252 can be stored in the memory device 204 and used by the control system 200 to determine the identity and concentration of any analytes in the user's breath. In some embodiments, the analyte sensor 252 is positioned near the user's mouth to detect analytes in the breath exhaled from the user's mouth. For example, when the user interface 120 is a mask that covers the user's nose and mouth, the analyte sensor 252 can be positioned inside the mask to monitor the user's mouth breathing. In other implementations, such as when the user interface 120 is a nasal mask or nasal pillow mask, the analyte sensor 252 can be positioned near the user's nose to detect analytes in the breath exhaled through the user's nose. In other embodiments, when the user interface 120 is a nasal mask or nasal pillow mask, the analyte sensor 252 can be positioned near the user's mouth. In this embodiment, the analyte sensor 252 can be used to detect whether any air is leaking inadvertently from the user's mouth and / or the user interface 120. In some embodiments, the analyte sensor 252 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some embodiments, the analyte sensor 252 can also be used to detect whether the user is breathing through their nose or mouth. For example, if the presence of an analyte is detected by the data output by the analyte sensor 252 positioned near the user's mouth or positioned inside the mask (e.g., in embodiments where the user interface 120 is a mask), the control system 200 can use this data as an indication that the user is breathing through their mouth.

[0070] The output of the humidity sensor 254 stores data that can be used by the control system 200. The humidity sensor 254 can be used to detect the humidity in various areas around the user (e.g., inside the conduit 140 or the user interface 120, near the user's face, near the connection between the conduit 140 and the user interface 120, near the connection between the conduit 140 and the respiratory therapy device 110, etc.). Thus, in some embodiments, the humidity sensor 254 can be coupled to the user interface 120 or the conduit 140 or integrated therein to monitor the humidity of the pressurized air from the respiratory therapy device 110. In other embodiments, the humidity sensor 254 is placed near any area where the humidity level needs to be monitored. The humidity sensor 254 can also be used to monitor the humidity of the surrounding environment that encloses the user, such as the air in a bedroom.

[0071] The LiDAR sensor 256 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and construct a three-dimensional (3D) map of the surrounding environment (such as a living space). LiDAR typically uses pulsed lasers for time-of-flight measurements. LiDAR is also known as 3D laser scanning. In an example of using such a sensor, a fixed or mobile device (such as a smartphone) having the LiDAR sensor 256 can measure and map an area extending 5 meters or more away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The lidar (LiDAR) sensor 256 can also use artificial intelligence (AI) to automatically geofence the RADAR system by detecting and classifying features in the space that may cause problems for the RADAR system, such as glass windows (which can be highly reflective to RADAR). For example, LiDAR can also be used to provide an estimate of a person's height and the change in height when a person sits down or falls. LiDAR can be used to form a 3D grid representation of the environment. In additional uses, for solid surfaces through which radio waves pass (e.g., radiolucent materials), LiDAR can reflect off such surfaces, allowing for the classification of different types of obstacles.

[0072] In some embodiments, the one or more sensors 210 further include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, a pulse oximeter sensor, a sonar sensor, a RADAR sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, an inclinometer sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0073] Although in Figure 1shown separately herein, but any combination of the one or more sensors 210 can be integrated in and / or coupled to any one or more of the components of the system 10, including the respiratory therapy device 110, the user interface 120, the conduit 140, the humidifier 160, the control system 200, the user device 260, the activity tracker 270, or any combination thereof. For example, the microphone 220 and the speaker 222 can be integrated in and / or coupled to the user device 260, and the pressure sensor 212 and / or the flow sensor 214 can be integrated in and / or coupled to the respiratory therapy device 110. In some embodiments, at least one of the one or more sensors 210 is not coupled to the respiratory therapy device 110, the control system 200, or the user device 260, and is generally positioned adjacent to the user 20 during a sleep session (e.g., positioned on or in contact with a portion of the user 20, worn by the user 20, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.).

[0074] One or more of the respiratory therapy device 110, the user interface 120, the conduit 140, the display device 150, and the humidifier 160 can include one or more sensors (e.g., a pressure sensor, a flow sensor, a microphone, or more generally any one of the other sensors 210 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 110.

[0075] Data from the one or more sensors 210 can be analyzed (e.g., by the control system 200) to determine one or more sleep-related parameters, which can include a respiratory signal, a respiratory rate, a respiratory pattern, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, an event pattern, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. One or more events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak, cough, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, seizure, convulsion, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some sleep-related parameters can be non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined based on data from the one or more sensors 210 or based on other types of data.

[0076] User device 260 includes a display device 262. The user device 260 can be, for example, a mobile device such as a smart phone, a tablet computer, a game console, a smart watch, a laptop computer, etc. In some embodiments, the user device 260 is a portable device such as a smart phone, a tablet computer, a smart watch, a laptop computer, etc. Alternatively, the user device 260 can be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, Amazon Alexa, etc.). In some embodiments, the user device is a wearable device (e.g., a smart watch). The display device 262 is generally used to display images including still images, video images, or both. In some embodiments, the display device 262 serves as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display images and an input interface. The display device 262 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device 260. In some embodiments, the system 10 can use and / or include one or more user devices.

[0077] In some embodiments, the system 10 further includes an activity tracker 270. The activity tracker 270 is generally used to help generate physiological data related to the user. The activity tracker 270 can include one or more of the sensors 210 described herein, such as, for example, a motion sensor 218 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 236, and / or an ECG sensor 238. The physiological data from the activity tracker 270 can be used to determine, for example, the number of steps, the distance traveled, the number of steps climbed, the duration of physical activity, the type of physical activity, the intensity of physical activity, the time spent standing, the respiratory rate, the average respiratory rate, the resting respiratory rate, the maximum respiratory rate, the respiratory rate variability, the heart rate, the average heart rate, the resting heart rate, the maximum heart rate, the heart rate variability, the number of calories burned, the blood oxygen saturation, the skin electrical activity (also known as skin conductance or skin electrical response), or any combination thereof. In some embodiments, the activity tracker 270 is (e.g., electronically or physically) coupled to the user device 260.

[0078] In some embodiments, the activity tracker 270 is a wearable device that can be worn by the user, such as a smart watch, a wristband, a ring, or a patch. For example, refer to Figure 2, the activity tracker 270 is worn on the wrist of the user 20. The activity tracker 270 can also be coupled to or integrated into clothing or garments worn by the user. Alternatively, the activity tracker 270 can also be coupled to or integrated into the user device 260 (e.g., within the same housing). More generally, the activity tracker 270 can be communicatively coupled to or physically integrated within the control system 200, the memory device 204, the respiratory therapy system 100, and / or the user device 260 (e.g., within a housing).

[0079] In some embodiments, the system 10 further includes a blood pressure device 280. The blood pressure device 280 is generally used to help generate cardiovascular data for determining one or more blood pressure measurements associated with the user 20. The blood pressure device 280 can include at least one of the one or more sensors 210 to measure, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

[0080] In some embodiments, the blood pressure device 280 is a sphygmomanometer that includes an inflatable cuff that can be worn by the user 20 and a pressure sensor (e.g., the pressure sensor 212 described herein). For example, in Figure 2 the example, the blood pressure device 280 can be worn on the upper arm of the user 20. In such embodiments where the blood pressure device 280 is a sphygmomanometer, the blood pressure device 280 further includes a pump (e.g., a manually operated bulb) for inflating the cuff. In some embodiments, the blood pressure device 280 is coupled to the respiratory therapy device 110 of the respiratory therapy system 100, which in turn delivers pressurized air to inflate the cuff. More generally, the blood pressure device 280 can be communicatively coupled to and / or physically integrated within the control system 200, the memory device 204, the respiratory therapy system 100, the user device 260, and / or the activity tracker 270 (e.g., within a housing).

[0081] In other embodiments, the blood pressure device 280 is a mobile blood pressure monitor communicatively coupled to the respiratory therapy system 100. The mobile blood pressure monitor includes a portable recording device attached to a strap or band worn by the user 20 and an inflatable cuff attached to the portable recording device and worn around the user 20's arm. The mobile blood pressure monitor is configured to measure blood pressure approximately every 15 minutes to about 30 minutes over a 24-hour or 48-hour period. The mobile blood pressure monitor can simultaneously measure the heart rate of the user 20. The multiple readings are averaged over a 24-hour period. The mobile blood pressure monitor determines any changes in the blood pressure and heart rate of the user 20 measured during the user 20's sleep period and wake period, as well as any distribution and / or trend patterns of the blood pressure and heart rate data. The measured data and statistics can then be communicated to the respiratory therapy system 100.

[0082] The blood pressure device 280 can be positioned external to the respiratory therapy system 100, coupled directly or indirectly to the user interface 120, coupled directly or indirectly to a headgear associated with the user interface 120, or inflatably coupled to a portion of or around the user 20. The blood pressure device 280 is generally used to help generate physiological data to determine one or more blood pressure measurements associated with the user, such as a systolic blood pressure component and / or a diastolic blood pressure component. In some embodiments, the blood pressure device 280 is a sphygmomanometer that includes an inflatable cuff that can be worn by the user and a pressure sensor (e.g., the pressure sensor 212 described herein).

[0083] In some embodiments, the blood pressure device 280 is an invasive device that can continuously monitor the arterial blood pressure of the user 20 and collect arterial blood samples as needed to analyze the gas of the arterial blood. In some other embodiments, the blood pressure device 280 is a continuous blood pressure monitor that uses a radio frequency sensor and can measure the blood pressure of the user 20 only once every few seconds (e.g., every 3 seconds, every 5 seconds, every 7 seconds, etc.). The radio frequency sensor can use continuous waves, frequency modulated continuous waves (FMCW with ramp waves, chirp waves, triangle waves, sine waves, etc.), other schemes such as PSK, FSK, etc., pulsed continuous waves, and / or extensions in the ultra-wideband range (which may include extensions, PRN codes, or pulse systems).

[0084] Although the control system 200 and the memory device 204 are Figure 1 10 as separate and distinct components of system 10, in some embodiments, control system 200 and / or memory device 204 are integrated into user device 260 and / or respiratory therapy device 110. Thus, control system 200 and / or memory device 204 can be disposed within housing 112 of respiratory therapy device 110. Alternatively, in some embodiments, control system 200 or a portion thereof (e.g., processor 202) can be located in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0085] Although system 10 is shown as including all of the above components, more or fewer components may be included in a system according to an embodiment of the present disclosure. For example, a first alternative system includes at least one of control system 200, memory device 204, and one or more sensors 210, and does not include respiratory therapy system 100. As another example, a second alternative system includes control system 200, memory device 204, at least one of one or more sensors 210, and user device 260. As yet another example, a third alternative system includes control system 200, memory device 204, respiratory therapy system 100, at least one of one or more sensors 210, and user device 260. Thus, various systems can be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0086] Now referring Figure 3 , as used herein, a sleep session can be defined in a number of ways. For example, a sleep session can be defined by an initial start time and an end time. In some embodiments, a sleep session is the duration of time that a user is asleep, i.e., the sleep session has a start time and an end time, and during the sleep session, the user does not awaken until the end time. That is, any period of time during which the user is awake is not included in the sleep session. According to this first definition of a sleep session, if a user wakes up and falls asleep multiple times during the same night, each of the sleep intervals separated by wake intervals is a sleep session.

