Selection systems and methods for selecting intraoral devices

Abstract

The present disclosure relates to a system for selecting an intraoral device for a patient. The system comprises a data communication interface in communication with a scanning system and configured to receive mouth feature data of the patient, a memory storing machine readable instructions, and a processing system including one or more processors configured to execute the machine readable instructions. The processing system comprises a model generating module configured to process the mouth feature data to generate a model of the patient's mouth, and a selecting module configured to select a template intraoral device from a plurality of template intraoral devices, based on the generated model. The present disclosure also relates to a method for selecting an intraoral device for a patient.

Description

SELECTION SYSTEMS AND METHODS FOR SELECTINGINTRAORAL DEVICES1.1 FIELD OF THE TECHNOLOGY

[0001] The present technology relates to preventing and / or treating sleep disorders such as snoring and / or obstructive sleep apnea. In particular, the present technology relates to a system and a method for selecting an intraoral orthosis device, such as a mandibular repositioning device (MRD) or mandibular advancement device (MAD), for a patient for treating and / or preventing sleep disorders such as snoring and / or obstructive sleep apnea.1.2 DESCRIPTION OF THE RELATED ART1.2.1 Human Respiratory System and its Disorders

[0002] The respiratory system of the body facilitates gas exchange. The nose and mouth form the entrance to the airways of a patient.

[0003] The airways include a series of branching tubes, which become narrower, shorter and more numerous as they penetrate deeper into the lung. The prime function of the lung is gas exchange, allowing oxygen to move from the inhaled air into the venous blood and carbon dioxide to move in the opposite direction. The trachea divides into right and left main bronchi, which further divide eventually into terminal bronchioles. The bronchi make up the conducting airways, and do not take part in gas exchange. Further divisions of the airways lead to the respiratory bronchioles, and eventually to the alveoli. The alveolated region of the lung is where the gas exchange takes place and is referred to as the respiratory zone. See “Respiratory Physiology” , by John B. West, Lippincott Williams & Wilkins, 9th edition published in 2012.

[0004] A range of respiratory disorders exist. Certain disorders may be characterised by particular events, e.g. apneas, hypopneas, and hyperpneas.

[0005] Examples of respiratory disorders include Obstructive Sleep Apnea (OSA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hypoventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD) and Chest wall disorders.

[0006] Chronic snoring is a condition affecting a considerable proportion of the population, estimated at 40% by some studies. During sleep, the patient's throat muscles relax, causing a narrowing of the pharynx. The consequence of this narrowing is an increase in the speed of the inhaled air caused by a venturi-type effect. The air excites the flexible part of the soft palate and uvula and these begin to vibrate noisily. The noise created in this way can reach up to 90 decibels.

[0007] Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterised by events including occlusion or obstruction of the upper air passage during sleep. It results from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall during sleep. The condition causes the affected patient to stop breathing for periods typically of 30 to 120 seconds in duration, sometimes 200 to 300 times per night. It often causes excessive daytime somnolence, and it may cause cardiovascular disease and brain damage. The syndrome is a common disorder, particularly in middle aged overweight males, although a person affected may have no awareness of the problem, e.g. see US Patent No. 4,944,310 (Sullivan).

[0008] A patient with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise.

[0009] Obesity Hypoventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness.

[0010] A range of therapies have been used to treat or ameliorate such conditions. Furthermore, otherwise healthy individuals may take advantage of such therapies to prevent respiratory disorders from arising. However, these can have a number of shortcomings.1.2.2 Therapies

[0011] Various therapies, such as Continuous Positive Airway Pressure (CPAP) therapy, Non-invasive ventilation (NIV), Invasive ventilation (IV), and High Flow Therapy (HFT) have been used to treat one or more of the above respiratory disorders.1.2.2.1 Respiratory pressure therapies

[0012] Respiratory pressure therapy is the application of a supply of air to an entrance to the airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the patient’s breathing cycle (in contrast to negative pressure therapies such as the tank ventilator or cuirass).

[0013] Such respiratory therapies may be provided by a respiratory therapy system or device. Such systems and devices may also be used to screen, diagnose, or monitor a condition without treating it.

[0014] Continuous Positive Airway Pressure (CPAP) therapy has been used to treat Obstructive Sleep Apnea (OSA). The mechanism of action is hypothesized to be that continuous positive airway pressure acts as a pneumatic splint and may prevent upper airway occlusion, such as by pushing the soft palate and tongue forward and away from the posterior oropharyngeal wall. Treatment of OSA by CPAP therapy may be voluntary, and, hence, patients may elect not to comply with therapy. For example, patients may find devices used, such as CPAP masks, to provide therapy that is one or more of: uncomfortable, difficult to use, expensive and aesthetically unappealing. If a mask is uncomfortable, or difficult to use, a patient may not comply with therapy. Since it is often recommended that a patient regularly wash their CPAP mask, if a mask is difficult to clean (e.g., difficult to assemble or disassemble), patients may not clean their mask and this may impact on patient compliance. Additionally, some patients do not tolerate CPAP therapy well, so alternative therapies are available.

[0015] Another form of therapy system is a mandibular repositioning device.1.2.2.2 Mandibular repositioning

[0016] A mandibular repositioning device (MRD) or mandibular advancement device (MAD) (hereafter referred as a MAD for convenience) is one of the treatment options for sleep apnea and snoring. It is an adjustable intraoral appliance, available from a dentist or other supplier, which holds the lower jaw (mandible) in a forward position during sleep. The MAD is a removable device that a patient inserts into their mouth, prior to going to sleep, and removes following sleep. Thus, the MAD is not designed to be worn all the time. The MAD may be custom made or produced in a standard form and include a bite impression portion designed to allow fitting to a patient's teeth. The mechanical protrusion of the lower jaw expands the space behind the tongue, puts tension on the pharyngeal walls to reduce collapse of the airway and diminish palate vibration.

[0017] In certain examples, a mandibular advancement device may comprise an upper splint that is intended to engage with or fit over teeth on the upper jaw or maxilla and a lower splint that is intended to engage with or fit over teeth on the lower jaw or mandible. The upper and lower splints are connected together laterally via a pair of connecting rods. The pair of connecting rods are fixed symmetrically on the upper splint and on the lower splint.

[0018] In such a design, the length of the connecting rods is selected such that, when the MAD is placed in a patient’s mouth, the mandible is held in an advanced position. The length of the connecting rods may be adjusted to change the level of protrusion of the mandible. A dentist may determine a level of protrusion for the mandible that will determine the length of the connecting rods.

[0019] Some MADs are structured to push the mandible forward relative to the maxilla while other MADs, such as the ResMed Narval CC™ MAD, are designed to retain the mandible in a forward position. This device also reduces or minimises dental and temporo-mandibular joint (TMJ) side effects. Thus, it is configured to minimise or prevent any movement of one or more of the teeth by the applied pressure. For instance, document US2005016547 discloses a MAD with an upper groove and a lower groove designed to align respectively with the upper jaw and thelower jaw. The grooves are linked together by two tie rods of such length that the lower jaw is maintained in an extended position relative to the upper jaw.1.2.2.3 Bruxism treatment

[0020] Bruxism is the excessive grinding of the teeth and / or excessive clenching of the jaw. Some treatment devices known as occlusal splints cover the teeth of the upper and / or lower jaw to mechanically protect them. There are available intraoral devices including partial or full-coverage splints, i.e., splints fitting over some or all of the teeth. They are typically made of plastic (e.g., acrylic) and can be hard or soft. A lower appliance can be worn alone, or in combination with an upper appliance.1.2.3 Screening, Diagnosis, and Monitoring Systems

[0021] Polysomnography (PSG) is a conventional system for diagnosis and monitoring of cardio-pulmonary disorders, and typically involves expert clinical staff to apply the system. PSG typically involves the placement of 15 to 20 contact sensors on a patient in order to record various bodily signals such as electroencephalography (EEG), electrocardiography (ECG), electrooculograpy (EOG), electromyography (EMG), etc. PSG for sleep disordered breathing has involved two nights of observation of a patient in a clinic, one night of pure diagnosis and a second night of titration of treatment parameters by a clinician. PSG is therefore expensive and inconvenient. In particular, it is unsuitable for home screening / diagnosis / monitoring of sleep disordered breathing.

[0022] Screening and diagnosis generally describe the identification of a condition from its signs and symptoms. Screening typically gives a true / false result indicating whether or not a patient’s SDB is severe enough to warrant further investigation, while diagnosis may result in clinically actionable information. Screening and diagnosis tend to be one-off processes, whereas monitoring the progress of a condition can continue indefinitely. Some screening / diagnosis systemsare suitable only for screening / diagnosis, whereas some may also be used for monitoring.

[0023] Clinical experts may be able to screen, diagnose, or monitor patients adequately based on visual observation of PSG signals. However, there are circumstances where a clinical expert may not be available, or a clinical expert may not be affordable. Different clinical experts may disagree on a patient’s condition. In addition, a given clinical expert may apply a different standard at different times.

[0024] An intraoral device for a patient is fitted by a trained individual, such as a dentist or a physician. Typically, a patient needing an intraoral device to begin or continue therapy visits the trained individual at an accommodating facility where a series of measurements are made in an effort to determine an appropriate intraoral device size from standard template sizes. An appropriate size is intended to mean a particular combination of dimensions of certain features which provide adequate comfort and efficacy in therapy. Sizing in this way is not only labour intensive but also inconvenient. The inconvenience of taking time out of a busy schedule or, in some instances, having to travel great distances is only an example of barriers to many patients receiving a new or replacement device and, ultimately, treatment. Nevertheless, the selection of an appropriate size for an intraoral device is important for treatment quality and compliance.2 BRIEF SUMMARY OF THE TECHNOLOGY

[0025] Disclosed is a system for selecting an intraoral device for use in treatment of a sleep disorder for a patient. The system may be configured to select an intraoral device based on mouth feature data of the patient.

[0026] The system may comprise a data communication interface. The data communication interface may be in communication with a scanning system. The data communication interface in communication with the scanning system may be configured to receive mouth feature data of the patient.

[0027] The system may also comprise a memory storing machine readable instructions. The system may also comprise a processing system. The processing system may include one or more processors. The one or more processors may beconfigured to execute the machine readable instructions. The processing system may also comprise a model generating module. The module generating module may be configured to process the mouth feature data to generate a model of the patient’s mouth. The processing system may also comprise a selecting module. The selecting module may be configured to select a template intraoral device from a plurality of template intraoral devices, based on the generated model.

[0028] For example, the selecting module may be configured to select an intraoral device size that may be appropriate for the patient based on the generated model of their mouth.

[0029] In one embodiment, the selecting module may be configured to correlate at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

[0030] In one embodiment, the model generating module may be configured to determine a measurement from mouth feature data of at least one reference feature of the patient from a group of reference features. The at least one reference feature may comprise a jaw width, a jaw length, a profile depth and a dental arch profile.

[0031] In one embodiment, the system may further comprise a scanning system including a scanning device. The scanning device may be configured to capture the mouth feature data of the patient.

[0032] In one embodiment, the mouth feature data may be based on image data acquired from the scanning system.

[0033] In one embodiment, the scanning device may be a mobile device with an application configured to capture mouth features.

