Assessment of vital capacity, respiratory function, abdominal strength and / or thoracic strength or dysfunction

Abstract

1. A diagnostic device configured to assess a subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction, the diagnostic device comprising: a processor; a microphone; and memory storing computer-readable instructions that, when executed by the processor, cause the diagnostic device to: prompt the subject to perform a diagnostic task of emitting a long "aaah" sound for a predetermined duration; receive audio data associated with the diagnostic task via the microphone; extract digital biomarker data from the audio data; and apply an analytical model to the extracted digital biomarker data, the analytical model configured to generate an output indicative of the subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction.

Description

[Technical Field]

[0001] The present invention relates to diagnostic devices and computer-implemented methods configured to assess a subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or dysfunction. [Background technology]

[0002] Patients with spinal muscular atrophy (SMA) report difficulty speaking loudly (e.g., to be heard in noisy environments) and may experience shortness of breath while speaking.

[0003] Furthermore, the Scientific Advisory Working Group (SAWG) recommends that combining measurements from speech and respiratory assessments may help detect worsening bulbar function, which may be a precursor to serious events (such as aspiration). Additionally, patients with spinal muscular atrophy report difficulty speaking loudly, so the sound pressure level of speech may be important. (1) It is hypothesized that this could be a further outcome measure. (1) This is often incorrectly referred to as "loudness", but loudness is a psychoacoustic term referring to the subjective perception of sound pressure, and is influenced by factors such as the frequency-dependent sensitivity of human hearing, and the masking effects used in audio compression schemes such as MP3. Unless these effects on human hearing are modeled, the term level should be used.

[0004] It is desirable to measure respiratory function, lung capacity, and abdominal / thoracic strength / dysfunction, as this may be useful in tracking the status or progression of various conditions, such as SMA. The inventors have devised a method for doing so. Summary of the Invention

[0005] The present invention provides diagnostic devices and computer-implemented methods for assessing a subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or dysfunction. The output may be useful for assessing the subject's bulbar function and for tracking the status or progression of conditions that affect bulbar function, such as (but not exclusively) SMA.

[0006] More specifically, a first aspect of the present invention provides a diagnostic device configured to assess a subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction, the diagnostic device comprising a processor, a microphone, and a memory storing computer readable instructions that, when executed by the processor, cause the diagnostic device to: prompt the subject to perform a diagnostic task of emitting a long "aaah" sound for a predetermined duration; receive audio data associated with the diagnostic task via the microphone; extract digital biomarker data from the audio data; and apply an analytical model to the extracted digital biomarker data, the analytical model configured to generate an output indicative of the subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction.

[0007] By measuring respiratory function using the diagnostic device according to the first aspect of the present invention, it may be possible to effectively track the progression of various muscle disorders, such as SMA, in a subject through active testing of the subject. In particular, the computer-readable instructions, when executed by the processor, may further be configured to cause the diagnostic device to map the output to a bulbar function assessment grade indicative of the subject's bulbar function. As described in more detail later in this application, the diagnostic device according to the first aspect of the present invention may use the output indicative of respiratory function and / or the bulbar function assessment grade to indicate and / or track the presence or progression of a muscle disorder, such as SMA, in a subject or user.

[0008] In a preferred embodiment, the device is or includes a smartphone. This is advantageous because smartphones are virtually universally owned today. By performing a computer-implemented process as described on the smartphone, a user does not need to travel to, for example, a hospital or other clinical setting to measure a subject's respiratory function, lung capacity, abdominal strength, and / or thoracic strength or dysfunction. Other types of diagnostic devices, such as tablets, laptop computers, desktop computers, etc., may also be used. Alternatively, the diagnostic device may be a dedicated diagnostic device for assessing a subject's respiratory function, lung capacity, abdominal strength, and / or thoracic strength or dysfunction.

[0009] Generally, it is preferable to extract digital biomarker data only from portions of the recorded audio data in which the user is actually speaking. However, the recorded audio data may include background noise, for example, before the subject begins performing the diagnostic task and after completing the diagnostic task. More specifically, the audio data may include multiple segments, and extracting digital biomarker data may include applying a first algorithm to the audio data, the first algorithm configured to classify the segments of the audio data into active speech segments and background noise segments. As used herein, an "active speech segment" refers to a segment in which the user is actually performing the diagnostic task. Classifying the segments of the audio data into active speech segments and background noise segments includes generating timestamps indicating the start and end times of each respective active speech segment and background noise segment. The diagnostic task may be 10 to 60 seconds, 15 to 45 seconds, 20 to 40 seconds, or preferably about 30 seconds in length.