[0087] Alternatively, in some embodiments, a sleep session has a start time and an end time, and during the sleep session, the user can wake up as long as the duration of continuous wakefulness is below a wakefulness duration threshold, and the sleep session does not end. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold can be, for example, about 20% of the sleep session, about 15% of the sleep session duration, about 10% of the sleep session duration, about 5% of the sleep session duration, about 2% of the sleep session duration, etc. or any other threshold percentage. In some embodiments, the wakefulness duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc. or any other amount of time.

[0088] In some embodiments, a sleep session is defined as the entire time period between the time when the user first gets into bed at night and the time when the user finally gets out of bed the next morning. In other words, a sleep session can be defined as the time period starting at a first time (e.g., 10:00 p.m.) on a first date (e.g., Monday, January 6, 2020) and ending at a second time (e.g., 7:00 a.m.) on a second date (e.g., Tuesday, January 7, 2020). The first time can be referred to as the current night when the user first gets into bed with the intention of falling asleep (e.g., if the user intends to watch TV or play on a smartphone before falling asleep, then no). The second time can be referred to as the next morning when the user first gets out of bed with the intention of not going back to sleep the next morning.

[0089] In some embodiments, the user can manually define the start of a sleep session and / or manually terminate a sleep session. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable elements displayed on a display device 262 of the user device 260 ( Figure 1 ) to manually initiate or terminate a sleep session.

[0090] Generally, a sleep session includes any time point after the user has been in bed or sitting on the bed (or another area or object on which they intend to sleep) and has turned on the respiratory therapy device 110 and put on the user interface 120. A sleep session can thus include time periods: (i) when the user is using the respiratory therapy system 100 but before the user attempts to fall asleep (e.g., when the user is reading in bed); (ii) when the user starts to attempt to fall asleep but is still awake; (iii) when the user is in light sleep (also referred to as stages 1 and 2 of non-rapid eye movement (NREM) sleep); (iv) when the user is in deep sleep (also referred to as slow-wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user is in rapid eye movement (REM) sleep; (vi) when the user wakes up periodically between light sleep, deep sleep, or REM sleep; or (vii) when the user wakes up without falling back asleep. A sleep session can also be referred to as a therapy session, or can include a therapy session, which can be understood as the time period within a sleep session during which an individual participates in respiratory therapy (e.g., using the respiratory therapy system).

[0091] A sleep session is typically defined as ending once the user removes the user interface 120, turns off the respiratory therapy device 110, and leaves the bed. In some embodiments, the sleep session may include additional time periods, or may be restricted to only some of the time periods disclosed above. For example, a sleep session may be defined as a time period that begins when the respiratory therapy device 110 starts supplying pressurized air to the airway or the user, ends when the respiratory therapy device 110 stops supplying pressurized air to the user's airway, and includes some or all time points therebetween when the user is asleep or awake.

[0092] Figure 3 Illustrates an exemplary timeline 300 of a sleep session. Timeline 300 includes time of getting into bed (t 床 ), time of falling asleep (t GTS ), initial sleep time (t 睡 ), first microarousal MA 1 , second microarousal MA 2 , arousal A, wake time (t 醒 ), and time of getting out of bed (t 起床 ).

[0093] The time of getting into bed t 床 is related to the time when the user initially gets into bed (e.g., the bed 40 in Figure 2 ) before falling asleep (e.g., when the user lies down or sits in the bed). The time of getting into bed t 床 can be identified at least in part based on a bed threshold duration to distinguish the time when the user gets into bed to sleep from the time when the user gets into bed for other reasons (e.g., watching TV). For example, the bed threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Although the time of getting into bed t 床 is described herein with reference to the bed, more generally, the time of getting into bed t 床 can refer to the time when the user initially enters any location for sleeping (e.g., a chaise longue, a chair, a sleeping bag, etc.).

[0094] The time of going to sleep (GTS) is related to the time when the user initially attempts to fall asleep after getting into bed (t 床 ). For example, after getting into bed, the user may engage in one or more activities to relax before attempting to sleep (e.g., reading, watching TV, listening to music, using the user device 260, etc.). The initial sleep time (t 睡 ) is the time when the user initially falls asleep. For example, the initial sleep time (t 睡 ) can be the time when the user initially enters the first non-REM sleep stage.

[0095] The wake time t 醒is a time related to the time when a user wakes up and does not fall back asleep (e.g., as opposed to a user waking up in the middle of the night and falling back asleep). The user may experience multiple unconscious micro-awakenings (e.g., micro-awakenings MA 1 and MA 2 ) with a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initial falling asleep. Opposite to the wake-up time t 醒 , the user falls back asleep after each of the micro-awakenings MA 1 and MA 2 . Similarly, the user may have one or more conscious awakenings (e.g., awakening A) (e.g., getting up to go to the bathroom, taking care of a child or pet, sleepwalking, etc.) after initial falling asleep. However, the user falls back asleep after awakening A. Thus, the wake-up time t 醒 can be defined, for example, at least in part based on a wake-up threshold duration (e.g., the user has been awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0096] Similarly, the get-up time t 起床 is related to the time when the user gets out of bed and away from the bed with the aim of ending the sleep session (e.g., as opposed to a user getting up during the night to go to the bathroom, taking care of a child or pet, sleepwalking, etc.). In other words, the get-up time t 起床 is the time when the user finally leaves the bed and does not return to the bed until the next sleep session (e.g., the next night). Thus, the get-up time t 起床 can be defined, for example, at least in part based on a get-up threshold duration (e.g., the user has been out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The time to enter the bed for the second subsequent sleep session t 床 can also be defined at least in part based on a get-up threshold duration (e.g., the user has been out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0097] As mentioned above, the user may wake up and get up again during the night between the initial t 床 and the final t 起床 . In some embodiments, the final wake-up time t 醒 and / or the final get-up time t 起床 are identified or determined at least in part based on a predetermined threshold duration after an event (e.g., falling asleep or getting out of bed). Such a threshold duration can be customized for the user. For a standard user who goes to bed at night and then wakes up and gets up in the morning, any time period from about 12 hours to about 18 hours can be used (between the time the user wakes up (t 醒 ) or gets up (t 起床 ) and the time the user goes to bed (t 床 ), enters sleep (tGTS ) or falling asleep (t 睡 ). For users who spend a relatively long period of time in bed, a shorter threshold period can be used (e.g., from about 8 hours to about 14 hours). The threshold period can be initially selected and / or later adjusted at least in part based on a system that monitors a user's sleep behavior.

[0098] Total in-bed time (TIB) is the time t 床 when entering the bed and the time t 起床 when getting out of the bed. Total sleep time (TST) is related to the duration between the initial sleep time and the waking time, excluding any conscious or unconscious awakenings and / or micro-awakenings during that period. Generally, total sleep time (TST) is shorter than total in-bed time (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, as shown by timeline 300, total sleep time (TST) spans between the initial sleep time t 睡 and the waking time t 醒 but does not include the durations of the first micro-awakening MA 1 , the second micro-awakening MA 2 and the awakening A. As shown in the figure, in this example, total sleep time (TST) is shorter than total in-bed time (TIB).

[0099] In some implementations, total sleep time (TST) can be defined as persistent total sleep time (PTST). In such embodiments, persistent total sleep time does not include a predetermined initial portion or period of the first non-REM stage (e.g., the light sleep stage). For example, the predetermined initial portion can be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. Persistent total sleep time is a measure of continuous sleep and smooths the sleep-wake sleep graph. For example, when a user initially falls asleep, the user may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the waking stage for a short period (e.g., one minute), and then return to the first non-REM stage. In this example, persistent total sleep time does not include the first instance of the first non-REM stage (e.g., about 30 seconds).

[0100] In some embodiments, a sleep session is defined as starting at the time of entering the bed (t 床 ) and ending at the time of getting out of the bed (t 起床 ), that is, a sleep session is defined as total in-bed time (TIB). In some implementations, a sleep session is defined as starting at the initial sleep time (t 睡 ) and ending at the waking time (t 醒) ends. In some embodiments, a sleep session is defined as total sleep time (TST). In some embodiments, a sleep session is defined as the time between sleep onset time (t GTS ) and starts at the wake-up time (t 醒 ) ends. In some embodiments, a sleep session is defined as a sleep session starting at sleep entry time (t GTS ) and starts at the wake-up time (t 起床 ) ends. In some embodiments, a sleep session is defined as the time at which a person enters the bed (t 床 ) and starts at the wake-up time (t 醒 ) ends. In some embodiments, a sleep session is defined as a sleep session at an initial sleep time (t 睡 ) and starts at the wake-up time (t 起床 )Finish.

[0101] Reference Figure 4 , illustrating the corresponding Figure 3 300. As shown, hypnogram 400 includes a sleep-wake signal 401, a wake stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. An intersection between sleep-wake signal 401 and one of axes 410-440 represents a sleep stage at any given time during a sleep session.

[0102] The sleep-wake signal 401 may be generated based at least in part on physiological data associated with the user (e.g., generated by one or more of the sensors 210 described herein). The sleep-wake signal may indicate one or more sleep stages, including wakefulness, relaxed wakefulness, micro-awakening, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some embodiments, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage may be grouped together and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage may include the first non-REM stage, and the deep sleep stage may include the second non-REM stage and the third non-REM stage. Although in Figure 4The sleep graph 400 shown includes a light sleep stage axis 430 and a deep sleep stage axis 440. However, in some implementations, the sleep graph 400 may include axes for each of a first non-REM stage, a second non-REM stage, and a third non-REM stage. In other implementations, the sleep-wake signal may also indicate a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration amplitude ratio, an inspiration-expiration duration ratio, a number of events per hour, a pattern of events, or any combination thereof. Information describing the sleep-wake signal may be stored in the memory device 204.

[0103] The sleep graph 400 can be used to determine one or more sleep-related parameters, such as, for example, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep apnea, or any combination thereof.

[0104] Sleep onset latency (SOL) is defined as the time between the time of entry into sleep time (t GTS ) and the initial sleep time (t 睡 ). In other words, sleep onset latency indicates the time it takes for the user to actually fall asleep after initially attempting to fall asleep. In some embodiments, sleep onset latency is defined as persistent sleep onset latency (PSOL). Persistent sleep onset latency differs from sleep onset latency in that persistent sleep onset latency is defined as the duration between the time of entry into sleep time and a predetermined amount of persistent sleep. In some embodiments, the predetermined amount of persistent sleep may include, for example, at least 10 minutes of sleep within a second non-REM stage, a third non-REM stage, and / or a REM stage with awakenings not exceeding 2 minutes, a first non-REM stage, and / or movement therebetween. In other words, persistent sleep onset latency requires persistent sleep within a second non-REM stage, a third non-REM stage, and / or a REM stage for up to, for example, 8 minutes. In other embodiments, the predetermined amount of persistent sleep may include at least 10 minutes of sleep within a first non-REM stage, a second non-REM stage, a third non-REM stage, and / or a REM stage after the initial sleep time. In such embodiments, the predetermined amount of persistent sleep may not include any micro-awakenings (e.g., a ten-second micro-awakening does not restart the 10-minute period).