[0034] In one embodiment, the scanning system may comprise a user interface configured for providing guidance to the patient in acquiring the mouth feature data.

[0035] In one embodiment, the system may further comprise a patient data collection interface configured to collect patient input data from the patient.

[0036] In one embodiment, the system may further comprise a data communication interface configured to receive data of the patient. The model generating module and / or the selecting module may be configured to process the patient data to generate the model of the patient’s mouth and / or to select a template intraoral device from a plurality of template intraoral devices.

[0037] In one embodiment, the data of the patient may comprise one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

[0038] In one embodiment, the self-reported information may be objective data entered by the patient and may include one or more of age, gender, weight, and height.

[0039] In one embodiment, self-reported information may be subjective data entered by the patient in relation to a previous intraoral device worn by the patient.

[0040] In one embodiment, the self-reported information may include feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

[0041] In one embodiment, the self-reported data may be collected by displaying questions in an interface on a mobile device.

[0042] In one embodiment, the self-reported data may be acquired from the patient data collection interface.

[0043] In one embodiment, the system may further comprise a machine learning module operable to determine mouth feature data and patient data from multiple patients correlated with the mouth model generating module and / or selecting module to adjust a characteristic of the mouth model generating module and / or selecting module.

[0044] In one embodiment, the patient data may be acquired from the multiple patients.

[0045] Also disclosed is a system for determining a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient. For example, the system can determine particular design characteristics of the intraoral device. The system can comprise a data communication interface. The data communication interface can be in communication with a scanning system. The data communication interface in communication with a scanning system can be configured to receive mouth feature data of the patient.

[0046] The system can also comprise a memory storing machine readable instructions. The system can also comprise a processing system. The processing system can include one or more processors. The one or more processors can be configured to execute the machine readable instructions. The processing system can comprise a model generating module. The model generating module can be configured to process the mouth feature data to generate a model of the patient’s mouth. The processing system can also comprise a specifying module. The specifying module can be configured to specify at least one characteristic of the intraoral device based on the generated model.

[0047] In one embodiment, the data communication interface may be configured to receive data of the patient. The model generating module and / or specifying module may be configured to process the patient data to generate the model of the patient’s mouth and / or to specify at least one characteristic of the intraoral device based on the patient data.

[0048] In one embodiment, the data of the patient may comprise one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

[0049] In one embodiment, the self-reported information may be objective data entered by the patient and includes one or more of age, gender, weight, and height.

[0050] In one embodiment, the self-reported information may be subjective data entered by the patient in relation to a previous intraoral device worn by the patient.

[0051] In one embodiment, the self-reported information may include feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

[0052] In one embodiment, the self- reported data may be collected by displaying questions in an interface on a mobile device.

[0053] In one embodiment, the system may further comprise a patient data collection interface to acquire the self reported information.

[0054] In one embodiment, the system may further comprise a machine learning module operable to determine mouth feature data and patient data from multiple patients correlated with the mouth model generating module and / or specifying module to adjust a characteristic of the mouth model generating module and / or specifying module.

[0055] In one embodiment, the patient data may be acquired from the multiple patients.

[0056] Also disclosed is a system for obtaining feedback from a patient using an intraoral device for treatment of a sleep disorder. The system can comprise a data communication interface. The data communication interface can be in communication with a scanning system. The data communication interface in communication with the scanning system can be configured to receive mouth feature data of the patient.

[0057] The system can also comprise a memory storing machine readable instructions. The system can also comprise a processing system. The processing system can include one or more processors. The one or more processors can be configured to execute the machine readable instructions. The processing system can comprise a model generating module. The model generating module can be configured to process the mouth feature data to generate a model of the patient’s mouth. The processing system can also comprise an analysis module. The analysis module can be configured to correlate at least one characteristic of the mouth feature model to a previous mouth feature model of the patient.

[0058] In one embodiment, the system can further comprise a patient data collection interface. The patient data collection interface can be configured to collect patient input data from the patient.

[0059] In one embodiment, the data communication interface may be configured to receive data of the patient. The model generating module may be configured to process the patient data to generate the model of the patient’s mouth based on the patient data.

[0060] In one embodiment, the data of the patient may comprise one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

[0061] In one embodiment, the self-reported information may be objective data entered by the patient and includes one or more of age, gender, weight, and height.

[0062] In one embodiment, the self-reported information may be subjective data entered by the patient in relation to an intraoral device worn by the patient.

[0063] In one embodiment, the self-reported information may include feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

[0064] In one embodiment, the self- reported data may be collected by displaying questions in an interface on a mobile device.

[0065] In one embodiment, the system may further comprise a patient data collection interface configured to collect the self-reported data from the patient.

[0066] In one embodiment, the system may further comprise a specifying module configured to specify at least one characteristic of an intraoral device for the patient based on the correlated characteristic.

[0067] In one embodiment, the at least one characteristic of an intraoral device may be a template intraoral device selected from a plurality of template intraoral devices.

[0068] In one embodiment, the at least one characteristic of an intraoral device may be an adjustment to an intraoral device worn by the patient.

[0069] Also disclosed is a system for specifying a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient. For example, the system can determine particular design characteristics of the intraoral device. The system can comprise a data communication interface configured to receive data of the patient and a model of the patient’s mouth. The system can also comprise a memory storing machine-readable instructions. The system can also comprise a control system including one or more processors configured to execute the machine-readable instructions to process the data of the patient to correlate the patient data with at least one characteristic of the mouth feature model. The machine-readable instructions can also be executed to adjust the generated model of the patient’s mouth model based on the correlated at least one characteristic. The machine-readable instructions can also be executed to specify a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

[0070] In one embodiment, the data of the patient may comprise one or more of: physiological data of the patient, physiological data correlated with sleep states of the patient, and self-reported information in relation to the patient.

[0071] In one embodiment, the self-reported information may be objective data entered by the patient and includes one or more of age, gender, weight, and height.

[0072] In one embodiment, the self-reported information may be subjective data entered by the patient in relation to an intraoral device worn by the patient.

[0073] In one embodiment, the self-reported information may include feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

[0074] In one embodiment, the subjective data may be collected by displaying questions in an interface on a mobile device.

[0075] In one embodiment, the at least one characteristic of an intraoral device may be a template intraoral device selected from a plurality of template intraoral devices.

[0076] In one embodiment, the at least one characteristic of an intraoral device may be an adjustment to an intraoral device worn by the patient.

[0077] Also disclosed is a system for treatment of a sleep disorder for a patient using an intraoral device. The system can comprise a data communication interface in communication with a scanning system and sensors, the data communication interface configured to receive data of the patient. The system can also comprise a memory storing machine -readable instructions. The system can also comprise a control system including one or more processors configured to execute the machine-readable instructions to process the patient data to generate a model of the patient’s mouth. The machine readable instructions can also be executed to select a template intraoral device from a plurality of template intraoral devices, based on the generated model. The machine readable instructions can also be executed to acquire the patient data from the sensors whilst under treatment using the intraoral device. The machine readable instructions can also be executed to adapt the generated model based on the acquired patient data.

[0078] In one embodiment, the scanning data may be configured to receive mouth feature data of the patient and wherein the model of the patient’s mouth may be generated using the mouth feature data.

[0079] In one embodiment, the patient sensor data may comprise one or more of: physiological data correlated with sleep states of the patient, physiological responses to changes in mandibular advancement settings of the intraoral device, compliance with a therapy plan, number of apneas, AHI levels.

[0080] In one embodiment, the patient data may further comprise self-reported information in relation to the patient.

[0081] In one embodiment, the control system may be further configured to execute machine-readable instructions to specify a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

[0082] In one embodiment, the at least one characteristic of an intraoral device may be a template intraoral device selected from a plurality of template intraoral devices.

[0083] In one embodiment, the at least one characteristic of an intraoral device may be an adjustment to the intraoral device.

[0084] In one embodiment, the sleep disorder may be any one of snoring, Obstructive Sleep Apnea, Cheyne-Stokes Respiration, respiratory insufficiency, Obesity Hypoventilation Syndrome, Chronic Obstructive Pulmonary Disease, Neuromuscular Disease or Chest wall disorders.

[0085] Also disclosed is a method for adjusting an intraoral device for a patient. The method can comprise capturing mouth feature data from the patient. The method can also comprise processing the mouth feature data to generate a model of the patient’s mouth. The method can also comprise correlating at least one characteristic of the mouth feature model to a previous mouth feature model of the patient. The method can also comprise adjusting the generated model of the patient’s mouth based on the correlation between the mouth feature model and the previous mouth feature model. The method may also comprise selecting a template intraoral device from a plurality of template intraoral devices based on the adjusted generated model.

[0086] Also disclosed is a method for selecting an intraoral device for a patient. The method may comprise capturing mouth feature data from the patient. The method may also comprise processing the mouth feature data to generate a model of the patient’s mouth. The method may also comprise selecting a template intraoral device from a plurality of template intraoral devices based on the generated model.

[0087] In one embodiment, the method may further comprise correlating at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

[0088] In one embodiment, the model may further comprise determining a measurement from mouth feature data. The mouth feature data may be of at least one reference feature of the patient from a group of reference features. The group ofreference features may comprise a jaw width, a jaw length, a profile depth and a dental arch profile.

[0089] In one embodiment, the method may further comprise using a mobile device with an application. The application may be used to capture mouth features. The application may also be used to capture the mouth feature data from the patient.

[0090] Also disclosed is a method for selecting an intraoral device for use in treatment of a sleep disorder for a patient. The method can comprise scanning mouth feature data of the patient with a scanning system in communication with a data communication interface. The method can also comprise processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory. The processing system can be configured for generating a model of the patient’s mouth with a model generating module. The processor can also be configured for selecting a template intraoral device from a plurality of template intraoral devices with a selecting module. The template intraoral device can be based on the generated model.

[0091] In one embodiment, the selecting module may be configured to correlate at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

[0092] Also disclosed is a method for determining a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient. The method may comprise scanning mouth feature data of the patient with a scanning system in communication with a data communication interface. The method can also comprise processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory. The processing system can be configured for generating a model of the patient’s mouth with a model generating module. The processing system can also be configured for specifying at least one characteristic of the intraoral device with a specifying module. The at least one characteristic of the intraoral device can be based on the generated model.

[0093] In one embodiment, the data communication interface may be configured to receive data of the patient. The model generating module and / or specifying modulemay be configured to process the patient data to generate the model of the patient’s mouth and / or to specify at least one characteristic of the intraoral device based on the patient data.

[0094] Also disclosed is a method for obtaining feedback from a patient using an intraoral device for treatment of a sleep disorder. The method can comprise scanning mouth feature data of the patient with a scanning system in communication with a data communication interface. The method can also comprise processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory. The processing system can be configured for generating a model of the patient’s mouth with a model generating module. The processing system can also be configured for correlating at least one characteristic of the mouth feature model to a previous mouth feature model of the patient with an analysis module.

[0095] In one embodiment, the data communication interface may be configured to receive data of the patient. The model generating module may be configured to process the patient data to generate the model of the patient’s mouth based on the patient data.