[0010] During each active speech segment, there may be a period during which the subject makes an "ah" sound and a period during which the user must pause, for example, to breathe, to initiate another "ah" sound. These may be referred to as voiced and non-voiced speech subsegments, respectively. More specifically, each active speech segment may include multiple subsegments, and extracting digital biomarker data may include applying a second algorithm to the active speech segments of the audio data, the second algorithm configured to classify the subsegments into voiced and non-voiced speech subsegments. Classification of subsegments may be accomplished in the same manner as classification of segments; i.e., classifying subsegments of the active speech segments of the audio data into voiced and non-voiced speech segments may include generating timestamps indicating the start and end times of each respective voiced and non-voiced speech subsegment. The term "voiced speech subsegment" may correspond to a subsegment during which the subject's vocal cords or lips are actually vibrating.

[0011] The nature of digital biomarker data and its extraction will now be discussed in more detail. Various types of digital biomarker data may be extracted from the recorded audio data, and the list of examples provided below is by no means exhaustive. Essentially, the digital biomarker types parameterize various aspects of a subject's respiratory function, lung capacity, abdominal strength, and / or thoracic strength that may be affected by, for example, reduced bulbar muscle function as a result of SMA.

[0012] In some cases, the digital biomarker data may include the total duration of voiced speech sub-segments within a predetermined duration of the diagnostic task, in these cases, extracting the digital biomarker data may include calculating the total duration of the voiced speech sub-segments based on, for example, the generated timestamps.

[0013] In some cases, the digital biomarker data may include a total number of voiced speech sub-segments within an active speech segment of the audio data, in which case extracting the digital biomarker data may include counting the total duration of the voiced speech sub-segments within the active speech segment, for example based on generated timestamps.

[0014] In some cases, the digital biomarker data may include the total duration of non-voiced speech sub-segments within a predetermined duration of the diagnostic task, in these cases, extracting the digital biomarker data may include calculating the total duration of the non-voiced speech sub-segments based on, for example, the generated timestamps.

[0015] In some cases, the digital biomarker data may include one or more of the duration of the longest and shortest non-voiced speech sub-segment within an active speech segment of the audio data.

[0016] When obtaining such data, the relative distance and orientation of the microphone with respect to the subject's mouth is important, for example, to ensure consistency of measurements. Thus, in some cases, the computer-readable instructions, when executed by a processor, may further cause the device to prompt the subject to place the device at a predetermined distance from the subject. Alternatively, or additionally, the computer-readable instructions, when executed by a processor, may further cause the device to prompt the subject to place the device in a predetermined location.

[0017] In some cases, the computer-readable instructions, when executed by the processor, may further cause the device to receive noise data via a microphone, calculate background noise from the noise data, and apply a correction to the audio data using the background noise.

[0018] In some examples, the output indicative of the subject's respiratory function may correspond to the digital biomarker data. For example, the output indicative of the respiratory function may correspond to the total duration of voiced speech subsegments within a predetermined duration, the total number of voiced speech subsegments within an active speech segment, the total duration of non-voiced speech subsegments within a predetermined duration, the duration of the longest non-voiced speech subsegment within an active speech segment, or the duration of the shortest non-voiced speech subsegment within an active speech segment.

[0019] We now discuss how an output indicative of respiratory function may be used to indicate the presence or progression of a muscular disorder, such as SMA. The computer-readable instructions, when executed by at least one processor, may cause the diagnostic device to apply a clinical interpretation model to the output indicative of respiratory function. The clinical interpretation model may be configured to output an indication of the presence or absence of a muscular disorder, such as SMA, in a user, or an indication of the progression of the muscular disorder in the user. The clinical interpretation model may be configured to compare the output indicative of respiratory function to a predetermined value and, based on the comparison, output an indication of the presence or absence of a muscular disorder, such as SMA. In particular, the clinical interpretation model may be configured to determine whether the output indicative of respiratory function is greater than a predetermined threshold. In particular, the clinical interpretation model may be configured to output an indication of the presence of a muscular disorder (e.g., that the user has PlwSMA) if the output indicative of respiratory function is determined to be greater than the predetermined threshold, and / or output an indication of the absence of a muscular disorder if the output indicative of respiratory function is determined to be equal to or less than the predetermined threshold. In other examples, the clinical interpretation model may be configured to output an indication of the presence of a muscle disorder (e.g., that the user has PlwSMA) if the output indicative of respiratory function is determined to be less than a predetermined threshold, and / or to output an indication of the absence of a muscle disorder if the output indicative of respiratory function is determined to be greater than or equal to a predetermined threshold.

[0020] A second aspect of the present invention provides a computer-implemented method for assessing a subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction, the computer-implemented method comprising the steps of prompting the subject to perform a diagnostic task in which the subject makes a long "ahh" sound for a predetermined duration; receiving audio data associated with the diagnostic task via a microphone; extracting digital biomarker data from the audio data; and applying an analytical model to the extracted digital biomarker data, the analytical model being configured to generate an output indicative of the subject's respiratory function, lung capacity, abdominal and / or thoracic strength or dysfunction. Preferably, the computer-implemented method of the second aspect of the present invention is executed by a processor of a diagnostic device, such as the diagnostic device of the first aspect of the present invention. It will be understood that any feature described above with respect to the first aspect of the present invention applies equally well to the second aspect of the present invention, unless the context clearly dictates otherwise or unless such combinations of features are clearly incompatible technically.