[0105] Wake after sleep onset (WASO) is related to the total duration that the user wakes up between the initial sleep time and the wake time. Thus, wake after sleep onset includes brief and micro-awakenings of the sleep session (e.g., Figure 4 the micro-awakenings MA shown in 1 and MA 2),either consciously or unconsciously. In some embodiments, wake after sleep onset (WASO) is defined as persistent wake after sleep onset (PWASO) that includes only the total duration of wake with a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0106] Sleep efficiency (SE) is determined as the ratio of total in-bed time (TIB) to total sleep time (TST). For example, if the total in-bed time is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency of this sleep session is 93.75%. Sleep efficiency indicates the user's sleep hygiene. For example, if the user gets into bed before sleeping and spends time engaging in other activities (e.g., watching TV), the sleep efficiency will decrease (e.g., the user is penalized). In some embodiments, sleep efficiency (SE) can be calculated at least in part based on total in-bed time (TIB) and the total time the user attempts to sleep. In such embodiments, the total time the user attempts to sleep is defined as the duration between the time of going to sleep (GTS) and the wake-up time described herein. For example, if the total sleep time is 8 hours (e.g., from 11:00 p.m. to 7:00 a.m.), the time of going to sleep is 10:45 p.m., and the wake-up time is 7:15 a.m., then in such embodiments, the sleep efficiency parameter is calculated to be approximately 94%.

[0107] The fragmentation index is determined at least in part based on the number of awakenings during the sleep session. For example, if the user has two micro-awakenings (e.g., Figure 4 the micro-awakenings MA shown 1 and micro-awakening MA 2 ), then the fragmentation index can be expressed as 2. In some embodiments, the fragmentation index is scaled between a predetermined range of integers (e.g., between 0 and 10).

[0108] Sleep blocks are associated with transitions between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wake stage. Sleep blocks can be calculated at a resolution of, for example, 30 seconds.

[0109] In some embodiments, the systems and methods described herein can include generating or analyzing a sleep graph including sleep-wake signals to determine or identify the time of getting into bed (t 床 ), the time of going to sleep (t GTS ), the initial sleep time (t 睡 ), one or more first micro-awakenings (e.g., MA 1 and MA 2 ), the wake-up time (t 醒 ), the wake-up time (t起床 ) or any combination thereof.

[0110] In other embodiments, one or more of the sensors 210 can be used to determine or identify the time of getting into bed (t 床 ) that in turn defines the entry into a sleep session, the time of falling asleep (t GTS ), the initial sleep time (t 睡 ), one or more first micro-awakenings (e.g., MA 1 and MA 2 ), the waking time (t 醒 ), the time of getting out of bed (t 起床 ) or any combination thereof. For example, the time of getting into bed t 床 can be determined at least in part based on, for example, data generated by the motion sensor 218, the microphone 220, the camera 232, or any combination thereof. The time of falling asleep can be determined at least in part based on, for example, data from the motion sensor 218 (e.g., data indicating that the user is not moving), data from the camera 232 (e.g., data indicating that the user is not moving and / or the user has turned off the light), data from the microphone 220 (e.g., data indicating that the TV is being turned off), data from the user device 260 (e.g., data indicating that the user is no longer using the user device 260), data from the pressure sensor 212 and / or the flow rate sensor 214 (e.g., data indicating that the user has turned on the respiratory therapy device 110, data indicating that the user has put on the user interface 120, etc.) or any combination thereof.

[0111] Now referring to Figure 5A and 5B , it may be advantageous to be able to analyze various different body characteristics of an individual to determine individual risk factors associated with certain disorders such as SDB and / or OSA. Figure 5A Illustrates an individual 500 holding a smart phone 502. The smart phone 502 includes an image sensor 504 that is generally aimed in the direction of the individual 500. The smart phone 502 includes a display 506 that can display an image and / or video of anything in the field of view of the image sensor 504. In Figure 5AIn [description], the mouth 510 of the individual 500 is open, and an image and / or video of the mouth 510 is shown on the display 506 of the smart phone 502. It can be seen that the image / video of the mouth 510 shown on the display 506 includes the tongue 512, upper teeth 514, lower teeth 516, and uvula 518 of the individual 500. The individual 500 generally holds the smart phone 502 close enough to their face such that only the mouth 510 is shown in the display 506 of the smart phone 502. In this position, the image sensor 504 can generate image data that can be reproduced as one or more images and / or videos of the interior of the mouth 510 of the individual 500. This image data is related to various different internal body characteristics of the individual 500 (e.g., the size of the tongue 512, the relative positions of the upper teeth 514 and lower teeth 516, etc.).

[0112] In Figure 5B In [description], the mouth 510 of the individual 500 is closed, and the smart phone 502 is held further away from the face of the individual 500. As shown on the display 506 of the smart phone 502, the head 520 of the individual 500 is within the field of view of the image sensor 504, but the interior of the mouth 510 is not. Thus, the display 506 of the smart phone 502 shows details of the exterior of the head 520, including the eyes 522, nose 524, exterior of the mouth 510, jaw 526, throat 528, and neck 530. In this position, the image sensor 504 can generate image data that can be reproduced as one or more images and / or videos of the exterior of the head 520 and / or neck 530 of the individual 500. This image data is related to various different external body characteristics of the individual 500 (e.g., the shape of the jaw 526, the circumference of the neck 530, etc.).

[0113] Figure 6 A method 600 for determining risk factors of an individual related to conditions such as sleep disordered breathing (SDB) and / or obstructive sleep apnea (OSA) is shown. Generally, a control system (such as the control system 200 of system 10) is configured to perform the various steps of method 600. A memory device (such as the memory device 204 of system 10) can be used to store any type of data utilized in the steps of method 600 (or other methods). In some implementations, a risk factor indicates the likelihood that an individual will develop a condition at some point in the future. In some implementations, a risk factor indicates the likelihood that an individual has developed a condition. In some embodiments, a risk factor indicates the severity of a condition that an individual has developed or is at risk of developing.

[0114] At step 602 of method 600, first image data of the interior of an individual's mouth and / or throat is generated. The first image data may be reproduced as one or more images and / or videos of the interior of the individual's oral cavity and / or throat and indicate one or more internal body features of the individual. Any suitable device having one or more image sensors may be used to generate the first image data, such as a user device (which may be the same as or similar to user device 260 of system 10). The user device may be an individual's smart phone, tablet computer, camera, or any other suitable device.

[0115] Generally, various different parts of the interior of an individual's mouth and / or throat will be within the field of view of the image sensor of the device, such as the tongue, upper teeth, lower teeth, uvula, gums, the top of the individual's mouth (also referred to as the palate and may include the hard palate and the soft palate), tonsils, salivary glands, and the oral cavity (e.g., the open space defined between at least the tongue, soft palate, and / or hard palate and the interior of the individual's cheeks).

[0116] Accordingly, the first image data can be analyzed to determine various different internal body features of the individual, such as the size of the individual's tongue (e.g., height, length, etc.), the distance between the individual's tongue and the individual's upper jaw, the positions of the individual's upper teeth and lower teeth relative to each other and / or other landmarks, the positions of the individual's upper jaw and lower jaw relative to each other and / or other landmarks, the widths of the individual's upper jaw and lower jaw, the size of the individual's uvula, the height and width of the back of the individual's mouth and / or the throat passing through the uvula, and other features. Generally, the internal body features that can be identified and / or analyzed using the first image data are the body features of the individual that are not visible externally when the individual's mouth is closed (e.g., not within the field of view of the image sensor of the device used to generate the first image data).

[0117] In some embodiments, internal body characteristics can include the Mallampati score, which is used to classify the amount of open space within an individual's mouth. The Mallampati score is evaluated by analyzing an individual's mouth when the individual opens their mouth and extends their tongue. The Mallampati score refers to the amount of visible anatomical structures. In some implementations, an individual's Mallampati score is Class I, Class II, Class III, or Class IV. Class I refers to an individual in whom the soft palate, hard palate, uvula, and tonsils are visible. In some cases, Class I refers to an individual with these visible characteristics, as well as the fish (e.g., the opening into the throat at the back of the mouth) and the palatine arches (the palatoglossal arch and the palatopharyngeal arch). Class IIA refers to an individual in whom the soft palate, hard palate, and most of the uvula are visible. In some cases, Class IIA refers to an individual with these visible characteristics, as well as the larynx. Class IIB refers to an individual in whom the soft palate, hard palate, and the base of the uvula are visible. Class IV refers to an individual in whom only some of the soft palate is visible along with the hard palate. Class V refers to an individual when only the hard palate is visible. In Figure 7 which different classes are shown, which show images of open mouths in each of the five different classes. In some cases, the Mallampati score may include only four classes (I, II, III, and IV). In these embodiments, Class II generally refers to an individual in whom the soft palate and most of the uvula are visible, while Class III generally refers to an individual in whom the soft palate and only the base of the uvula are visible. In both of these implementations, Class I and Class IV are generally the same. In addition or as an alternative, aspects of the Mallampati score can also be included as part of the determined internal body characteristics. For example, different body characteristics can be the size of the uvula, the amount of uvula visible, the size of the soft palate, the amount of soft palate visible, the size of the hard palate, the amount of hard palate visible, the size of the tonsils, the amount of tonsils visible, etc.

[0118] The first image data can be analyzed in a variety of different ways to quantify the internal body characteristics. In some implementations, the absolute value of any identified internal body characteristic is determined. In other implementations, the value of any identified internal body characteristic relative to some baseline is determined. The baseline can be the individual at a previous time, the average of the group of individuals to which the individual belongs, or some other baseline.

[0119] At step 604 of method 600, second image data of the exterior of an individual's head and / or neck is generated. The second image data can be reproduced as one or more images and / or videos of the exterior of the individual's head and / or neck and indicates one or more external physical characteristics of the individual. Similar to the first image data, any suitable device having one or more image sensors (e.g., a user device (which can be the same as or similar to user device 260 of system 10)) can be used to generate the second image data. The user device can be the individual's smart phone, tablet computer, camera, or any other suitable device.

[0120] Generally, various different parts of the exterior of an individual's head and / or neck will be within the field of view of the image sensor of the device, such as the user's eyes, nose, mouth, chin, jaw, and neck. The second image data can be analyzed to determine various different external body characteristics of the individual, such as the position and / or size (e.g., width) of the individual's jaw (a smaller jaw size can indicate a weak jaw, which can cause the individual's tongue to fall backward toward the individual's airway when the individual is lying on their back), the circumference of the individual's neck, the position and / or amount of body fat in the individual's head and neck region, the position and / or amount of muscle in the individual's head and neck region, the size and / or shape of the individual's nose (which can indicate nasal congestion or blockage, which in turn can contribute to SDB), and other characteristics. Body characteristics related to the individual's jaw can include the alignment of the temporomandibular joint (certain alignments of the temporomandibular joint can affect the position of the tongue during sleep, which in turn can cause the tongue to partially or completely block the individual's airway). Generally, the external body characteristics that can be identified and / or analyzed using the second image data are the body characteristics of the individual that are visible externally (e.g., within the field of view of the image sensor of the device used to generate the second image data) when the individual's mouth is closed.