[0096] Also disclosed is a method for specifying a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient. The method can comprise receiving data of the patient and a model of the patient’s mouth with a data communication interface. The method can also comprise processing the data of the patient with a control system including one or more processors configured to execute machine-readable instructions stored in a memory. The processors can be configured for correlating the patient data with at least one characteristic of the mouth feature model. The processors can also be configured for adjusting the generated model of the patient’s mouth model based on the correlated at least one characteristic. The processors can also be configured for specifying a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

[0097] Also disclosed is a method for treatment of a sleep disorder for a patient using an intraoral device. The method can comprise receiving data of the patient with a scanning system and sensors in communication with a data communicationinterface. The method can also comprise processing the data of the patient with a control system including one or more processors configured to execute machine- readable instructions stored in a memory. The processors can be configured for generating a model of the patient’s mouth. The processors can also be configured for selecting a template intraoral device from a plurality of template intraoral devices, based on the generated model. The processors can also be configured for acquiring the patient data from the sensors whilst under treatment using the intraoral device. The processors can also be configured for adapting the generated model based on the acquired patient data.

[0098] In one embodiment, the scanning data may be configured to receive mouth feature data of the patient. The model of the patient’s mouth may be generated using the mouth feature data.3 BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like reference numerals refer to similar elements including:3.1 THERAPY3.1.1 Respiratory System

[0100] Fig. 1A shows a blocked airway due to the collapse of the muscles in the upper airway blocking the upper airway.

[0101] Fig. IB shows how protrusion of the lower jaw expands the space behind the tongue to prevent or reduce blockage of the upper airway.

[0102] Fig. 2A shows an overview of a human respiratory system.

[0103] Fig. 2B shows a view of a human upper airway.3.1.2 Mouth Anatomy

[0104] Fig. 3 shows an anterolateral view of an open human mouth showing the arrangement of the teeth on the maxilla and mandible jaws.

[0105] Fig. 4 shows a frontal (antero-posterior) view of an open human mouth showing the arrangement of the teeth on the maxilla and mandible jaws.

[0106] Fig. 5A shows a side view of a human face.

[0107] Fig. 5B shows an anterolateral view of a closed human mouth with a line indicating the occlusal plane.3.2 BREATHING WAVEFORMS

[0108] Fig. 6A shows a model typical breath waveform of a person while sleeping.

[0109] Fig. 6B shows polysomnography of a patient before treatment.

[0110] Fig. 6C shows patient flow rate data where the patient is experiencing a series of total obstructive apneas.

[0111] Fig. 6D shows a scaled inspiratory portion of a breath where the patient is experiencing low-frequency inspiratory snore.3.3 INTRAORAL DEVICE

[0112] Figs. 7A to 7F show an intraoral device according to a first embodiment, in front views and perspective views.

[0113] Figs. 8A to 8E show side views of the upper and lower splints of a Mandibular Advancement Device (MAD).

[0114] Fig. 9 is a diagram of an example system for collecting patient data relating to an intraoral device.

[0115] Fig. 10 is a block diagram of the components of a computing device configured to capture mouth data.

[0116] Fig. 11 A is an example interface that has captured a mouth image for generating mouth data.

[0117] Fig. 1 IB is an example interface that shows a mouth mesh interposed on the mouth image in Fig. 11 A for data collection of mouth measurements.

[0118] Fig. 11C is an example interface configured to collect sleep position data.

[0119] Fig. 12 is a flow diagram illustrating an exemplary embodiment of a method 1200 for selecting an intraoral device for a patient.4 DETAILED DESCRIPTION OF EXAMPLES OF THETECHNOLOGY

[0120] Before the present technology is described in further detail, it is to be understood that the technology is not limited to the particular examples described herein, which may vary. It is also to be understood that the terminology used in this disclosure is for the purpose of describing only the particular examples discussed herein, and is not intended to be limiting.

[0121] The following description is provided in relation to various examples which may share one or more common characteristics and / or features. It is to be understood that one or more features of any one example may be combinable with one or more features of another example or other examples. In addition, any single feature or combination of features in any of the examples may constitute a further example.4.1 EMB ODIMENTS OF THE INTRAORAL DEVICE

[0122] Before the present technology is described, reference is briefly made to Figs. 1A to 5B which are provided for anatomical and physiological descriptions / references only.

[0123] Fig. 6A shows a model typical breath waveform of a person while sleeping. The horizontal axis is time, and the vertical axis is respiratory flow rate. While the parameter values may vary, a typical breath may have the following approximate values: tidal volume Vt 0.5L, inhalation time Ti 1.6s, peak inspiratory flow rate Qpeak 0.4 L / s, exhalation time Te 2.4s, peak expiratory flow rate Qpeak -0.5 L / s. The total duration of the breath, Ttot, is about 4s. The person typically breathes at a rate of about 15 breaths per minute (BPM), with Ventilation Vent about 7.5 L / min. A typical duty cycle, the ratio of Ti to Ttot, is about 40%.

[0124] Fig. 6B shows polysomnography of a patient before treatment. There are eleven signal channels from top to bottom with a 6-minute horizontal span. The top two channels are both electroencephalogram t(EEG) from different scalp locations. Periodic spikes in the second EEG represent cortical arousal and related activity. The third channel down is submental electromyogram (EMG). Increasing activity around the time of arousals represents genioglossus recruitment. The fourth & fifth channels are electro-oculogram (EOG). The sixth channel is an electrocardiogram. The seventh channel shows pulse oximetry (SpO2) with repetitive desaturations to below 70%from about 90%. The eighth channel is respiratory flow rate using a nasal cannula connected to a differential pressure transducer. Repetitive apneas of 25 to 35 seconds alternate with 10 to 15 second bursts of recovery breathing coinciding with EEG arousal and increased EMG activity. The ninth channel shows movement of chest and the tenth shows movement of abdomen. The abdomen shows a crescendo of movement over the length of the apnea leading to the arousal. Both become untidy during the arousal due to gross body movement during recovery hyperpnea. The apneas are therefore obstructive, and the condition is severe. The lowest channel is posture, and, in this example, it does not show change.

[0125] Fig. 6C shows patient flow rate data, where the patient is experiencing a series of total obstructive apneas. The duration of the recording is approximately 160 seconds. Flow rates range from about +1 L / s to about -1.5 L / s. Each apnea lasts approximately 10-15 s.

[0126] Fig. 6D shows a scaled inspiratory portion of a breath where the patient is experiencing low-frequency inspiratory snore.

[0127] Referring to Fig. 7A, an intraoral device or a mandibular advancement device (MAD) 1000 is shown fitted over a mould of an upper jaw and lower jaw including teeth. The intraoral device or MAD comprises an upper splint 1100, a lower splint 1200, and a pair of connecting rods 1300 connecting the upper and lower splints 1100, 1200 together.

[0128] As seen in Figs. 7A to 8E, the upper splint 1100 includes two maxilla or upper gutter portions 1110 designed or structured to fit over at least a portion of one or more teeth on each side of the maxilla or upper jaw. The upper gutter portions 1110 may cover a plurality of teeth in the region between the molars and canine on the maxilla. A maxilla or upper band portion 1120 is preferably provided between the two upper gutter portions 1110 to join the two upper gutter portions 1110 together. The upper band portion 1120 may be designed to extend between the two upper gutter portions 1110 across the front portion of the lateral and central incisors and may not engage with the internal or the external surface of these incisor teeth. Here, the upper band portion 1120 reduces the visual impact of the upper splint when inserted within the patient's mouth. Preferably, the upper splint 1100 is formed as a single piece with the upper gutter portions 1110 and the upper band portion 1120 integrally formed together.

[0129] However, it is noted that the upper splint 1100 may include a single upper gutter portion 1110 designed to fit over all of the teeth of the maxilla, thus no upper band portion 1120 would be required in such an upper splint. Such an upper splint may be more intrusive within the mouth. Such an upper splint may be used when the splint is used to treat bruxism alone or simultaneously with treating obstructive sleep apnea.

[0130] The upper splint 1100 also may include one or more, but preferably a pair of upper splint connection points 1130, preferably one on each side of the upper splint 1100, to allow connection of a respective second rod end 1320 of each one of the pair of connecting rods 1300, to the upper splint 1100. As illustrated in Figs. 7A and 8E, the upper splint connection points 1130 are preferably provided in the region of the canines. Preferably, the upper splint connection points 1130 are made as small as possible and may include a rounded shape to prevent irritation within the mouth. Preferably, the shape and size of the contact surface of the upper splint connection point 1130 substantially correspond to the shape and size of the second rod end 1320 (see Figs. 7A to 7F and Figs. 8A to 8E).

[0131] The lower splint 1200, as illustrated in Figs. 7A to 8E, includes two mandible or lower gutter portions 1210 designed to fit over at least a portion of one or more teeth on each side of the mandible. The lower gutter portions 1210 may cover a plurality of teeth in the region between the molars and canine on the mandible. A mandible or lower band portion 1220 is preferably provided between the two lower gutter portions 1210 to join the two lower gutter portions 1210 together. The lower band portion 1220 may be designed to extend between the two lower gutter portions 1210 across the front portion of the lateral and central incisors and may not engage with the internal or the external surface of these incisor teeth. Here, the lower band portion 1220 reduces the visual impact of the lower splint 1200 when inserted within the patient's mouth. Preferably, the lower splint 1200 is formed as a single piece with the two lower gutter portions 1210 and the lower band portion 1220 integrally formed together.

[0132] However, it is noted that the lower splint 1200 may include a single lower gutter portion 1210 designed to fit over all of the teeth of the mandible respectively, thus no lower band portion 1220 is required in such a lower splint. Such a lower splint may be more intrusive within the mouth. Such a lower splint may be used when thesplint is used to treat bruxism alone or simultaneously with treating obstructive sleep apnea.

[0133] As particularly seen in Fig. 8E, the lower band portion 1220 may be inclined relative to a plane P-P perpendicular to the sliding plane surface 1160, 1260, which is parallel to the occlusal plane. The front surface 1220a of the lower band portion 1220 is inclined or angled (line Q-Q in Fig. 8E) to follow the angle of the incisors to prevent protrusion of the lower band portion 1220 into the inside of the lips. In other words, the front surface 1220a of the lower band portion 1220 angles slightly outwards from the bottom to the top in use. In a similar manner, as seen in Fig. 8E, the upper band portion 1120 may also be angled (line R-R) to follow the angle of the patients’ incisors and may include rounded or smoothed top and bottom edges to reduce irritation of the maxilla gums. Such a design preferably prevents protruding too far inside of the lips.

[0134] Vestibular bands may be replaced by lingual bands and angles may be adjustable parameters. It is appreciated that the upper band portion 1120 or the lower band portion 1220 as such may be provided with different designs of MADs and the specific arrangement of other components such as the gutter portion design may vary.

[0135] As seen in Figs. 7A to 8E, the lower splint 1200 also may include one or more of lower splint connection points 1230, preferably a pair of points, one on each side of the lower splint 1200. Each lower splint connection point 1230 may be configured to allow connection to a first end 1310 of a respective one of the pair of connecting rods 1300 to the lower splint 1200 (Figs. 7C, 7D and 8B). The lower splint connection points 1230 may be elevated relative to the lower gutter portions 1210. Preferably, the elevated lower splint connection points 1230 are adjacent the upper gutter portions 1110 on the upper splint 1100 (Figs. 7D, 8A-1 to 8C).