[0121] Similar to the internal body characteristics, the second image data can be analyzed in various different ways to quantify the external body characteristics. In some implementations, the absolute value of any identified external body characteristic is determined. In other implementations, the value of any identified external body characteristic relative to some baseline is determined. The baseline can be the individual at a previous time, the average of a group of individuals to which the individual belongs, or some other baseline.

[0122] At step 606 of method 600, the disease risk factors of an individual are determined. In some implementations, the risk factors of the individual are not based on the second image data and external body features, but on the first image data and internal body features (and any other information that may be required). In these implementations, step 604 of method 600 is typically optional, and method 600 may only include generating the first image data. In other implementations, the risk factors of the individual are not based on the first image and internal body features, but on the second image data and external body features (and any other information that may be required). In these implementations, step 602 of method 600 is typically optional, and method 600 may only include generating the second image data. In further implementations, the risk factors of the individual are based on the first image data and the second image data (and any other information that may be required), and thus both steps 602 and 604 will be performed. In these implementations, the initial risk factors may be determined based on the first image data and the second image data, or the initial risk factors may be determined based on the first image data or the second image data, and then the initial risk factors are updated based on the image data.

[0123] In implementations where the risk factors are determined based only on the first image data or only on the second image data, the risk factors may be updated based on other image data. For example, if the determination of the initial risk factors based only on the first image data or only on the second image data is not accurate enough, the initial risk factors may subsequently be updated based on the second image data or the first image data. Generally, the updated risk factors will be more accurate than the initial risk factors.

[0124] As described herein, the risk factors of an individual may take different forms. In some implementations, the risk factor is an estimate of whether the individual will develop the disease, which may be expressed as a percentage. In other implementations, the risk factor is an estimate of when the individual will develop the disease, which may be expressed as an absolute time (e.g., on a certain date) or a relative time (e.g., within a certain number of months from the current date). In further embodiments, the risk factor is an estimate of whether and when the individual will develop the disease, which may be expressed as a percentage and a relative time (e.g., the chance that the individual will develop the disease within Y months is X%). In additional implementations, determining the risk factors includes determining that the user has (or may have) developed the disease and estimating the severity of the disease.

[0125] In some implementations, determining the risk factors at step 602 can include inputting the first image data and / or the second image data into a trained machine learning model that has been trained to output risk factors. In some cases, additional information can be input into the trained machine learning model, and the trained machine learning model can be used in combination with the first image data and / or the second image data to determine the risk factors. The additional information can include physical characteristics of the individual, such as the individual's height, weight, age, etc. The additional information can additionally or alternatively include information related to the individual's medical history.

[0126] In some implementations, method 600 can further include analyzing the physical characteristics of the individual to determine which physical characteristics may be causing the individual to be at risk of developing the disorder, or which physical characteristics may have caused the individual to have developed the disorder. Thus, method 600 can include determining the contribution of any one or more physical characteristics (e.g., one or more internal physical characteristics, one or more external physical characteristics, or both). The contribution of each physical characteristic represents an estimate of the impact of each physical characteristic on the presence of the risk factor and / or the disorder.

[0127] In some implementations, the estimate of the impact is an estimate of how much each physical characteristic contributes to the development of the disorder in the individual (or, if the individual has not yet developed the disorder, how much the disorder may develop in the individual). In some implementations, the estimate of the impact is an estimate of how much each physical characteristic contributes to the severity of the disorder in the individual (or, if the individual has not yet developed the disorder, how much each physical characteristic would likely contribute to the severity of the disorder). Thus, compared to physical characteristics with a relatively small contribution, physical characteristics with a relatively large contribution are estimated to contribute more to the development and / or severity of the disorder.

[0128] In some implementations, the contribution is expressed as a percentage (e.g., a given physical characteristic can be responsible for 40% of the development of the disorder in the individual). In other implementations, the contribution is expressed in relative terms. In these implementations, the physical characteristics of the individual (internal, external, or both) can be ranked according to their contribution to the development and / or severity of the disorder.

[0129] The contribution degree can be determined in various different ways. In some implementations, the contribution degree of each body feature is at least partially based on the deviation of the value of each body feature from a certain baseline value. The more the current value of each body feature deviates from the baseline value of the body feature, the greater the contribution degree of each body feature. The baseline value of each body feature can be the previously determined value of each body feature of the individual, the average value of each body feature of multiple other individuals within a group of similar individuals, or other values. The contribution degree of the corresponding body feature can also be determined by comparing the value of the corresponding body feature with a certain metric of the disease in the individual (such as the AHI metric). The contribution degree of each body feature can also be determined by machine learning techniques, such as through a trained model.

[0130] The contribution degree of the body feature can be used for various different purposes, including providing the individual with more information about the disease, generating or updating a treatment plan for the disease, and other purposes. In some implementations, the body features with a contribution degree above a threshold are identified. For example, the analysis of the first image data and / or the second image data can indicate that many body features contribute to the development and / or severity of the disease in the individual (or contribute to the risk of the individual developing the disease). However, for the individual, knowing each single body feature that contributes to the development and / or severity of the disease may not be very useful. Therefore, the body features with a contribution degree above the threshold (for example, the main body features) can be identified and communicated to the individual and / or a third party for developing a treatment plan, etc. In some implementations, the identified body features with a contribution degree above the threshold can be further classified according to whether they are related to the tongue and / or jaw of the individual (for example, inside the mouth of the individual), or whether they are related to the airway of the individual (for example, from the back of the throat of the individual down into the trachea and lungs).

[0131] In some implementations, method 600 includes determining whether the individual can easily modify various different body features. These implementations include making this determination for one or more body features (internal and / or external), for one or more internal body features, for one or more external body features, for one or more body features with a contribution degree above the threshold (internal and / or external), for one or more internal body features with a contribution degree above the threshold, for one or more external body features with a contribution degree above the threshold, or for any other set or subset of body features.

[0132] In some of these embodiments, a physical characteristic is considered modifiable if it can be modified in response to a change in an individual's physical activity regimen, a change in the individual's diet, a change in the individual's medication regimen, or any combination thereof. A change in any of these regimens can include modifying an existing regimen and starting a new regimen. A physical activity regimen can include traditional exercises (such as strength training, cardiovascular exercise, etc.), but can also include other types of physical activity, such as performing breathing exercises and playing a musical instrument (which can help strengthen neck muscles and / or cause hypertrophy of neck muscles). In some implementations, modifiable physical characteristics can include the circumference of an individual's neck, the amount and / or location of body fat in the individual's head and / or neck region, the amount and / or location of muscle in the individual's head and / or neck region (e.g., muscles in the tongue and / or upper airway, whose strengthening can help reduce the severity of a condition), and other characteristics.

[0133] Physical characteristics that are considered non-modifiable generally will not be able to be changed in response to changing a physical activity regimen, a diet regimen, or a medication regimen. However, these physical characteristics may be able to be modified in response to more severe interventions (such as surgery). Non-modifiable physical characteristics can include: the position and / or size of an individual's jaw, the size of an individual's tongue, the distance between an individual's tongue and the top of the individual's mouth, the relative position between an individual's upper and lower teeth, and other characteristics.

[0134] A variety of different physical characteristics can be used to design and recommend a treatment plan for an individual. This process can be based on which characteristics are determined to be modifiable and which are determined to be non-modifiable. For example, if the primary physical characteristics (e.g., those with a relatively large contribution) that contribute to the development and / or severity of a condition in an individual are modifiable (e.g., a large neck circumference due to body fat, weak neck muscles, etc.), then the treatment plan can include less severe interventions, such as exercise and diet changes. If the treatment plan includes using a respiratory therapy system during a sleep session, the treatment plan can recommend using a respiratory therapy system with a lower aggressiveness (e.g., lower) treatment pressure. However, if the primary physical characteristics that contribute to the development and / or severity of a condition in an individual are non-modifiable (e.g., jaw position that contributes to the development of a condition), then the treatment plan can include more invasive aspects, such as a more invasive use of a respiratory therapy system, surgical intervention, etc.

[0135] In some implementations, recommending a treatment plan to an individual can include communicating an explanation of which physical characteristics contribute to the development and / or severity of a condition in the individual, and how the treatment plan can be modified in the future based on those physical characteristics. For example, in certain cases, if an individual is informed that one or more causes of their condition can be more easily modified by following the treatment plan, they may be more likely to accept and / or follow the treatment plan. In other cases, if an individual knows that the intensity of future treatment may be reduced, they may be more willing to start and / or follow the treatment plan because one or more causes of their condition can be regulated through physical activity / diet / drugs. In further cases, letting an individual know that one or more causes of their condition are less controllable by them (e.g., the development of their condition is less attributable to them) can lead to the individual being more likely to accept and / or follow the treatment plan.

[0136] In some implementations, an individual's physical characteristics can be monitored over time to determine whether any physical characteristics (e.g., modifiable physical characteristics) have changed, and whether the current treatment plan can or should be modified. For example, first image data and / or second image data can be generated and analyzed at a first time to determine the individual's risk factors and to determine an initial treatment plan. After a specific amount of time that the individual follows the treatment plan, additional first image data and / or additional second image data can be generated and analyzed to determine whether any of the physical characteristics have changed. If any physical characteristics have changed, the risk factors can be updated based on the changes in the physical characteristics. If the risk factors have been reduced (e.g., the probability that the individual currently has the condition has decreased, or the severity of the individual's current condition has lessened, etc.), the treatment plan can be updated. The treatment plan can be updated to include reducing the intensity of the use of the respiratory treatment system, reducing the drug dosage, reducing the recommended amount of physical activity, relaxing the dietary restrictions, etc.

[0137] In some examples, changes in physical characteristics can indicate that an individual has lost weight. For example, analysis of updated image data can indicate that the individual's neck circumference has decreased and / or that the individual has reduced body fat in their neck. Based on these altered physical characteristics, the treatment plan for the condition can be updated. For example, the treatment plan can be updated to include a lower intensity use of the respiratory therapy system (e.g., using the respiratory therapy system at a lower treatment pressure, or ceasing use of the respiratory therapy system altogether, etc.). In another example, weight loss in an individual may cause the individual to no longer breathe primarily through their mouth during sleep. Thus, the updated treatment plan can include using a nasal mask (which does not cover the individual's mouth and is generally considered to be less difficult to use during sleep) instead of a full-face mask. In another instance, weight loss in an individual may cause the individual to experience OSA only when sleeping in certain positions (e.g., the individual now has positional OSA instead of OSA). In these examples, the updated treatment plan can include using a position adjustment device that is configured to assist the individual in sleeping in a desired position during a sleep session. Generally, the initial treatment plan can include a first set of settings for the respiratory therapy system, a first type of user interface, a first sleep position, etc., and the updated treatment plan can include a second set of settings for the respiratory therapy system, a second type of user interface, a second sleep position, etc.

[0138] In some implementations, method 600 includes determining an individual's body position when generating the first image data and / or the second image data. The risk factor can be at least partially based on the body position. For example, an individual in a particular body position may cause the values of certain physical characteristics to appear to deviate from their actual values, which may affect the accuracy of the risk factor. Thus, by determining the individual's body position, the risk factor can be adjusted as needed to account for apparent deviations in the physical characteristics. Any treatment plan for the individual can also be partially based on the individual's body position.

[0139] In some implementations, at least a portion of the first image data and / or the second image data can be generated while the individual is asleep during one or more sleep sessions. The image data can be analyzed to determine the individual's body position during the sleep session, which in turn can affect any treatment plan recommended to the individual. For example, if the first image data and / or the second image data indicate that the individual is self-compensating during the sleep session (e.g., subconsciously positioning their body in a position that opens their airway, such as tilting their head back), then the risk factor and / or the treatment plan can be updated. In this example, the treatment plan can include recommendations to use a particular type of pillow during subsequent sleep sessions, recommendations to sleep in a particular body position during subsequent sleep sessions, and / or other recommendations.

[0140] In some implementations, method 600 further includes generating acoustic data representing one or more sounds produced by an individual. The risk factors can be at least partially based on the acoustic data. The acoustic data can be analyzed to identify internal and / or external body characteristics, or to assist in identifying internal and / or external characteristics in combination with image data. In some implementations, analyzing the acoustic data includes determining values of one or more acoustic features (e.g., frequency, amplitude, spectrum, cepstrum, etc.) of the acoustic data, comparing the values of the acoustic features to baseline values, and identifying body characteristics based on the comparison. In some implementations, the acoustic data is analyzed to determine the pronunciation or changes in pronunciation of one or more words and / or phrases of the individual. The pronunciation or changes in pronunciation can indicate risk factors and / or various body characteristics of the individual. In some implementations, the acoustic data can be analyzed to determine the fatigue level of the individual, which can be used to determine and / or adjust the risk factors. In some implementations, the acoustic data is generated via passive monitoring of the individual. In other cases, the acoustic data is generated after prompting the individual to produce one or more sounds (e.g., say one or more desired words, phrases, sentences, etc.).

[0141] In some implementations, method 600 (and / or any of the various implementations of method 600 described herein) can be implemented using a system for determining risk factors (such as system 10). The system includes a control system (such as control system 200 of system 10) and a memory (such as memory device 204 of system 10). The control system includes one or more processors (such as processor 202 of control system 200). The memory has machine-readable instructions stored thereon. The control system is coupled to the memory, and when at least one of the one or more processors of the control system desires the machine-readable instructions in the memory, method 600 (and / or any of the various implementations of method 600 described herein) can be implemented.

[0142] Generally, method 600 can be implemented using a system (such as system 10) having a control system (such as control system 200 of system 10) that has one or more processors (such as processor 202 of control system 200) and a memory (e.g., memory device 204 of system 10) storing machine-readable instructions. The control system can be coupled to the memory, and when the machine-readable instructions are executed by at least one processor of the control system, method 600 can be implemented. Method 600 can also be implemented using a computer program product (such as a non-transitory computer-readable medium) including instructions that, when executed by a computer, cause the computer to perform the steps of method 600.

[0143] Alternative implementation

[0144] Alternative Implementation 1. A method for determining risk factors of an individual related to a disease, the method comprising: generating first image data of the interior of the individual's mouth, the interior of the individual's throat, or both, the first image data being related to one or more internal physical characteristics of the individual; and determining the risk factors of the individual related to the disease at least in part based on the first image data.

[0145] Alternative Implementation 2. The method according to Alternative Implementation 1, wherein determining the risk factors includes determining that the individual currently has the disease.

[0146] Alternative Implementation 3. The method according to Alternative Implementation 1 or 2, further comprising generating second image data of the individual's head, the individual's neck, or both, the second image data being related to one or more external physical characteristics of the individual.

[0147] Alternative Implementation 4. The method according to Alternative Implementation 3, wherein the risk factors are determined based on the first image data and the second image data.

[0148] Alternative Implementation 5. The method according to Alternative Implementation 3, wherein the risk factors determined at least in part based on the first image data are initial risk factors, and wherein the method further comprises updating the initial risk factors at least in part based on the second image data.

[0149] Alternative Implementation 6. The method according to Alternative Implementation 5, wherein the updated risk factors are more accurate than the initial risk factors.

[0150] Alternative Implementation 7. The method according to any one of Alternative Implementations 2 to 6, wherein when the individual's mouth is closed, one or more external physical characteristics of the individual are externally visible.

[0151] Alternative Implementation 8. The method according to any one of Alternative Implementations 1 to 7, wherein when the individual's mouth is closed, one or more internal physical characteristics of the individual are not externally visible.

[0152] Alternative Implementation 9. The method according to any one of Alternative Implementations 1 to 8, further comprising determining (i) the contribution of one or more internal physical characteristics, (ii) the contribution of one or more external physical characteristics, or (iii) both (i) and (ii), the contribution of each physical characteristic being an estimate of the impact of each physical characteristic on the presence of the disease.

[0153] Alternative Implementation 10. The method according to Alternative Implementation 9, wherein the estimate of the impact of each physical characteristic on the disease is (i) the contribution of each physical characteristic to the development of the disease in the individual, (ii) the contribution of each physical characteristic to the severity of the disease in the individual, or (iii) an estimate of both (i) and (ii).

[0154] Alternative implementation 11: According to the method of alternative implementation 9 or 10, wherein the contribution degree of each respective physical characteristic is represented as (i) a percentage, (ii) the contribution degree relative to each other physical characteristic, or (iii) both (i) and (ii).

[0155] Alternative implementation 12: According to the method of any one of alternative implementations 9 to 11, further comprising identifying at least one threshold physical characteristic with a contribution degree higher than a threshold.

[0156] Alternative implementation 13: According to the method of alternative implementation 12, further comprising determining whether each threshold physical characteristic is related to (i) the tongue of the individual, the jaw of the individual, or both, or (ii) the airway of the individual.

[0157] Alternative implementation 14: According to the method of any one of alternative implementations 9 to 13, further comprising identifying one or more modifiable physical characteristics from internal physical characteristics and external physical characteristics, and each of the one or more modifiable physical characteristics is modifiable in response to (i) a change in the physical activity program of the individual, (ii) a change in the diet of the individual, (iii) a change in the drug program of the individual, or (iv) any combination of (i) - (iii).

[0158] Alternative implementation 15: According to the method of alternative implementation 14, wherein each of the one or more modifiable physical characteristics of the individual is an external physical characteristic or an internal physical characteristic.

[0159] Alternative implementation 16: According to the method of alternative implementation 14 or 15, wherein the one or more modifiable physical characteristics of the individual include the circumference of the individual's neck, the amount of body fat in the head and neck region of the individual, the location of the body fat in the head and neck region of the individual, the amount of muscle in the head and neck region of the individual, the location of the muscle in the head and neck region of the individual, or any combination thereof.

[0160] Alternative implementation 17: According to the method of any one of alternative implementations 9 to 16, further comprising identifying one or more non - modifiable physical characteristics from internal physical characteristics and external physical characteristics, and the non - modifiable physical characteristics are not modifiable in response to (i) a change in the physical activity program of the individual, (ii) a change in the diet of the individual, (iii) a change in the drug treatment program of the individual, or (iv) any combination of (i) - (iii).

[0161] Alternative implementation 18 According to the method of alternative implementation 17, wherein one or more non-modifiable physical characteristics of the individual include the position of the individual's jaw, the width of the individual's jaw, the height of the individual's tongue, the distance between the individual's tongue and the individual's palate, the relative position between the individual's upper teeth and the individual's lower teeth, or any combination thereof.

[0162] Alternative implementation 19 According to the method of any one of alternative implementations 12 to 18, further comprising determining a treatment plan for the individual based at least in part on at least one physical characteristic whose identified contribution degree is higher than a threshold.

[0163] Alternative implementation 20 According to the method of alternative implementation 19, wherein the treatment plan is further based on the severity of the condition.

[0164] Alternative implementation 21 According to the method of alternative implementation 19 or 20, wherein: in response to at least one threshold physical characteristic including one or more modifiable physical characteristics, the determined treatment plan is a first treatment plan; and in response to at least one threshold physical characteristic not including modifiable physical characteristics, the determined treatment plan is a second treatment plan different from the first treatment plan.

[0165] Alternative implementation 22 According to the method of any one of alternative implementations 1 to 21, further comprising: determining the position of the individual's body when generating the first image data.

[0166] Alternative implementation 23 According to the method of alternative implementation 22, wherein the risk factor is at least partially based on the position of the individual's body.

[0167] Alternative implementation 24 According to the method of alternative implementation 22 or 23, further comprising determining the treatment plan for the individual based at least in part on the position of the individual's body.

[0168] Alternative implementation 25 According to the method of alternative implementation 24, wherein the treatment plan for the individual is further at least partially based on one or more internal physical characteristics of the individual.

[0169] Alternative implementation 26 According to the method of alternative implementation 24 or 25, wherein at least a part of the first image data, the second image data, or both are generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to be used during one or more subsequent sleep sessions, (ii) a recommended body position to be in during one or more subsequent sleep sessions, or (iii) both (i) and (ii).

[0170] Alternative implementation 27. A method according to any one of alternative implementations 1 to 26, wherein the first image data is generated at a first time, and wherein the method further comprises: generating additional first image data at a second time after the first time; and determining a change in one or more external body features of the individual based at least in part on the first image data and the additional first image data.

[0171] Alternative implementation 28. A method according to any one of alternative implementations 2 to 26, wherein the first image data and the second image data are generated at a first time, and wherein the method further comprises: generating additional first image data and second image data at a second time after the first time; and (i) determining a change in one or more external body features of the individual based at least in part on the first image data and the additional first image data, (ii) determining a change in one or more internal body features of the individual based at least in part on the second image data and the additional second image data, or (iii) both (i) and (ii).

[0172] Alternative implementation 29. A method according to alternative implementation 27 or 28, further comprising determining a change in a risk factor based at least in part on (i) a change in one or more external body features of the individual, (ii) a change in one or more internal body features of the individual, or (iii) both (i) and (ii).

[0173] Alternative implementation 30. A method according to any one of alternative implementations 27 to 29, further comprising: determining an initial treatment plan for the individual based at least in part on the first image data, the second image data, or both; and determining an updated treatment plan based at least in part on (i) a change in one or more external body features of the individual, (ii) a change in one or more internal body features of the individual, or (iii) both (i) and (ii).

[0174] Alternative implementation 31. A method according to alternative implementation 30, wherein (i) a change in one or more external body features of the individual, (ii) a change in one or more internal body features of the individual, or (iii) both (i) and (ii) indicate that the individual has experienced weight loss between the first time and the second time.

[0175] Alternative implementation 32. A method according to alternative implementation 31, wherein the initial treatment plan comprises using a respiratory therapy system having a first treatment pressure, and wherein the updated treatment plan comprises using a respiratory therapy system having a second treatment pressure less than the first treatment pressure.

[0176] Alternative implementation 33. A method according to alternative implementation 31 or 32, wherein the initial treatment plan includes using a respiratory therapy system having a first type of user interface, and wherein the updated treatment plan includes using a respiratory therapy system having a second type of user interface different from the first type of user interface.

[0177] Alternative implementation 34. A method according to alternative implementation 33, wherein the first type of user interface is a full-face mask and the second type of user interface is a nasal mask.

[0178] Alternative implementation 35. A method according to any one of alternative implementations 31 to 34, wherein the initial treatment plan includes using a respiratory therapy system, and wherein the updated treatment plan does not include using a respiratory therapy system.