[0136] The lower splint connection point 1230 is preferably provided in the area of molars, such as the second molar (Fig. 10A). Thus, each one of the pair of connecting rods 1300 may be configured to substantially laterally connect the upper splint 1100 and the lower splint 1200, with the second end 1320 of the connecting rod 1300 connected to the upper splint 1100 and the first end 1310 of the connecting rod 1300 connected to the lower splint 1200 (e.g., see Figs. 7D and 8A).

[0137] The lower splint connection points 1230 on the lower splint 1200 may be elevated in a position so that, when the connecting rods 1300 are connected to the upper splint 1100 and lower splint 1200, the connecting rods 1300 are positionedsubstantially parallel with the Frankfort plane. In such an arrangement, the traction force of the connecting rods 1300 is substantially parallel to the occlusal plane, which reduces the likelihood of the intraoral device or MAD coming loose in use. This arrangement of the connecting rods 1300 is also advantageous for retaining the mandible in an advanced position.4.2 INTRAORAL DEVICE SELECTION

[0138] In some forms, the intraoral device including the shell, the insert, and other components may be provided in different sizes and / or material combinations to improve both comfort and efficacy of use. As such, the device “kit” may be provided as different device templates, with the most appropriate template being based on anatomical features of the patient, particularly mouth features including intraoral anatomical features such as, for instance, a jaw width, a jaw length, a profile depth, and a dental arch profile. Other intraoral anatomical features are encompassed within the scope of this disclosure. Once a template is chosen, the fit may be further refined by the patient. In other forms, a template intraoral device may be in its final form ready for patient use without requiring further process. In either case, further adjustment may occur during use.

[0139] In some forms, the present technology allows patients to more quickly and conveniently select an intraoral device by obtaining data from individual patients determined by a scanning process. A scanning process may allow a patient to quickly obtain feature data of their mouth (including intraoral features) and / or facial anatomy (collectively herein after referred to as mouth features) from the comfort of their own home using a computing device, such as a desktop computer, tablet, smart phone or other mobile device. The computing device may then receive a recommendation for an appropriate intraoral device size and type after analysis of the mouth feature data. Other data for aiding the selection process may also be gathered in other ways such as from pre-stored information, or from quantitative or qualitative data collected from the input. Such data may be stored and correlated with information relating to the patient and the scanned mouth feature data.

[0140] In this example, an application downloadable from a manufacturer or third party server to a smartphone or tablet with an integrated camera may be used to collect the mouth feature data. When launched, the application may provide visualand / or audio instructions. As instructed, the user (i.e. a patient) may stand in front of a mirror, and press the camera button on a user interface. An activated process may then take a series of images of the user's face and mouth, for example as still images or video, and then, within a matter of seconds for example, obtain relevant dimensions of the face and / or mouth (including intraoral anatomy) for selection of a template intraoral device (based on the processor analysing the images). Such an application may be configured for collecting patient feedback as part of forming an initial design of the intraoral device, i.e., forming part of the data informing the selection or modification of a first intraoral device to be used by the patient. The patient feedback may comprise subjective and / or objective data, where the subjective data may be e.g., a questionnaire, and the objective data may consist of physiological data as set forth above.

[0141] As will be explained below, such an application may also be used to collect feedback from a patient once an intraoral device is selected and used in a therapy treatment. In this regard, the feedback provided through the application may be used not only to inform selection of existing intraoral device templates, but also to guide design / design modifications or creation of new templates.

[0142] A user / patient may capture an image or series of images of their mouth features. Instructions provided by an application stored on a computer-readable medium, such as when executed by a processor, detect various facial / mouth landmarks within the images, measure and scale the distance between such landmarks, compare these distances to a data record, and establish a model of the patient’s mouth. Based on the model generated, an appropriate template intraoral device may be selected. Thus, an automated device of a consumer may permit accurate device selection, such as in the home, to permit customers to determine device template selection without trained associates.

[0143] Fig. 9 depicts an example system 2000 that may be implemented for collecting data from patients. The system 2000 may also include automatic mouth feature measuring and intraoral device selection. System 2000 may generally include one or more of servers 2010, a communication network 2020, and a computing device 2030. Server 2010 and computing device 2030 may communicate via a communication network 2020, which may be a wired network 2022, wireless network 2024, or wired network with a wireless link 2026. In some versions, server 2010 may communicate one-way with computing device 2030 by providing information tocomputing device 2030, or vice versa. In other embodiments, server 2010 and computing device 2030 may share information and / or processing tasks. The system 2000 may be implemented, for example, to permit automated purchase of intraoral devices 1000 in Figs. 11A-11C where the process may include automatic sizing processes described in more detail herein. For example, a customer may order an intraoral device online after running a device selection process that automatically identifies a suitable device size by image analysis of the customer's mouth features. The system 2000 may also continue to collect feedback data after the device is used by a patient.

[0144] The server 2010 and / or the computing device 2030 may also be in communication with other sensors 2050 relating to patient therapy compliance, sleep health or hygiene such as environmental sensing. For example, the sensors may monitor maximum compliance with a therapy plan (e.g., device in and out times and frequency of in and out events), number of apneas overnight, AHI levels, mandibular advancement settings used on their device and also prescribed settings. This data may be correlated with mouth and / or facial dimensional data for a new patient. As will be explained, the server 2010 may collect the data from multiple patients stored in the database 2060 and corresponding intraoral device data stored in the database 2070. This may be used to further inform the selection of an appropriate intraoral device based on the optimal device that best fits the scanned mouth feature data collected from the new patient and the devices that achieved the best operational data for patients that have mouth features, sleep behavioural data, and demographic data that are similar to the new patient. Such data may be used to refine the mouth model that informs the intraoral device selection. Such data may be further supplemented by additional feedback in the form of subjective data entered by a patient. This may be achieved by a guided survey or intermittent feedback prompts provided on the user interface of the system 2000.

[0145] The computing device 2030 can be a desktop or laptop computer 2032 or a mobile device, such as a smart phone 2034 or tablet 2036. Fig. 10 depicts the general architecture 2300 of the computing device 2030. The computing device 2030 may include one or more processors 2310. The computing device 2030 may also include a display interface 2320, user control / input interface 2331, sensor 2340 and / or a sensor interface for one or more sensor(s), inertial measurement unit (IMU) 2342 and non-volatile memory / data storage 2350.

[0146] Sensor 2340 may be one or more cameras (e.g., a CCD charge - coupled device or active pixel sensors) that are integrated into computing device 2030, such as those provided in a smartphone or in a laptop. Alternatively, where the computing device 2030 is a desktop computer, computing device 2030 may include a sensor interface for coupling with an external camera, such as the webcam 2033. Other exemplary sensors that could be used to assist in the methods described herein that may either be integral with or external to the computing device 2030 include stereoscopic cameras for capturing three-dimensional images, or a light detector capable of detecting reflected light from a laser or strobing structured light source. Such devices may be in the form of a wand that allows for easy insertion into the mouth.

[0147] User control / input interface 2331 allows the user to provide commands or respond to prompts or instructions provided to the user. This could be a touch panel, keyboard, mouse, microphone, and / or speaker, for example.

[0148] The display interface 2320 may include a monitor, LCD panel, or the like to display prompts, output information (such as mouth feature measurements or device selection recommendations), and other information, such as a capture display.

[0149] Memory / data storage 2350 may be the internal memory of computing device 2030, such as RAM, flash memory or ROM. In some embodiments, memory / data storage 2350 may also be external memory linked to computing device 2030, such as an SD card, server, USB flash drive or optical disc, for example. In other embodiments, memory / data storage 2350 can be a combination of external and internal memory. Memory / data storage 2350 includes stored data 2354 and processor control instructions 2352 that instruct processor 2310 to perform certain tasks. Stored data 2354 can include data received by sensor 2340, such as a captured image, and other data that is provided as a component part of an application. Processor control instructions 2352 can also be provided as a component part of an application.

[0150] As explained above, images of mouth features may be captured by a mobile computing device such as the smartphone 2034. An appropriate application executed on the computing device 2030 or the server 2010 can provide three- dimensional relevant data to assist in selection of an appropriate intraoral device. The application may use any appropriate method of patient feature scanning. Such applications may include the Capture from StandardCyborg (https: / / www.standardcyborg.com / ), an application from Scandy Pro(https: / / www.scandy.co / products / scandy - pro), the Beauty3D application from Qianxun3d (http: / / www.qianxun3d.com / scanpage), the Unre 3D FaceApp (http: / / www.unre. ai / index.php?route=ios / detail) and an application from Bellus3D (https: / / www.bellus3d.com / ). A detailed process of facial scanning includes the techniques disclosed in WO 2017000031, hereby incorporated by reference in its entirety.

[0151] One such application is an application for mouth feature measuring and / or patient data collection 2360, which may be an application downloadable to a mobile device, such as smartphone 2034 and / or tablet 2036. The application 2360 may also collect mouth / facial features and data of patients who have already been using intraoral devices for better collection of feedback from such devices. The application 2360, which may be stored on a computer-readable medium, such as memory data storage 2350, includes programmed instructions for processor 2310 to perform certain tasks related to mouth feature measuring. The application also includes data that may be processed by the algorithm of the automated methodology. Such data may include a data record, reference feature, and correction factors.

[0152] The application 2360 is executed by the processor 2310 to capture patient mouth features using two-dimensional or three-dimensional images, to create a model of the patient mouth from that data, and in some forms, from other captured data, and to select appropriate intraoral device sizes and types, such as from a group of template devices, based on the resultant output. The method may generally be characterized as including four or five different phases: a pre-capture phase, a capture phase, a post-capture image processing phase, model building phase, and a comparison and output phase.

[0153] In some cases, the application for mouth feature determining may control a processor 2310 to output a visual display that includes a reference feature on the display interface 2320. The user may position the feature adjacent to their facial or mouth features, such as by movement of the camera. The processor 2310 may then capture and store one or more images of the mouth features in association with the reference feature when certain conditions, such as alignment conditions, are satisfied. This data may include dynamic movements of the patient captured by video, such as jaw articulation. This may be done with the assistance of a mirror. The mirror reflects the displayed reference feature and the user's face to the camera. The application then controls the processor 2310 to identify certain mouth features within the images andmeasure distances therebetween. By image analysis processing, a scaling factor may then be used to convert the mouth feature measurements, which may be pixel counts, to standard intraoral device measurement values based on the reference feature. Such values may be, for example, standardised unit of measure, such as a metre or an inch, and values expressed in such units suitable for intraoral device sizing.

[0154] Additional correction factors may be applied to the measurements. The mouth feature measurements may be compared to data records that include measurement ranges corresponding to different template intraoral devices for particular mouth forms. The recommended intraoral device size may then be chosen and be output to the user / patient based on the comparison(s) as a recommendation. Such a process may be conveniently effected within the comfort of any preferred user location. The application may perform this method within seconds. In one example, the application performs this method in real time.

[0155] In the pre-capture phase, the processor 2310, among other things, assists the user in establishing the proper conditions for capturing one or more images for sizing processing. Some of these conditions include proper lighting and camera orientation and motion blur caused by an unsteady hand holding the computing device 2030, for example.