[0179] Alternative implementation 36. A method according to alternative implementation 35, wherein the updated treatment plan includes using a position adjustment device configured to assist an individual in sleeping in a desired position.

[0180] Alternative implementation 37. A method according to any one of alternative implementations 1 to 36, wherein determining a risk factor of an individual associated with a disorder includes inputting first image data, second image data, or both into a trained machine learning model configured to output a risk factor.

[0181] Alternative implementation 38. A method according to any one of alternative implementations 1 to 37, further comprising generating acoustic data representative of one or more sounds produced by an individual, wherein the risk factor is at least partially based on the acoustic data.

[0182] Alternative implementation 39. A method according to alternative implementation 38, further comprising analyzing the acoustic data to identify one or more external body features of the individual, one or more internal body features of the individual, or both.

[0183] Alternative implementation 40. A method according to alternative implementation 38 or 39, further comprising: analyzing the acoustic data to determine values of one or more acoustic features of the acoustic data; comparing the value of each of the one or more acoustic features with a baseline value; and identifying one or more external body features of the individual, one or more internal body features of the individual, or both at least partially based on the comparison.

[0184] Alternative implementation 41. A method according to any one of alternative implementations 38 to 40, further comprising analyzing the acoustic data to determine an individual's pronunciation of at least one of the one or more sounds, the pronunciation indicating a risk factor.

[0185] Alternative implementation 42: The method according to any one of alternative implementations 38 to 41 further includes analyzing acoustic data to determine a change in the pronunciation of an individual for at least one of one or more sounds, and the change in pronunciation indicates a risk factor.

[0186] Alternative implementation 43: The method according to any one of alternative implementations 38 to 42 further includes analyzing acoustic data to determine the fatigue level of an individual, and the fatigue level of the individual indicates a risk factor.

[0187] Alternative implementation 44: The method according to any one of alternative implementations 38 to 43, wherein at least a portion of the acoustic data is generated by passive monitoring of the individual.

[0188] Alternative implementation 45: The method according to any one of alternative implementations 38 to 44, wherein at least a portion of the acoustic data is generated after prompting the individual to produce at least one of one or more sounds.

[0189] Alternative implementation 46: The method according to any one of alternative implementations 38 to 45, wherein the one or more sounds include one or more words, one or more phrases, one or more sentences, or any combination thereof.

[0190] Alternative implementation 47: A system for determining risk factors of a disease, the system includes: a control system including one or more processors; and a memory storing machine-readable instructions thereon; wherein the control system is coupled to the memory, and when the machine-readable instructions in the memory are executed by at least one of the one or more processors of the control system, the method according to any one of alternative implementations 1 to 46 is implemented.

[0191] Alternative implementation 48: A system for determining risk factors of a disease, the system includes a control system having one or more processors configured to implement the method according to any one of alternative implementations 1 to 46.

[0192] Alternative implementation 49: A computer program product including instructions that, when executed by a computer, cause the computer to execute the method according to any one of alternative implementations 1 to 46.

[0193] Alternative implementation 50: The computer program product according to alternative implementation 49, wherein the computer program product is a non-transitory computer-readable medium.

[0194] Alternative implementation 51: A system for determining risk factors of an individual related to a disease, the system includes:

[0195] An electronic interface configured to generate data related to the individual, receive data related to the individual, or both;

[0196] A memory storing machine-readable instructions; and

[0197] A control system including one or more processors configured to execute the machine-readable instructions to:

[0198] Generate first image data of the interior of an individual's mouth, the interior of an individual's throat, or both, the first image data being related to one or more internal physical characteristics of the individual; and

[0199] Determine a risk factor of the individual related to a disorder, at least in part based on the first image data.

[0200] Alternative implementation 52: The system according to alternative implementation 51, wherein determining the risk factor includes determining that the individual currently has a disorder.

[0201] Alternative implementation 53: The system according to alternative implementation 51 or 52, wherein the one or more processors are further configured to execute the machine-readable instructions to generate second image data of the individual's head, the individual's neck, or both, the second image data being related to one or more external body characteristics of the individual.

[0202] Alternative implementation 54: The system according to alternative implementation 53, wherein the risk factor is determined based on the first image data and the second image data.

[0203] Alternative implementation 55: The system according to alternative implementation 53, wherein the risk factor determined at least in part based on the first image data is an initial risk factor, and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor at least in part based on the second image data.

[0204] Alternative implementation 56: The system according to alternative implementation 55, wherein the updated risk factor is more accurate than the initial risk factor.

[0205] Alternative implementation 57: The system according to any one of alternative implementations 52 to 56, wherein one or more external body characteristics of the individual are externally visible when the individual's mouth is closed.

[0206] Alternative implementation 58: The system according to any one of alternative implementations 51 to 57, wherein one or more internal body characteristics of the individual are not externally visible when the individual's mouth is closed.

[0207] Alternative implementation 59. A system according to any one of alternative implementations 51 to 58, wherein one or more processors are further configured to execute machine-readable instructions to determine (i) one or more internal body characteristics, (ii) one or more external body characteristics, or (iii) the contribution degrees of both (i) and (ii), and the contribution degree of each body characteristic is an estimate of the impact of each body characteristic on the presence of the disorder.

[0208] Alternative implementation 60. A system according to alternative implementation 59, wherein the estimate of the impact of each body characteristic on the disorder is (i) the contribution of each body characteristic to the development of the disorder in the individual, (ii) the contribution of each body characteristic to the severity of the disorder in the individual, or (iii) an estimate of both (i) and (ii).

[0209] Alternative implementation 61. A system according to alternative implementation 59 or 60, wherein the contribution degree of each body characteristic is expressed as (i) a percentage, (ii) relative to the contribution degree of each other body characteristic, or (iii) both (i) and (ii).

[0210] Alternative implementation 62. A system according to any one of alternative implementations 59 to 61, wherein one or more processors are further configured to execute machine-readable instructions to identify at least one threshold body characteristic whose contribution degree is higher than a threshold.

[0211] Alternative implementation 63. A system according to alternative implementation 62, wherein one or more processors are further configured to execute machine-readable instructions to determine whether each threshold body characteristic is related to (i) the tongue of the individual, the jaw of the individual, or both, or (ii) the airway of the individual.

[0212] Alternative implementation 64. A system according to any one of alternative implementations 59 to 63, wherein one or more processors are further configured to execute machine-readable instructions to identify one or more modifiable body characteristics from the internal body characteristics and the external body characteristics, and each of the one or more modifiable body characteristics can be modified in response to (i) a change in the physical activity regimen of the individual, (ii) a change in the diet of the individual, (iii) a change in the medication regimen of the individual, or (iv) any combination of (i) - (iii).

[0213] Alternative implementation 65. A system according to alternative implementation 64, wherein each of the one or more modifiable body characteristics of the individual is an external body characteristic or an internal body characteristic.

[0214] Alternative implementation 66: A system according to alternative implementation 64 or 65, wherein one or more modifiable physical characteristics of an individual include the circumference of the individual's neck, the amount of body fat in the individual's head and neck region, the location of body fat in the individual's head and neck region, the amount of muscle in the individual's head and neck region, the location of muscle in the individual's head and neck region, or any combination thereof.

[0215] Alternative implementation 67: A system according to any one of alternative implementations 59 to 66, wherein one or more processors are further configured to execute machine-readable instructions to identify one or more non-modifiable physical characteristics from non-modifiable internal physical characteristics and external physical characteristics in response to (i) a change in the individual's physical activity regimen, (ii) a change in the individual's diet, (iii) a change in the individual's medication regimen, or (iv) any combination of (i) - (iii).

[0216] Alternative implementation 68: A system according to alternative implementation 67, wherein one or more non-modifiable physical characteristics of an individual include the position of the individual's jaw, the width of the individual's jaw, the height of the individual's tongue, the distance between the individual's tongue and the individual's palate, the relative position between the individual's upper teeth and the individual's lower teeth, or any combination thereof.

[0217] Alternative implementation 69: A system according to any one of alternative implementations 62 to 68, wherein one or more processors are further configured to execute machine-readable instructions to determine a treatment plan for the individual based at least in part on at least one physical characteristic having a contribution degree higher than a threshold.

[0218] Alternative implementation 70: A system according to alternative implementation 69, wherein the treatment plan is further based on the severity of the condition.

[0219] Alternative implementation 71: A system according to alternative implementation 69 or 70, wherein: in response to at least one threshold physical characteristic including one or more modifiable physical characteristics, the determined treatment plan is a first treatment plan; and in response to at least one threshold physical characteristic not including modifiable physical characteristics, the determined treatment plan is a second treatment plan different from the first treatment plan.

[0220] Alternative implementation 72: A system according to any one of alternative implementations 51 to 71, wherein one or more processors are further configured to execute machine-readable instructions to determine the position of the individual's body when generating the first image data.

[0221] Alternative implementation 73: A system according to alternative implementation 72, wherein the risk factor is at least partially based on the position of the individual's body.

[0222] Alternative implementation 74: A system according to alternative implementation 72 or 73, wherein one or more processors are further configured to execute machine-readable instructions to determine a treatment plan for an individual at least in part based on the position of the individual's body.

[0223] Alternative implementation 75: A system according to alternative implementation 74, wherein the treatment plan for the individual is further at least in part based on one or more internal body characteristics of the individual.

[0224] Alternative implementation 76: A system according to alternative implementation 74 or 75, wherein at least a portion of the first image data, the second image data, or both is generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to be used during one or more subsequent sleep sessions, (ii) a recommended body position to be in during one or more subsequent sleep sessions, or (iii) both (i) and (ii).

[0225] Alternative implementation 77: A system according to any one of alternative implementations 51 to 76, wherein the first image data is generated at a first time, and wherein one or more processors are further configured to execute machine-readable instructions to: generate additional first image data at a second time after the first time; and determine a change in one or more external body characteristics of the individual at least in part based on the first image data and the additional first image data.

[0226] Alternative implementation 78: A system according to any one of alternative implementations 52 to 76, wherein the first image data and the second image data are generated at a first time, and wherein one or more processors are further configured to execute machine-readable instructions to: generate additional first image data and second image data at a second time after the first time; and (i) determine a change in one or more external body characteristics of the individual at least in part based on the first image data and the additional first image data, (ii) determine a change in one or more internal body characteristics of the individual at least in part based on the second image data and the additional second image data, or (iii) both (i) and (ii).

[0227] Alternative implementation 79: A system according to alternative implementation 77 or 78, wherein one or more processors are further configured to execute machine-readable instructions to determine a change in a risk factor at least in part based on (i) a change in one or more external body characteristics of the individual, (ii) a change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

[0228] Alternative Implementation 80. A system according to any one of alternative implementations 77 to 79, wherein one or more processors are further configured to execute machine-readable instructions to: determine an initial treatment plan for an individual based at least in part on the first image data, the second image data, or both; and determine an updated treatment plan based at least in part on (i) a change in one or more external body characteristics of the individual, (ii) a change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

[0229] Alternative Implementation 81. A system according to alternative implementation 80, wherein (i) a change in one or more external body characteristics of the individual, (ii) a change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii) indicate that the individual has experienced weight loss between a first time and a second time.