[0156] A user may conveniently download an application for performing the automatic measuring and sizing at computing device 2030 from a server, such as a third party application-store server, onto their computing device 2030. When downloaded, such application may be stored on the internal non-volatile memory of computing device 2030, such as RAM or flash memory. Computing device 2030 is preferably a mobile device, such as smartphone 2034 or tablet 2036.

[0157] When the user launches the application, the processor 2310 may prompt the user via the display interface 2320 to provide patient specific information, such as age, gender, weight, and height. However, the processor 2310 may prompt to the user to input this information at any time, such as after the user's mouth features are measured and after the user uses the intraoral device. The processor 2310 may also present a tutorial, which may be presented audibly and / or visually, as provided by the application to aid the user in understanding their role during the process. Also, in the pre-capture phase, the application may extrapolate the patient specific information based on information already gathered by the user, such as after receiving captured images of the user's mouth, and based on machine learning techniques orthrough artificial intelligence. Other information may also be collected through interfaces as will be explained below.

[0158] When the user is prepared to proceed, which may be indicated by a user input or response to a prompt via user control / input interface 2331, the processor 2310 activates the sensor 2340 as instructed by the processor control instructions 2352. The sensor 2340 is preferably the mobile device's forward facing camera, which is located on the same side of the mobile device as display interface 2320. The camera is generally configured to capture two-dimensional images. Mobile device cameras that capture two-dimensional images are ubiquitous. The present technology takes advantage of this ubiquity to avoid burdening the user with the need to obtain specialized equipment.

[0159] Around the same time the sensor / camera 2340 is activated, the processor 2310, as instructed by the application, presents a capture display on the display interface 2320. The capture display may include a camera live action preview, a reference feature, a targeting box, and one or more status indicators or any combination thereof. In this example, the reference feature is displayed centered on the display interface and has a width corresponding to the width of the display interface 2320. The vertical position of the reference feature may be such that the top edge of reference feature abuts the upper most edge of the display interface 2320 or the bottom edge of reference feature abuts the lower most edge of the display interface 2320. A portion of the display interface 2320 will display the camera live action preview, typically showing the user's mouth features captured by the sensor / camera 2340 in real time if the user is in the correct position and orientation.

[0160] The reference feature is a feature that is known to computing device 2030 (i.e., predetermined) and provides a frame of reference to processor 2310 that allows processor 2310 to scale captured images. The reference feature may preferably be a feature other than a facial or anatomical feature of the user. Thus, during the image processing phase, the reference feature assists processor 2310 in determining when certain alignment conditions are satisfied, such as during the pre-capture phase. The reference features may be a quick response (QR) code or known exemplar or marker, which can provide processor 2310 certain information, such as scaling information, orientation, and / or any other desired information which can optionally be determined from the structure of the QR code. The QR code may have a square or rectangular shape. When displayed on display interface 2320, the reference featurehas predetermined dimensions, such as in units of millimetres or centimetres, the values of which may be coded into the application and communicated to processor 2310 at the appropriate time. The actual dimensions of reference feature 2326 may vary between various computing devices. In some versions, the application may be configured to be a computing device model-specific in which the dimensions of reference feature, when displayed on the particular model, is already known. However, in other embodiments, the application may instruct processor 2310 to obtain certain information from device 2030, such as display size and / or zoom characteristics that allow the processor 2310 to compute the real world / actual dimensions of the reference feature as displayed on display interface 2320 via scaling. Regardless, the actual dimensions of the reference feature as displayed on the display interfaces 2320 of such computing devices are generally known prior to post capture image processing.

[0161] Along with the reference feature, the targeting box may be displayed on display interface 2320. The targeting box allows the user to align certain components within capture display in targeting box, which is desired for successful image capture.

[0162] The status indicator provides information to the user regarding the status of the process. This helps ensure the user does not make major adjustments to the positioning of the sensor / camera prior to completion of image capture.

[0163] Thus, when the user holds display interface 2320 parallel to the mouth facial features to be measured and presents user display interface 2320 to a mirror or other reflective surface, the reference feature is prominently displayed and overlays the real-time images seen by camera / sensor 2340 and as reflected by the mirror. This reference feature may be fixed near the top of display interface 2320. The reference feature is prominently displayed in this manner at least partially so that sensor 2340 can clearly see the reference feature so that processor 2310 can easily the identify feature. In addition, the reference feature may overlay the live view of the user's face, which helps avoid user confusion.

[0164] The user may also be instructed by processor 2310, via display interface 2320, by audible instructions via a speaker of the computing device 2030, or be instructed ahead of time by the tutorial, to position display interface 2320 in a plane of the mouth features to be measured. For example, the user may be instructed to position display interface 2320 such that it is facing anteriorly and placed under,against, or adjacent to the user's chin in a plane aligned with certain mouth features to be measured. As the images ultimately captured are two-dimensional, planar alignment helps ensure that the scale of reference feature 2326 is equally applicable to the mouth feature measurements. In this regard, the distance between the mirror and both of the user's mouth features and the display will be approximately the same.

[0165] When the user is positioned in front of a mirror, and the display interface 2320, which includes the reference feature, is roughly placed in planar alignment with the mouth features to be measured, the processor 2310 checks for certain conditions to help ensure sufficient alignment. One exemplary condition that may be established by the application, as previously mentioned, is that the entirety of the reference feature must be detected within targeting box in order to proceed. If the processor 2310 detects that the reference feature is not entirely positioned within targeting box, the processor 2310 may prohibit or delay image capture. The user may then move their face along with display interface 2320 to maintain planarity until the reference feature, as displayed in the live action preview, is located within targeting box. This helps optimized alignment of the mouth features and display interface 2320 with respect to the mirror for image capture.

[0166] When processor 2310 detects the entirety of reference feature within targeting box, processor 2310 may read the IMU 2342 of the computing device for detection of device tilt angle. The IMU 2342 may include an accelerometer or gyroscope, for example. Thus, the processor 2310 may evaluate device tilt such as by comparison against one or more thresholds to ensure it is in a suitable range. For example, if it is determined that computing device 2030, and consequently display interface 2320 and user's mouth features, is tilted in any direction within about 15 degrees, the process may proceed to the capture phase. In other embodiments, the tilt angle for continuing may be within about +10 degrees, 27 degrees, 13 degrees, or +1 degree. If excessive tilt is detected a warning message may be displayed or sounded to correct the undesired tilt. This is particularly useful for assisting the user to help prohibit or reduce excessive tilt, particularly in the anterior-posterior direction, which if not corrected, could pose as a source of measuring error as the captive reference image will not have a proper aspect ratio.

[0167] When alignment has been determined by the processor 2310 as controlled by the application, the processor 2310 proceeds into the capture phase. The capture phase preferably occurs automatically once the alignment parameters and anyother conditions precedent are. However, in some embodiments, the user may initiate the capture in response to a prompt to do so.

[0168] When image capture is initiated, the processor 2310 via the sensor 2340 captures a number ‘n’ number(s) of images, which is preferably more than one image. For example, the processor 2310 via the sensor 2340 may capture about 5 to 20 images, 10 to 20 images, or 10 to 15 images, etc. The quantity of images captured may be time-based. In other words, the number of images that are captured may be based on the number of images of a predetermined resolution that can be captured by sensor 2340 during a predetermined time interval. For example, if the number of images sensor 2340 can capture at the predetermined resolution in 1 second is 40 images and the predetermined time interval for capture is 1 second, the sensor 2340 will capture 40 images for processing with the processor 2310. The quantity of images may be user-defined, determined by the server 2010 based on artificial intelligence or machine learning of environmental conditions detected, or based on an intended accuracy target. For example, if high accuracy is required, then more captured images may be required. Although it is preferable to capture multiple images for processing, one image is contemplated and may be successful for use in obtaining accurate measurements. However, more than one image allows average measurements to be obtained. This may reduce error / inconsistencies and increase accuracy. The images may be placed by the processor 2310 in the stored data 2354 of the memory / data storage 2350 for post-capture processing.

[0169] The user may also be instructed by processor 2310, via display interface 2320, by audible instructions via a speaker of the computing device 2030, or be instructed ahead of time by the tutorial, to re-position display interface 2320 (or wand, etc.) in one or more additional planes to ensure that all relevant mouth features can be measured. For example, the user may be instructed to re -position display interface 2320 such that it is tilted leftward of the previously captured image.

[0170] The processor may be configured to prompt the user to reposition the reference feature e.g., to the left, right, upward, downward, etc., and then prompt the user to tilt the computing device so that the entirety of the repositioned reference feature can be detected within targeting box. As set forth previously, if the processor 2310 detects that the reference feature is not entirely positioned within targeting box, the processor 2310 may prohibit or delay image capture.

[0171] When re-alignment of the device and reference feature has been determined again by the processor 2310 as controlled by the application, the processor 2310 proceeds into the capture phase. This process of capturing multiple mouth angles may be repeated one time, or more than one time, to capture various angles of the user’s mouth, so as to capture relevant features to determine an appropriate intraoral device size and type.

[0172] Once the images are captured, the images are processed by processor 2310 to detect or identify mouth features / landmarks and measure distances therebetween. The resultant measurements may be used to recommend an appropriate intraoral device and size. This processing may alternatively be performed by server 2010 receiving the transmitted captured images and / or on the user's computing device (e.g., smart phone). Processing may also be undertaken by a combination of the processor 2310 and the server 2010. In one example, the recommended intraoral device size may be predominantly based on the user's mouth width. In other examples, the recommended intraoral device size may be based on the user's mouth and / or jaw dimensions.

[0173] The processor 2310, as controlled by the application, retrieves one or more captured images from the stored data 2354. The image is then extracted by the processor 2310 to identify each pixel comprising the two-dimensional captured image. The processor 2310 then detects certain predesignated mouth features within the pixel formation.

[0174] Detection may be performed by the processor 2310 using edge detection, such as Canny, Prewitt, Sobel, or Robert's edge detection, for example. These edge detection techniques / algorithms help identify the location of certain mouth features within the pixel formation, which correspond to the patient's actual mouth features as presented for image capture. For example, the edge detection techniques can first identify the user's mouth within the image and also identify pixel locations within the image corresponding to specific mouth features, such as specific teeth, e.g., central and lateral incisors, canine / cuspid, third molar, etc., and borders thereof, and also corners of the mouth. The processor 2310 may then mark, tag or store the particular pixel location(s) of each of these mouth features. Alternatively, or if such detection by the processor 2310 / server 2010 is unsuccessful, the predesignated mouth features may be manually detected and marked, tagged or stored bya human operator with viewing access to the captured images through a user interface of the processor 2310 / server 2010.

[0175] Once the pixel coordinates for these mouth features are identified, the application controls the processor 2310 to measure the pixel distance between certain coordinates of the identified features. For example, the distance may generally be determined by the number of pixels for each feature and may include scaling. For example, measurements between the left and right third molars may be taken to determine pixel width of the upper / lower jaw and / or between the upper and lower central incisors (when the user’s mouth is opened) to determine the pixel height of the user’s open mouth. Other examples include pixel distance between upper and lower central incisors (of a closed mouth) to determine e.g., overbite / jaw alignment, between mouth corners to determine mouth width, etc. In some cases, distances between facial features can be measured to complement measurements taken of the user’s mouth. For example, jaw line / perimeter, chin position (mental protuberance) relative to the upper lip (premaxilla), etc.

[0176] Once the pixel measurements of the pre-designated mouth features are obtained, an anthropometric correction factor(s) may be applied to the measurements. It should be understood that this correction factor can be applied before or after applying a scaling factor, as described below. The anthropometric correction factor can correct for errors that may occur in the automated process, which may be observed to occur consistently from patient to patient. In other words, without the correction factor, the automated process, alone, may result in consistent results from patient to patient, but results that may lead to a certain amount of mis-sized intraoral devices. The correction factor, which may be empirically extracted from population testing, shifts the results closer to a true measurement helping to reduce or eliminate mis-sizing. This correction factor can be refined or improved in accuracy over time as measurement and sizing data for each patient is communicated from respective computing devices to the server 2010 where such data may be further processed to improve the correction factor.

[0177] In order to apply the mouth feature measurements to an intraoral device sizing, whether corrected or uncorrected by the anthropometric correction factor, the measurements may be scaled from pixel units to other values that accurately reflect the distances between the patient's mouth features as presented for image capture. The reference feature may be used to obtain a scaling value or values. Thus, the processor2310 similarly determines the reference feature's dimensions, which can include pixel width and / or pixel height (x and y) measurements (e.g., pixel counts) of the entire reference feature. More detailed measurements of the pixel dimensions of the many squares / dots that comprise a QR code reference feature, and / or pixel area occupied by the reference feature and its constituent parts may also be determined. Thus, each square or dot of the QR code reference feature may be measured in pixel units to determine a scaling factor based on the pixel measurement of each dot and then averaged among all the squares or dots that are measured, which can increase accuracy of the scaling factor as compared to a single measurement of the full size of the QR code reference feature. However, it should be understood that, whatever measurements are taken of the reference feature, the measurements may be utilized to scale a pixel measurement of the reference feature to a corresponding known dimension of the reference feature.

[0178] Once the measurements of the reference feature are taken by the processor 2310, the scaling factor is calculated by the processor 2310 as controlled by the application. The pixel measurements of reference feature are related to the known corresponding dimensions of the reference feature, e.g., the reference feature as displayed by the display interface 2320 for image capture, to obtain a conversion or scaling factor. Such a scaling factor may be in the form of length / pixel or area / pixel A2. In other words, the known dimension(s) may be divided by the corresponding pixel measurement(s) (e.g., count(s)).

[0179] The processor 2310 then applies the scaling factor to the mouth feature measurements (pixel counts) to convert the measurements from pixel units to other units to reflect distances between the patient's actual mouth features suitable for intraoral device sizing. This may typically involve multiplying the scaling factor by the pixel counts of the distance(s) for mouth features pertinent for intraoral device sizing.

[0180] These measurement steps and calculation steps for both the mouth features and reference features are repeated for each captured image until each image in the set has mouth feature measurements that are scaled and / or corrected.

[0181] The corrected and scaled measurements for the set of images may then optionally be averaged by the processor 2310 to obtain final measurements of the patient's mouth anatomy. Such measurements may reflect distances between the patient's mouth features.

[0182] In the comparison and output phase, results from the post-capture image processing phase may be directly output (displayed) to a person of interest or compared to data record(s) to obtain an automatic recommendation for an intraoral device size.

[0183] Once all of the measurements are determined, the results (e.g., averages) may be displayed by the processor 2310 to the user via the display interface 2320. In one embodiment, this may end the automated process. The user / patient can record the measurements for further use by the user.

[0184] Alternatively, the final measurements may be forwarded either automatically or at the command of the user to the server 2010 from the computing device 2030 via the communication network 2020. The server 2010 or individuals on the server-side may conduct further processing and analysis to determine a suitable intraoral device size.

[0185] In a further embodiment, the final mouth feature measurements that reflect the distances between the actual mouth features of the patient / user are compared by the processor 2310 to template intraoral device size data such as in a data record. The data record may be part of the application for automatic mouth feature measurements and intraoral device sizing. This data record can include, for example, a lookup table accessible by the processor 2310, which may include template intraoral device sizes corresponding to a range of mouth feature distances / values.

[0186] The example process for selection of intraoral devices identifies key landmarks from the mouth image captured by the above mentioned method. In this example, initial correlation to potential template intraoral devices involves mouth landmarks including teeth spacing of two or more teeth pairs, e.g., opposing molars, etc., and upper and lower jaw alignment. These at least three mouth landmark measurements are collected by the application to assist in selecting the size of an intraoral device such as through the lookup table or tables described above.

[0187] Operational data of the intraoral device, e.g., magnitude and frequency of mandibular advancement during sleep, may be collected for a large population of patients after an intraoral device has been selected or specifically manufactured for a patient. This may include usage data based on when each patient operates the intraoral device. Thus, compliance data such as how long and often a patient uses the intraoral device over a predetermined period of time may be determined from the collectedoperational data. The intraoral device may be operational to determine the efficacy of fit (as determined by the method set forth above) based on an strain gauges mounted with respect to the intraoral device. For example, said gauges may be configured to detect and measure unexpected movement in the intraoral device during mandibular advancement.

[0188] Patient input of feedback data may be collected via a user application executed on the computing device 2030 or the smartphone 2034. The user application may be part of the user application 2360 that instructs the user to obtain the mouth landmark features or a separate application. This may also include subjective data obtained via a questionnaire with questions to gather data on comfort preferences. For example, patient input may be gathered through a patient responding to subjective questions via the user application in relation to the comfort of the intraoral device. Other questions may relate to relevant user behaviour such as sleep characteristics. Subjective data may be as simple as a numerical rating as to comfort or a more detailed response. Such subjective data may also be collected from a graphical interface. For example, selected regions of discomfort in the intraoral device can be displayed on the graphical interface and selected by the patient (i.e., indicating areas of discomfort). The collected patient input data may be assigned to the patient database 2060 in Fig. 9. As set forth previously, the subjective data may be collected and utilised for intraoral device designs and features for an initial intraoral device design, i.e., a first device for use by the patient. The subjective input data from patients may also be used as feedback for intraoral device designs and features for future reference, i.e., for design of proceeding intraoral devices. Other subjective data may be collected related to the psychological safety of the patient. For example, questions such as whether the patient feels claustrophobic with that specific design of intraoral device (e.g., wall thickness may be too large) or how psychologically comfortable does the patient feel wearing the intraoral device next to their bed partner, may be asked and inputs may be collected.

[0189] Other data sources may collect data outside of use of the intraoral device and computing device that may be correlated to a particular intraoral device size. This may include patient demographic data such as age, gender or location; AHI severity indicating level of sleep apnea experienced by the patient. Other data may be the prescribed mandibular advancement settings for new patients of the intraoral device.

[0190] After selection of the intraoral device, the system 2000 continues to collect operational data from the intraoral device. The collected data is added to the databases 2060 and 2070. The feedback from new patients may be used to refine recommendations for adjustments to the intraoral device fit. For example, if operational data determines that a recommended intraoral device has poor fit, adjustments may be recommended to the patient. Through a feedback loop, the selection algorithm may be refined to learn particular aspects of mouth geometry that may be best suited to a particular intraoral design. This correlation may be used to refine the recommendation of an intraoral device to a new patient with that mouth geometry. The collected data and correlated intraoral device size data may thus provide additional updating to the selection and design criteria for intraoral devices. Thus, the system may provide additional insights for improving selection or design of an intraoral device for a patient.

[0191] In addition to intraoral device selection, the system 2000 may allow analysis of intraoral device selection in relation to respiratory therapy effectiveness and compliance. For example, external sensors may be utilised together with the computing device to monitor, e.g., breathing during sleep, to determine changes to e.g., apnea frequency, as a result of the intraoral device. The additional data allows optimization of the respiratory therapy based on data through a feedback loop.

[0192] Machine learning may be applied to provide correlations between intraoral device types and characteristics, and increasing compliance with respiratory therapy. The correlations may be employed to select or design characteristics for new intraoral device designs. Such machine learning may be executed by the server 2010. The intraoral device analysis algorithm may be learned with a training data set based on the outputs of favourable operational results and inputs including patient demographics and subjective data collected from patients. Machine learning may be used to discover correlation between desired intraoral device characteristics and predictive inputs such as mouth dimensions, patient demographics, operational data, and environmental conditions. Machine learning may employ techniques such as neural networks, clustering or traditional regression techniques. Test data may be used to test different types of machine learning algorithms and determine which one has the best accuracy in relation to predicting correlations.

[0193] The model for selection of an optimal intraoral device may be continuously updated by new input data from the system 2000 in Fig. 9. Thus, the model may become more accurate with greater use by the analytics platform.

[0194] As set forth previously, a second function of the system 2000 is a feedback data collection process that collects data for future intraoral device design or adjustment. Once the patient has been provided a recommended intraoral device and has used it for a period of time, such as two days, two weeks, or another period of time, the system 2000 can monitor usage and collect other data. Based on this collected data, if the intraoral device is not performing to a high standard as determined from adverse data indicating poor fit, dropping compliance, or unsatisfactory feedback, the system 2000 can re-evaluate the intraoral device selection (or design), and update the database 2060 and machine learning algorithm with the results for the patient. The system may then recommend a new intraoral device to suit the new collected data. For example, if a relatively high apnea rate is determined from data based off acoustic signatures or other sensors, the intraoral device may not effectively advance the mandible during REM sleep, which may signal the need for a different type of (or modification to the) intraoral device.

[0195] The system 2000 may also adjust the recommendation in response to satisfactory follow up data. For example, if operational data indicates a reduction in apnea frequency, but the patient experiences minor discomfort during use, minor modifications to the fit of the intraoral device may be recommended to the patient. Tradeoffs between performance and comfort may be used to provide follow up recommendations. The tradeoffs for an individual patient may be determined through a tree of inputs that are displayed to the patient by the application. For example, if a patient indicates gum irritation is a problem from a menu of potential problems, a graphic with locations of the potential irritation on a mouth image may be displayed to collect data as the specific location of irritation from the patient. The specific data may provide better correlation to the optimal intraoral device for the particular patient.

[0196] The present process allows for collection of feedback data and correlation with mouth feature data to provide designers of the intraoral device with data for designing other intraoral devices. As part of the application 2360 that collects mouth data or another application executed by a computing device such as thecomputing device 2030 or the mobile device 2034 in Fig. 9, feedback information relating to the intraoral device may be collected.

[0197] The application 2360 may collect initial patient information and provide security measures to protect data such as setting up passwords and the like. Once a patient sets up the application and correlates the application 2360 to the specific patient identity, the application may collect the feedback data.

[0198] If mouth data has already been gathered for the patient, the application 2360 will proceed to collect other data. If no previous mouth data has been collected for the patient, the application 2360 will provide an option to the patient for collecting mouth data. Fig. 11 A shows an exemplary embodiment of an interface 3700 of the application that shows a mouth image 3710. The patient may capture the mouth image 3710 similar to the mouth scanning process described above in relation to intraoral device selection. After the mouth image 3710 is displayed, a mouth mesh may be created. Fig. 11B shows an exemplary embodiment of a second interface 3720 that displays a mouth mesh 3712 created from the mouth image 3710. The mouth data may then be derived from the mouth mesh 3712 and stored, and transmitted to the server 2010 for storage in the database 2060 in Fig. 9.