[0230] Alternative Implementation 82. A system according to alternative implementation 81, wherein the initial treatment plan includes using a respiratory therapy system having a first treatment pressure, and wherein the updated treatment plan includes using a respiratory therapy system having a second treatment pressure that is less than the first treatment pressure.

[0231] Alternative Implementation 83. A system according to alternative implementation 81 or 82, wherein the initial treatment plan includes using a respiratory therapy system having a first type of user interface, and wherein the updated treatment plan includes using a respiratory therapy system having a second type of user interface that is different from the first type of user interface.

[0232] Alternative Implementation 84. A system according to alternative implementation 83, wherein the first type of user interface is a full-face mask, and the second type of user interface is a nasal mask.

[0233] Alternative Implementation 85. A system according to any one of alternative implementations 81 to 84, wherein the initial treatment plan includes using a respiratory therapy system, and wherein the updated treatment plan does not include using a respiratory therapy system.

[0234] Alternative Implementation 86. A system according to alternative implementation 85, wherein the updated treatment plan includes using a position adjustment device configured to assist the individual in sleeping in a desired position.

[0235] Alternative Implementation 87. A system according to any one of alternative implementations 51 to 86, wherein determining a risk factor of an individual associated with a disorder includes inputting the first image data, the second image data, or both into a trained machine learning model configured to output the risk factor.

[0236] Alternative implementation 88: A system according to any one of alternative implementations 51 to 87, wherein one or more processors are further configured to execute machine-readable instructions to generate acoustic data representing one or more sounds produced by an individual, and wherein a risk factor is at least partially based on the acoustic data.

[0237] Alternative implementation 89: A system according to alternative implementation 88, wherein one or more processors are further configured to execute machine-readable instructions to analyze the acoustic data to identify one or more external body characteristics of the individual, one or more internal body characteristics of the individual, or both.

[0238] Alternative implementation 90: A system according to alternative implementation 88 or 89, wherein one or more processors are further configured to execute machine-readable instructions to: analyze the acoustic data to determine values of one or more acoustic characteristics of the acoustic data; compare the value of each of the one or more acoustic characteristics with a baseline value; and identify one or more external body characteristics of the individual, one or more internal body characteristics of the individual, or both, at least partially based on the comparison.

[0239] Alternative implementation 91: A system according to any one of alternative implementations 88 to 90, wherein one or more processors are further configured to execute machine-readable instructions to analyze the acoustic data to determine the pronunciation of the individual for at least one of the one or more sounds, the pronunciation indicating a risk factor.

[0240] Alternative implementation 92: A system according to any one of alternative implementations 88 to 91, wherein one or more processors are further configured to execute machine-readable instructions to analyze the acoustic data to determine a change in the pronunciation of the individual for at least one of the one or more sounds, the change in pronunciation indicating a risk factor.

[0241] Alternative implementation 93: A system according to any one of alternative implementations 88 to 92, wherein one or more processors are configured to execute machine-readable instructions to analyze the acoustic data to determine the fatigue level of the individual, the fatigue level of the individual indicating a risk factor.

[0242] Alternative implementation 94: A system according to any one of alternative implementations 88 to 93, wherein at least a portion of the acoustic data is generated by passive monitoring of the individual.

[0243] Alternative implementation 95: A system according to any one of alternative implementations 88 to 94, wherein at least a portion of the acoustic data is generated after prompting the individual to produce at least one of one or more sounds.

[0244] Alternative implementation 96: A system according to any one of alternative implementations 88 to 95, wherein one or more sounds include one or more words, one or more phrases, one or more sentences, or any combination thereof.

[0245] One or more elements or aspects or steps, or any part thereof, from any one or more of the alternative implementations and / or claims herein, may be combined with one or more elements or aspects or steps, or any part thereof, from any one or more of any other alternative implementations and / or claims herein, or a combination thereof, to form one or more additional implementations and / or claims of the present disclosure.

[0246] Although the present disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes can be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and its obvious variations are considered to fall within the spirit and scope of the present disclosure. It is also contemplated that additional implementations in accordance with aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims

1. A method for determining a risk factor of an individual associated with a disorder, the method comprising: generating first image data of the interior of the mouth of the individual, the interior of the throat of the individual, or both, the first image data being related to one or more internal body features of the individual; and determining the risk factor of the individual associated with the disorder at least in part based on the first image data.

2. The method according to claim 1, wherein determining the risk factor includes determining that the individual currently has the disorder.

3. The method according to claim 1 or 2, further comprising generating second image data of the head of the individual, the neck of the individual, or both, the second image data being related to one or more external body features of the individual.

4. The method according to claim 3, wherein the risk factor is determined based on the first image data and the second image data.

5. The method according to claim 3, wherein the risk factor determined at least in part based on the first image data is an initial risk factor, and wherein the method further comprises updating the initial risk factor at least in part based on the second image data.

6. The method according to claim 5, wherein the updated risk factor is more accurate than the initial risk factor.

7. The method according to any one of claims 2 to 6, wherein when the mouth of the individual is closed, the one or more external body features of the individual are externally visible.

8. The method according to any one of claims 1 to 7, wherein when the mouth of the individual is closed, the one or more internal body features of the individual are not externally visible.

9. The method according to any one of claims 1 to 8, further comprising determining the contribution of (i) the one or more internal body features, (ii) the one or more external body features, or (iii) both (i) and (ii), the contribution of each body feature being an estimate of the impact of the respective body feature on the presence of the disorder.

10. The method according to claim 9, wherein the estimate of the impact of each body feature on the disorder is (i) the contribution of the respective body feature to the development of the disorder in the individual, (ii) the contribution of the respective body feature to the severity of the disorder in the individual, or (iii) an estimate of both (i) and (ii).

11. The method according to claim 9 or 10, wherein the contribution of each respective body feature is expressed as (i) a percentage, (ii) relative to the contribution of each other body feature, or (iii) both (i) and (ii).

12. The method according to any one of claims 9 to 11, further comprising identifying at least one threshold body feature having a contribution above a threshold.

13. The method according to claim 12, further comprising determining whether each threshold body feature is related to (i) the tongue of the individual, the jaw of the individual, or both, or (ii) the airway of the individual.

14. The method according to any one of claims 9 to 13, further comprising identifying one or more modifiable body features from the internal body features and the external body features, each of the one or more modifiable body features being modifiable in response to (i) a change in the individual's physical activity regimen, (ii) a change in the individual's diet, (iii) a change in the individual's medication regimen, or (iv) any combination of (i) - (iii).

15. The method according to claim 14, wherein each of the one or more modifiable body features of the individual is an external body feature or an internal body feature.

16. The method according to claim 14 or 15, wherein the one or more modifiable body features of the individual include the circumference of the individual's neck, the amount of body fat in the individual's head and neck region, the location of the body fat in the individual's head and neck region, the amount of muscle in the individual's head and neck region, the location of the muscle in the individual's head and neck region, or any combination thereof.

17. The method according to any one of claims 9 to 16, further comprising identifying one or more non-modifiable body features from the internal body features and the external body features, the non-modifiable body features being non-modifiable in response to (i) a change in the individual's physical activity regimen, (ii) a change in the individual's diet, (iii) a change in the individual's medication regimen, or (iv) any combination of (i) - (iii).

18. The method according to claim 17, wherein the one or more non-modifiable body features of the individual include the position of the individual's jaw, the width of the individual's jaw, the height of the individual's tongue, the distance between the individual's tongue and the individual's palate, the relative position between the individual's upper teeth and the individual's lower teeth, or any combination thereof.

19. The method according to any one of claims 12 to 18, further comprising determining a treatment plan for the individual based at least in part on at least one body feature whose identified contribution degree is higher than the threshold.

20. The method according to claim 19, wherein the treatment plan is further based on the severity of the disease.

21. The method according to claim 19 or 20, wherein: in response to the at least one threshold body feature including one or more modifiable body features, the determined treatment plan is a first treatment plan; and in response to the at least one threshold body feature not including modifiable body features, the determined treatment plan is a second treatment plan different from the first treatment plan.

22. The method according to any one of claims 1 to 21, further comprising: determining the position of the individual's body when generating the first image data.

23. The method according to claim 22, wherein the risk factor is at least partially based on the position of the individual's body.

24. The method according to claim 22 or 23, further comprising determining the individual's treatment plan at least in part based on the individual's body position.

25. The method according to claim 24, wherein the individual's treatment plan is further at least in part based on the individual's one or more internal body characteristics.

26. The method according to claim 24 or 25, wherein at least a portion of the first image data, the second image data, or both are generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to be used during one or more subsequent sleep sessions, (ii) a recommended body position to be in during the one or more subsequent sleep sessions, or (iii) both (i) and (ii).

27. The method according to any one of claims 1 to 26, wherein the first image data is generated at a first time, and wherein the method further comprises: generating additional first image data at a second time after the first time; and determining a change in the one or more external body characteristics of the individual at least in part based on the first image data and the additional first image data.

28. The method according to any one of claims 2 to 26, wherein, the first image data and the second image data are generated at a first time, and wherein the method further comprises: generating additional first image data and second image data at a second time after the first time; and (i) determining a change in the one or more external body characteristics of the individual at least in part based on the first image data and the additional first image data, (ii) determining a change in the one or more internal body characteristics of the individual at least in part based on the second image data and the additional second image data, or (iii) both (i) and (ii).

29. The method according to claim 27 or 28, further comprising determining a change in the risk factor at least in part based on (i) a change in the one or more external body characteristics of the individual, (ii) a change in the one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

30. The method according to any one of claims 27 to 29, further comprising: determining an initial treatment plan for the individual at least in part based on the first image data, the second image data, or both; and determining an updated treatment plan at least in part based on (i) a change in the one or more external body characteristics of the individual, (ii) a change in the one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

31. The method according to claim 30, wherein (i) the change in the one or more external body characteristics of the individual, (ii) the change in the one or more internal body characteristics of the individual, or (iii) both (i) and (ii) indicate that the individual has experienced weight loss between the first time and the second time.

32. The method according to claim 31, wherein the initial treatment plan includes using a respiratory therapy system having a first treatment pressure, and wherein the updated treatment plan includes using the respiratory therapy system having a second treatment pressure that is less than the first treatment pressure.

33. The method according to claim 31 or 32, wherein the initial treatment plan includes using a respiratory therapy system having a first type of user interface, and wherein the updated treatment plan includes using the respiratory therapy system having a second type of user interface that is different from the first type of user interface.

34. The method according to claim 33, wherein the first type of user interface is a full-face mask, and the second type of user interface is a nasal mask.

35. The method according to any one of claims 31 to 34, wherein the initial treatment plan includes using a respiratory therapy system, and wherein the updated treatment plan does not include using the respiratory therapy system.

36. The method according to claim 35, wherein the updated treatment plan includes using a position adjustment device configured to assist the individual in sleeping in a desired position.

37. The method according to any one of claims 1 to 36, wherein determining the risk factor of the individual associated with the condition includes inputting the first image data, the second image data, or both into a trained machine learning model configured to output the risk factor.

38. The method according to any one of claims 1 to 37, further comprising generating acoustic data representing one or more sounds produced by the individual, wherein the risk factor is at least partially based on the acoustic data.