[0199] The application 2360 collects all relevant data for evaluating characteristics for intraoral device design from the patient through other interfaces displayed. Fig. 11C shows an exemplary embodiment of a sleep data collection interface 3730 that allows collection of subjective patient data relating to sleep quality. This data may be collected and correlated to objective sleep data or associated operational data collected by the system explained above. The collection interface 3730 includes a question 3732 relating to sleep position. The interface 3730 includes choices for the user to select, including a back selection 3734, a stomach selection 3736, and a side selection 3738, for example. If a user is unsure, they may select an unsure option 3740. The interface 3730 thus collects sleep position data that is correlated with the particular user.

[0200] Fig. 12 is a flow diagram illustrating an exemplary embodiment of a method 1200 for selecting an intraoral device for a patient. The method comprises a step 1201 of capturing mouth feature data from the patient and a step 1202 of processing the mouth feature data to generate a model of the patient’s mouth. The method further comprises a step 1203 of selecting a template intraoral device from a plurality of template intraoral devices based on the generated model.4.3 GLOSSARY

[0201] For the purposes of the present technology disclosure, in certain forms of the present technology, one or more of the following definitions may apply. In other forms of the present technology, alternative definitions may apply.4.3.1 Respiratory cycle

[0202] Apnea: According to some definitions, an apnea is said to have occurred when flow falls below a predetermined threshold for a duration, e.g. 10 seconds. An obstructive apnea will be said to have occurred when, despite patient effort, some obstruction of the airway does not allow air to flow. A central apnea will be said to have occurred when an apnea is detected that is due to a reduction in breathing effort, or the absence of breathing effort, despite the airway being patent. A mixed apnea occurs when a reduction or absence of breathing effort coincides with an obstructed airway.

[0203] Expiratory portion of a breathing cycle: The period from the start of expiratory flow to the start of inspiratory flow.

[0204] Flow limitation: Flow limitation will be taken to be the state of affairs in a patient's respiration where an increase in effort by the patient does not give rise to a corresponding increase in flow. Where flow limitation occurs during an inspiratory portion of the breathing cycle it may be described as inspiratory flow limitation. Where flow limitation occurs during an expiratory portion of the breathing cycle it may be described as expiratory flow limitation.

[0205] Hypopnea: According to some definitions, a hypopnea is taken to be a reduction in flow, but not a cessation of flow. In one form, a hypopnea may be said to have occurred when there is a reduction in flow below a threshold rate for a duration. A central hypopnea will be said to have occurred when a hypopnea is detected that is due to a reduction in breathing effort. In one form in adults, either of the following may be regarded as being hypopneas:(i) a 30% reduction in patient breathing for at least 10 seconds plus an associated 4% desaturation; or(ii) a reduction in patient breathing (but less than 50%) for at least 10 seconds, with an associated desaturation of at least 3% or an arousal.

[0206] Hyperpnea: An increase in flow to a level higher than normal.

[0207] Inspiratory portion of a breathing cycle: The period from the start of inspiratory flow to the start of expiratory flow will be taken to be the inspiratory portion of a breathing cycle.

[0208] Patency (airway): The degree of the airway being open, or the extent to which the airway is open. A patent airway is open. Airway patency may be quantified, for example with a value of one (1) being patent, and a value of zero (0), being closed (obstructed).

[0209] Apnea-Hyperpnea Index (AHI): The combined average number of apneas and hypopneas that occur per hour of sleep.4.3.2 Anatomy4.3.2.1 Anatomy of the skull

[0210] Frontal bone: The frontal bone includes a large vertical portion, the squama frontalis, corresponding to the region known as the forehead.

[0211] Mandible: The mandible forms the lower jaw. The mental protuberance is the bony protuberance of the jaw that forms the chin.

[0212] Maxilla: The maxilla forms the upper jaw and is located above the mandible and below the orbits. The frontal process of the maxilla projects upwards by the side of the nose, and forms part of its lateral boundary.

[0213] Nasal bones: The nasal bones are two small oblong bones, varying in size and form in different individuals; they are placed side by side at the middle and upper part of the face, and form, by their junction, the "bridge" of the nose.

[0214] Nasion: The intersection of the frontal bone and the two nasal bones, a depressed area directly between the eyes and superior to the bridge of the nose.

[0215] Occipital bone: The occipital bone is situated at the back and lower part of the cranium. It includes an oval aperture, the foramen magnum, through which the cranial cavity communicates with the vertebral canal. The curved plate behind the foramen magnum is the squama occipitalis.

[0216] Orbit: The bony cavity in the skull to contain the eyeball.

[0217] Parietal bones: The parietal bones are the bones that, when joined together, form the roof and sides of the cranium.

[0218] Temporal bones: The temporal bones are situated on the bases and sides of the skull, and support that part of the face known as the temple.

[0219] Zygomatic bones: The face includes two zygomatic bones, located in the upper and lateral parts of the face and forming the prominence of the cheek.4.3.2.2 Anatomy of the respiratory system

[0220] Diaphragm: A sheet of muscle that extends across the bottom of the rib cage. The diaphragm separates the thoracic cavity, containing the heart, lungs and ribs, from the abdominal cavity. As the diaphragm contracts the volume of the thoracic cavity increases and air is drawn into the lungs.

[0221] Larynx: The larynx, or voice box houses the vocal folds and connects the inferior part of the pharynx (hypopharynx) with the trachea.

[0222] Lungs: The organs of respiration in humans. The conducting zone of the lungs contains the trachea, the bronchi, the bronchioles, and the terminal bronchioles. The respiratory zone contains the respiratory bronchioles, the alveolar ducts, and the alveoli.

[0223] Nasal cavity: The nasal cavity (or nasal fossa) is a large air filled space above and behind the nose in the middle of the face. The nasal cavity is divided in two by a vertical fin called the nasal septum. On the sides of the nasal cavity are three horizontal outgrowths called nasal conchae (singular "concha") or turbinates. To the front of the nasal cavity is the nose, while the back blends, via the choanae, into the nasopharynx.

[0224] Pharynx: The part of the throat situated immediately inferior to (below) the nasal cavity, and superior to the oesophagus and larynx. The pharynx is conventionally divided into three sections: the nasopharynx (epipharynx) (the nasal part of the pharynx), the oropharynx (mesopharynx) (the oral part of the pharynx), and the laryngopharynx (hypopharynx).4.4 OTHER REMARKS

[0225] A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in Patent Office patent files or records, but otherwise reserves all copyright rights whatsoever.

[0226] Unless the context clearly dictates otherwise and where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limit of that range, and any other stated orintervening value in that stated range is encompassed within the technology. The upper and lower limits of these intervening ranges, which may be independently included in the intervening ranges, are also encompassed within the technology, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the technology.

[0227] Furthermore, where a value or values are stated herein as being implemented as part of the technology, it is understood that such values may be approximated, unless otherwise stated, and such values may be utilized to any suitable significant digit to the extent that a practical technical implementation may permit or require it.

[0228] Furthermore, “approximately”, “substantially”, “about”, or any similar term used herein means + / - 5-10% of the recited value.

[0229] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this technology belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present technology, a limited number of the exemplary methods and materials are described herein.

[0230] When a particular material is identified as being used to construct a component, obvious alternative materials with similar properties may be used as a substitute. Furthermore, unless specified to the contrary, any and all components herein described are understood to be capable of being manufactured and, as such, may be manufactured together or separately.

[0231] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include their plural equivalents, unless the context clearly dictates otherwise.

[0232] All publications mentioned herein are incorporated herein by reference in their entirety to disclose and describe the methods and / or materials which are the subject of those publications. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present technology is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publicationprovided may be different from the actual publication dates, which may need to be independently confirmed.

[0233] The terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced.

[0234] The subject headings used in the detailed description are included only for the ease of reference of the reader and should not be used to limit the subject matter found throughout the disclosure or the claims. The subject headings should not be used in construing the scope of the claims or the claim limitations.

[0235] Although the technology herein has been described with reference to particular examples, it is to be understood that these examples are merely illustrative of the principles and applications of the technology. In some instances, the terminology and symbols may imply specific details that are not required to practice the technology. For example, although the terms "first" and "second" may be used, unless otherwise specified, they are not intended to indicate any order but may be utilised to distinguish between distinct elements. Furthermore, although process steps in the methodologies may be described or illustrated in an order, such an ordering is not required. Those skilled in the art will recognize that such ordering may be modified and / or aspects thereof may be conducted concurrently or even synchronously.

[0236] It is therefore to be understood that numerous modifications may be made to the illustrative examples and that other arrangements may be devised without departing from the spirit and scope of the technology.4.5 REFERENCE SIGNS LIST

Claims

CLAIMS1. A system for selecting an intraoral device for use in treatment of a sleep disorder for a patient, comprising: a data communication interface in communication with a scanning system and configured to receive mouth feature data of the patient; a memory storing machine readable instructions; and a processing system including one or more processors configured to execute the machine readable instructions, the processing system comprising: a model generating module configured to process the mouth feature data to generate a model of the patient’s mouth; and a selecting module configured to select a template intraoral device from a plurality of template intraoral devices, based on the generated model.

2. A system according to claim 1, wherein the selecting module is configured to correlate at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

3. A system according to claim 2, wherein the model generating module is configured to determine a measurement from mouth feature data of at least one reference feature of the patient from the group comprising of a jaw width, a jaw length, a profile depth, a dental arch profile.

4. A system according to any preceding claim, further comprising a scanning system including a scanning device configured to capture the mouth feature data of the patient.

5. A system according to claim 4, wherein the mouth feature data is based on image data acquired from the scanning system.

6. A system according to claim 5, wherein the scanning device is a mobile device with an application configured to capture mouth features.

7. A system according to any one of claims 4 to 6, wherein the scanning system comprises a user interface configured for providing guidance to the patient in acquiring the mouth feature data.

8. A system according to any preceding claim, further comprising a patient data collection interface configured to collect patient input data from the patient.

9. A system according to any preceding claim, further comprising a data communication interface configured to receive data of the patient; and wherein the model generating module and / or the selecting module is configured to process the patient data to generate the model of the patient’s mouth and / or to select a template intraoral device from a plurality of template intraoral devices.

10. The system according to claim 9, wherein the data of the patient comprises one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

11. The system according to claim 10, wherein the self-reported information is objective data entered by the patient and includes one or more of age, gender, weight, and height.

12. The system according to claims 10 or 11, wherein self-reported information is subjective data entered by the patient in relation to a previous intraoral device worn by the patient.

13. The system according to claim 12 wherein the self-reported information includes feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

14. The system according to any one of claims 10 to 13, wherein the self- reported data is collected by displaying questions in an interface on a mobile device.

15. A system according to any one of claims 10 to 14, when dependent on claim 8, wherein the self-reported data is acquired from the patient data collection interface.

16. A system according to any preceding claim, wherein the processing system is further configured to acquire patient treatment data whilst the patient is under treatment using the intraoral device; and wherein the selecting module is further configured to adaptively update the intraoral device selection based on the patient treatment data received over a period of use.

17. A system according to claim 16, wherein the patient treatment data comprises one or more of: physiological data correlated with sleep states of the patient, physiological responses to changes in mandibular advancement settings of the intraoral device, compliance with a therapy plan, number of apneas, AHI levels.

18. The system according to claims 16 or 17, wherein the patient treatment data further comprises self-reported information in relation to the patient.

19. A system according to any preceding claim, further comprising a machine learning module operable to determine mouth feature data and patient data from multiple patients correlated with the mouth model generating module and / or selecting module to adjust a characteristic of the mouth model generating module and / or selecting module.

20. A system according to claim 19, wherein the patient data is acquired from the multiple patients according to any one of claims 9 to 15 and / or as patient treatment data according to any one of claims 16 to 18.

21. A system for determining a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient, comprising: a data communication interface in communication with a scanning system and configured to receive mouth feature data of the patient; a memory storing machine readable instructions; anda processing system including one or more processors configured to execute the machine readable instructions, the processing system comprising: a model generating module configured to process the mouth feature data to generate a model of the patient’s mouth; and a specifying module configured to specify at least one characteristic of the intraoral device based on the generated model.

22. A system according to claim 21, wherein the data communication interface is configured to receive data of the patient; and wherein the model generating module and / or specifying module is configured to process the patient data to generate the model of the patient’s mouth and / or to specify at least one characteristic of the intraoral device based on the patient data.

23. The system according to claim 22, wherein the data of the patient comprises one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

24. The system according to claim 23, wherein the self-reported information is objective data entered by the patient and includes one or more of age, gender, weight, and height.

25. The system according to claims 23 or 24, wherein the self-reported information is subjective data entered by the patient in relation to a previous intraoral device worn by the patient.

26. The system according to claim 25 wherein the self-reported information includes feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

27. The system according to any one of claims 23 to 26, wherein the self- reported data is collected by displaying questions in an interface on a mobile device.

28. A system according to any one of claims 23 to 27, wherein the system further comprises a patient data collection interface to acquire the self reported information.

29. A system according to any one of claims 21 to 28, further comprising a machine learning module operable to determine mouth feature data and patient data from multiple patients correlated with the mouth model generating module and / or specifying module to adjust a characteristic of the mouth model generating module and / or specifying module.

30. A system according to claim 29, wherein the patient data is acquired from the multiple patients according to any one of claims 22 to 29.

31. A system for obtaining feedback from a patient using an intraoral device for treatment of a sleep disorder, the system comprising: a data communication interface in communication with a scanning system and configured to receive mouth feature data of the patient; a memory storing machine readable instructions; and a processing system including one or more processors configured to execute the machine readable instructions, the processing system comprising: a model generating module configured to process the mouth feature data to generate a model of the patient’s mouth; and an analysis module configured to correlate at least one characteristic of the mouth feature model to a previous mouth feature model of the patient.

32. A system according to claim 31, wherein the data communication interface is configured to receive data of the patient; and wherein the model generating module is configured to process the patient data to generate the model of the patient’s mouth based on the patient data.

33. The system according to claim 32, wherein the data of the patient comprises one or more of: physiological data of the patient; physiological data correlated with sleep states of the patient; and self-reported information in relation to the patient.

34. The system according to claim 33, wherein the self-reported information is objective data entered by the patient and includes one or more of age, gender, weight, and height.

35. The system according to claims 33 or 34, wherein the self-reported information is subjective data entered by the patient in relation to an intraoral device worn by the patient.

36. The system according to claim 35 wherein the self-reported information includes feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

37. The system according to any one of claims 33 to 36, wherein the self- reported data is collected by displaying questions in an interface on a mobile device.

38. A system according to any one of claims 33 to 37, further comprising a patient data collection interface configured to collect the self-reported data from the patient.

39. The system according to any one of claims 31 to 38, further comprising a specifying module configured to specify at least one characteristic of an intraoral device for the patient based on the correlated characteristic.

40. The system according to claim 39, wherein the at least one characteristic of an intraoral device is a template intraoral device selected from a plurality of template intraoral devices.

41. The system according to claim 39 or 40, wherein the at least one characteristic of an intraoral device is an adjustment to an intraoral device worn by the patient.

42. A system for specifying a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient, comprising: a data communication interface configured to receive data of the patient and a model of the patient’s mouth; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to: process the data of the patient to correlate the patient data with at least one characteristic of the mouth feature model; adjust the generated model of the patient’s mouth model based on the correlated at least one characteristic; and specify a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

43. The system according to claim 42 wherein the data of the patient comprises one or more of: physiological data of the patient, physiological data correlated with sleep states of the patient, and self-reported information in relation to the patient.

44. The system according to claim 43, wherein the self-reported information is objective data entered by the patient and includes one or more of age, gender, weight, and height.

45. The system according to claims 43 or 44, wherein self-reported information is subjective data entered by the patient in relation to an intraoral device worn by the patient.

46. The system according to claim 45 wherein the self-reported information includes feedback on psychologically and / or physiologically comfort of the patient in relation to the worn intraoral device.

47. The system according to claims 45 or 46, wherein the subjective data is collected by displaying questions in an interface on a mobile device.

48. The system according to any one of claims 42 to 47, wherein the at least one characteristic of an intraoral device is a template intraoral device selected from a plurality of template intraoral devices.

49. The system according to any one of claims 42 to 48, wherein the at least one characteristic of an intraoral device is an adjustment to an intraoral device worn by the patient.

50. A system for treatment of a sleep disorder for a patient using an intraoral device, comprising: a data communication interface in communication with a scanning system and sensors, the data communication interface configured to receive data of the patient; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to: process the patient data to generate a model of the patient’s mouth; select a template intraoral device from a plurality of template intraoral devices, based on the generated model; acquire the patient data from the sensors whilst under treatment using the intraoral device; and adapt the generated model and / or update the selection of the intraoral device based on the acquired patient data.

51. The system according to claim 50, wherein the scanning data is configured to receive mouth feature data of the patient and wherein the model of the patient’s mouth is generated using the mouth feature data.

52. The system according to claim 50 or 51, wherein the patient sensor data comprises one or more of: physiological data correlated with sleep states of the patient, physiological responses to changes in mandibular advancementsettings of the intraoral device, compliance with a therapy plan, number of apneas, AHI levels.

53. The system according to any one of claims 50 to 52, wherein the patient data further comprises self-reported information in relation to the patient.

54. The system according to any one of claims 50 to 53, wherein the control system is further configured to execute machine -readable instructions to specify a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

55. The system according to claim 54, wherein the at least one characteristic of an intraoral device is a template intraoral device selected from a plurality of template intraoral devices.

56. The system according to either claims 54 or 55, wherein the at least one characteristic of an intraoral device is an adjustment to the intraoral device.

57. A system according to any one of the preceding claims, wherein the sleep disorder is any one of snoring, Obstructive Sleep Apnea, Cheyne-Stokes Respiration, respiratory insufficiency, Obesity Hypoventilation Syndrome, Chronic Obstructive Pulmonary Disease, Neuromuscular Disease or Chest wall disorders.

58. A system according to any one of the preceding claims, wherein the intraoral device is a mandibular repositioning device or mandibular advancement device.

59. A method for adjusting an intraoral device for a patient, comprising: capturing mouth feature data from the patient; processing the mouth feature data to generate a model of the patient’s mouth; correlating at least one characteristic of the mouth feature model to a previous mouth feature model of the patient;adjusting the generated model of the patient’s mouth based on the correlation between the mouth feature model and the previous mouth feature model; and selecting a template intraoral device from a plurality of template intraoral devices based on the adjusted generated model.

60. A method for selecting an intraoral device for use in treatment of a sleep disorder for a patient, comprising: capturing mouth feature data from the patient; processing the mouth feature data to generate a model of the patient’s mouth; and selecting a template intraoral device from a plurality of template intraoral devices based on the generated model.

61. A method according to claim 60, further comprising correlating at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

62. A method according to claim 60 or 61, wherein generating the model comprises determining a measurement from mouth feature data of at least one reference feature of the patient from the group comprising of a jaw width, a jaw length, a profile depth, a dental arch profile.

63. A method according to any one of claims 60 to 62, further comprising using a mobile device with an application to capture mouth features to capture the mouth feature data from the patient.

64. A method according to claim 63, wherein the method is as otherwise defined in claim 59.

65. A method for selecting an intraoral device for use in treatment of a sleep disorder for a patient, the method comprising:scanning mouth feature data of the patient with a scanning system in communication with a data communication interface; processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory, the processing system: generating a model of the patient’s mouth with a model generating module; and selecting a template intraoral device from a plurality of template intraoral devices with a selecting module, the template intraoral device based on the generated model.

66. A method according to claim 65, wherein the selecting module is configured to correlate at least one measurement of the generated model to at least one stored measurement of the template intraoral devices.

67. A method for determining a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient, the method comprising: scanning mouth feature data of the patient with a scanning system in communication with a data communication interface; processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory, the processing system: generating a model of the patient’s mouth with a model generating module; and specifying at least one characteristic of the intraoral device with a specifying module, the at least one characteristic of the intraoral device based on the generated model.

68. A method according to claim 67, wherein the data communication interface is configured to receive data of the patient; and wherein the model generating module and / or specifying module is configured to process the patient data to generate the model of the patient’s mouth and / or to specify at least one characteristic of the intraoral device based on the patient data.

69. A method for obtaining feedback from a patient using an intraoral device for treatment of a sleep disorder, the method comprising: scanning mouth feature data of the patient with a scanning system in communication with a data communication interface; processing the mouth feature data of the patient with a processing system including one or more processors configured to execute machine readable instructions stored in a memory, the processing system: generating a model of the patient’s mouth with a model generating module; and correlating at least one characteristic of the mouth feature model to a previous mouth feature model of the patient with an analysis module.

70. A method according to claim 69, wherein the data communication interface is configured to receive data of the patient; and wherein the model generating module is configured to process the patient data to generate the model of the patient’s mouth based on the patient data.

71. A method for specifying a characteristic of an intraoral device for use in treatment of a sleep disorder for a patient, the method comprising: receiving data of the patient and a model of the patient’s mouth with a data communication interface;processing the data of the patient with a control system including one or more processors configured to execute machine-readable instructions stored in a memory, the processors: correlating the patient data with at least one characteristic of the mouth feature model; adjusting the generated model of the patient’s mouth model based on the correlated at least one characteristic; and specifying a characteristic of the intraoral device based on the adjusted model of the patient’s mouth.

72. A method for treatment of a sleep disorder for a patient using an intraoral device, the method comprising: receiving data of the patient with a scanning system and sensors in communication with a data communication interface; processing the data of the patient with a control system including one or more processors configured to execute machine-readable instructions stored in a memory, the processors: generating a model of the patient’s mouth; selecting a template intraoral device from a plurality of template intraoral devices, based on the generated model; acquiring the patient data from the sensors whilst under treatment using the intraoral device; and adapting the generated model based on the acquired patient data.

73. The method according to claim 72, wherein the scanning data is configured to receive mouth feature data of the patient and wherein the model of the patient’s mouth is generated using the mouth feature data.

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