39. The method according to claim 38, further comprising analyzing the acoustic data to identify the one or more external body characteristics of the individual, the one or more internal body characteristics of the individual, or both.

40. The method according to claim 38 or 39, further comprising: analyzing the acoustic data to determine values of one or more acoustic characteristics of the acoustic data; comparing the value of each of the one or more acoustic characteristics with a baseline value; and identifying the one or more external body characteristics of the individual, the one or more internal body characteristics of the individual, or both at least partially based on the comparison.

41. The method according to any one of claims 38 to 40, further comprising analyzing the acoustic data to determine the pronunciation of the individual for at least one of the one or more sounds, the pronunciation indicating the risk factor.

42. The method according to any one of claims 38 to 41, further comprising analyzing the acoustic data to determine a change in the pronunciation of at least one of the one or more sounds by the individual, the change in pronunciation indicating the risk factor.

43. The method according to any one of claims 38 to 42, further comprising analyzing the acoustic data to determine the fatigue level of the individual, the fatigue level of the individual indicating the risk factor.

44. The method according to any one of claims 38 to 43, wherein at least a portion of the acoustic data is generated by passive monitoring of the individual.

45. The method according to any one of claims 38 to 44, wherein at least a portion of the acoustic data is generated after prompting the individual to produce at least one of the one or more sounds.

46. The method according to any one of claims 38 to 45, wherein the one or more sounds include one or more words, one or more phrases, one or more sentences, or any combination thereof.

47. A system for determining a risk factor for a medical condition, the system comprising: a control system including one or more processors; and a memory storing machine-readable instructions; wherein the control system is coupled to the memory, and when the machine-readable instructions in the memory are executed by at least one of the one or more processors of the control system, the method according to any one of claims 1 to 46 is implemented.

48. A system for determining a risk factor for a medical condition, the system including a control system having one or more processors configured to implement the method according to any one of claims 1 to 46.

49. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 46.

50. The computer program product according to claim 49, wherein the computer program product is a non-transitory computer-readable medium.

51. A system for determining a risk factor for an individual associated with a medical condition, the system comprising: an electronic interface configured to generate data related to the individual, receive data related to the individual, or both; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to: generate first image data of the interior of the individual's mouth, the interior of the individual's throat, or both, the first image data being related to one or more internal physical characteristics of the individual; and determine the risk factor for the individual associated with the medical condition at least in part based on the first image data.

52. The system according to claim 51, wherein determining the risk factor includes determining that the individual currently has the medical condition.

53. The system according to claim 51 or 52, wherein the one or more processors are further configured to execute the machine-readable instructions to generate second image data of the head of the individual, the neck of the individual, or both, the second image data being related to one or more external body features of the individual.

54. The system according to claim 53, wherein the risk factor is determined based on the first image data and the second image data.

55. The system according to claim 53, wherein the risk factor determined at least in part based on the first image data is an initial risk factor, and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor at least in part based on the second image data.

56. The system according to claim 55, wherein the updated risk factor is more accurate than the initial risk factor.

57. The system according to any one of claims 52 to 56, wherein when the mouth of the individual is closed, the one or more external body features of the individual are externally visible.

58. The system according to any one of claims 51 to 57, wherein when the mouth of the individual is closed, the one or more internal body features of the individual are not externally visible.

59. The system according to any one of claims 51 to 58, wherein the one or more processors are further configured to execute the machine-readable instructions to determine (i) the contribution of the one or more internal body features, (ii) the contribution of the one or more external body features, or (iii) the contribution of both (i) and (ii), the contribution of each body feature being an estimate of the impact of the respective body feature on the presence of the disorder.

60. The system according to claim 59, wherein the estimate of the impact of each body feature on the disorder is (i) the contribution of the respective body feature to the development of the disorder in the individual, (ii) the contribution of the respective body feature to the severity of the disorder in the individual, or (iii) an estimate of both (i) and (ii).

61. The system according to claim 59 or 60, wherein the contribution of each respective body feature is expressed as (i) a percentage, (ii) the contribution relative to the contribution of each other body feature, or (iii) both (i) and (ii).

62. The system according to any one of claims 59 to 61, wherein the one or more processors are further configured to execute the machine-readable instructions to identify at least one threshold body feature whose contribution is higher than a threshold.

63. The system according to claim 62, wherein the one or more processors are further configured to execute the machine-readable instructions to determine whether each threshold body feature is related to (i) the tongue of the individual, the jaw of the individual, or both, or (ii) the airway of the individual.

64. The system according to any one of claims 59 to 63, wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more modifiable body features from the internal body features and the external body features, and each of the one or more modifiable body features is modifiable in response to (i) a change in the individual's physical activity regimen, (ii) a change in the individual's diet, (iii) a change in the individual's medication regimen, or (iv) any combination of (i) - (iii).

65. The system according to claim 64, wherein each of the one or more modifiable body features of the individual is an external body feature or an internal body feature.

66. The system according to claim 64 or 65, wherein the one or more modifiable body features of the individual include the circumference of the individual's neck, the amount of body fat in the individual's head and neck region, the location of the body fat in the individual's head and neck region, the amount of muscle in the individual's head and neck region, the location of the muscle in the individual's head and neck region, or any combination thereof.

67. The system according to any one of claims 59 to 66, wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more non-modifiable body features from the non-modifiable internal body features and the external body features in response to (i) a change in the individual's physical activity regimen, (ii) a change in the individual's diet, (iii) a change in the individual's medication regimen, or (iv) any combination of (i) - (iii).

68. The system according to claim 67, wherein the one or more non-modifiable body features of the individual include the position of the individual's jaw, the width of the individual's jaw, the height of the individual's tongue, the distance between the individual's tongue and the individual's palate, the relative position between the individual's upper teeth and the individual's lower teeth, or any combination thereof.

69. The system according to any one of claims 62 to 68, wherein the one or more processors are further configured to execute the machine-readable instructions to determine a treatment plan for the individual based at least in part on at least one body feature whose identified contribution degree is higher than the threshold.

70. The system according to claim 69, wherein the treatment plan is further based on the severity of the disease.

71. The method according to claim 69 or 70, wherein: in response to the at least one threshold body feature including one or more modifiable body features, the determined treatment plan is a first treatment plan; and in response to the at least one threshold body feature not including modifiable body features, the determined treatment plan is a second treatment plan different from the first treatment plan.

72. The system according to any one of claims 51 to 71, wherein the one or more processors are further configured to execute the machine-readable instructions to determine the position of the individual's body when generating the first image data.

73. The system according to claim 72, wherein the risk factor is at least partially based on the position of the individual's body.

74. The system according to claim 72 or 73, wherein the one or more processors are further configured to execute the machine-readable instructions to determine a treatment plan for the individual at least partially based on the position of the individual's body.

75. The system according to claim 74, wherein the treatment plan for the individual is further at least partially based on the one or more internal body characteristics of the individual.

76. The system according to claim 74 or 75, wherein at least a portion of the first image data, the second image data, or both are generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to be used during one or more subsequent sleep sessions, (ii) a recommended body position to be in during the one or more subsequent sleep sessions, or (iii) both (i) and (ii).

77. The system according to any one of claims 51 to 76, wherein the first image data is generated at a first time, and wherein the one or more processors are further configured to execute the machine-readable instructions to: generate additional first image data at a second time after the first time; and determine a change in one or more external body characteristics of the individual at least partially based on the first image data and the additional first image data.

78. The system according to any one of claims 52 to 76, wherein the first image data and the second image data are generated at a first time, and wherein the one or more processors are further configured to execute the machine-readable instructions to: generate additional first image data and second image data at a second time after the first time; and (i) determine a change in one or more external body characteristics of the individual at least partially based on the first image data and the additional first image data, (ii) determine a change in one or more internal body characteristics of the individual at least partially based on the second image data and the additional second image data, or (iii) both (i) and (ii).

79. The system according to claim 77 or 78, wherein the one or more processors are further configured to execute the machine-readable instructions to determine a change in the risk factor at least partially based on (i) a change in one or more external body characteristics of the individual, (ii) a change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

80. The system according to any one of claims 77 to 79, wherein the one or more processors are further configured to execute the machine-readable instructions to: Determine an initial treatment plan for the individual based at least in part on the first image data, the second image data, or both; and Determine an updated treatment plan based at least in part on (i) a change in one or more external body characteristics of the individual, (ii) a change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii).

81. The system according to claim 80, wherein (i) the change in one or more external body characteristics of the individual, (ii) the change in one or more internal body characteristics of the individual, or (iii) both (i) and (ii) indicate that the individual has experienced weight loss between the first time and the second time.

82. The system according to claim 81, wherein the initial treatment plan includes using a respiratory therapy system having a first treatment pressure, and wherein the updated treatment plan includes using the respiratory therapy system having a second treatment pressure that is less than the first treatment pressure.

83. The system according to claim 81 or 82, wherein the initial treatment plan includes using a respiratory therapy system having a first type of user interface, and wherein the updated treatment plan includes using the respiratory therapy system having a second type of user interface that is different from the first type of user interface.

84. The system according to claim 83, wherein the first type of user interface is a full-face mask, and the second type of user interface is a nasal mask.

85. The system according to any one of claims 81 to 84, wherein the initial treatment plan includes using a respiratory therapy system, and wherein the updated treatment plan does not include using the respiratory therapy system.

86. The system according to claim 85, wherein the updated treatment plan includes using a position adjustment device configured to assist the individual in sleeping in a desired position.

87. The system according to any one of claims 51 to 86, wherein determining the risk factor of the individual associated with the disorder includes inputting the first image data, the second image data, or both into a trained machine learning model configured to output the risk factor.

88. The system according to any one of claims 51 to 87, wherein the one or more processors are further configured to execute the machine-readable instructions to generate acoustic data representing one or more sounds produced by the individual, wherein, the risk factor is at least partially based on the acoustic data.

89. The system according to claim 88, wherein the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to identify one or more external physical characteristics of the individual, one or more internal physical characteristics of the individual, or both.

90. The system according to claim 88 or 89, wherein the one or more processors are further configured to execute the machine-readable instructions to: analyze the acoustic data to determine values of one or more acoustic characteristics of the acoustic data; compare the value of each of the one or more acoustic characteristics with a baseline value; and identify one or more external physical characteristics of the individual, one or more internal physical characteristics of the individual, or both, at least in part based on the comparison.

91. The system according to any one of claims 88 to 90, wherein the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine the pronunciation of the individual for at least one of the one or more sounds, the pronunciation indicating the risk factor.

92. The system according to any one of claims 88 to 91, wherein the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a change in the pronunciation of the individual for at least one of the one or more sounds, the change in pronunciation indicating the risk factor.

93. The system according to any one of claims 88 to 92, wherein the one or more processors are configured to execute the machine-readable instructions to analyze the acoustic data to determine the fatigue level of the individual, the fatigue level of the individual indicating the risk factor.

94. The system according to any one of claims 88 to 93, wherein at least a portion of the acoustic data is generated by passive monitoring of the individual.

95. The system according to any one of claims 88 to 94, wherein at least a portion of the acoustic data is generated after prompting the individual to produce at least one of the one or more sounds.

96. The system according to any one of claims 88 to 95, wherein the one or more sounds include one or more words, one or more phrases, one or more sentences, or any combination thereof.

Images