[0021] A third aspect of the present invention provides a computer program comprising instructions which, when executed by a processor of a computer (or other suitable data processing device), cause the processor to perform the computer-implemented method of the second aspect of the invention. A further aspect of the present invention provides a computer-readable storage medium having stored thereon the computer program of the third aspect of the invention.

[0022] The present invention includes combinations of the described embodiments and preferred features except where such combinations are expressly not permitted or explicitly avoided.

[0023] Embodiments of the present invention will now be described with reference to the accompanying drawings. [Brief explanation of the drawings]

[0024] [Figure 1]1 is a diagram of an exemplary environment in which a diagnostic device for assessing a subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or dysfunction is provided. [Figure 2] 1 is a flow diagram of a computer-implemented method for assessing a subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment. [Figure 3] FIG. 1 illustrates an example of a network architecture and data processing device that can be used to implement one or more exemplary aspects described herein. DETAILED DESCRIPTION OF THE INVENTION

[0025] Aspects and embodiments of the present invention will now be described with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art. All documents mentioned herein are incorporated by reference.

[0026] In the following description of various aspects, reference is made to the accompanying drawings, which form a part hereof, and which show, by way of illustration, various embodiments in which the aspects described herein may be practiced. It is to be understood that other aspects and / or embodiments may be utilized and structural and functional changes may be made without departing from the scope of the described aspects and embodiments.

[0027] The aspects described herein are capable of other embodiments and of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. Rather, the phrases and terms used herein should be given their broadest interpretation and meaning. The use of "including" and "comprising" and variations thereof is intended to encompass the items listed thereafter and their equivalents, as well as additional items and their equivalents. The use of the terms "mounted," "connected," "coupled," "disposed," "engaged," and similar terms is meant to include both direct and indirect mounting, connecting, coupling, disposing, and engaging.

[0028] The systems, methods, and devices described herein provide diagnostic devices and computer-implemented methods for assessing, measuring, or determining a subject's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment, for example, for a patient suffering from a muscular disorder such as certain SMA. In some cases, the diagnostic device may be in the form of a mobile device, particularly a smartphone, with a specific software application installed. The software application may be configured to execute (or cause a processor of the mobile device to execute) a corresponding computer-implemented method.

[0029] In some cases, the diagnosis acquires or receives sensor data from one or more sensors associated with the mobile device when the subject uses the mobile device to interact with the software application. In some cases, the sensors may be located within the mobile device. In some cases, data indicative of the subject's respiratory function, lung capacity, abdominal strength, and / or thoracic strength or impairment is derived, calculated, or extracted from the received or acquired sensor data. In some cases, an assessment of the severity and progression of symptoms of muscle disorders, particularly SMA, in the subject may be determined based on the extracted sensor features.

[0030] In embodiments of the present invention, the diagnostic device may prompt the subject to perform a diagnostic task. In some cases, the diagnostic task is anchored or modeled after established methods and standardized tests. In some cases, in response to the subject performing the diagnostic task, the diagnostic device acquires or receives sensor data via one or more sensors. In some cases, the sensors may be in a mobile device or in a wearable sensor worn by the subject. In some cases, sensor features associated with symptoms of myopathy, particularly SMA, are extracted from the received or acquired sensor data. In some cases, an assessment of the severity and progression of symptoms of myopathy, particularly SMA, in the subject is determined based on the extracted features of the sensor data.

[0031] Assessment of the severity and progression of symptoms of myopathies, particularly SMA, using a diagnostic according to the present disclosure correlates well with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing. Exemplary diagnostics according to the present disclosure can be used outside of a clinic environment, thus offering advantages in cost, ease of subject monitoring, and convenience for the subject. This facilitates frequent, particularly daily, subject monitoring and testing, resulting in a better understanding of disease stages and providing disease-related insights useful to both the clinical and research communities. Exemplary diagnostics according to the present disclosure can provide earlier detection of even small changes in a subject's respiratory function, vital capacity, abdominal and / or chest strength, or dysfunction, which can indicate the presence or progression of myopathies, particularly SMA, in a subject, and can therefore be used for better disease management, including personalized treatment.

[0032] FIG. 1 is a diagram of an exemplary environment in which a diagnostic device 105 for assessing a subject's 110 respiratory function, lung capacity, abdominal strength, and / or thoracic strength or dysfunction may be provided. In some cases, the device 105 may be a smartphone, smartwatch, or other mobile computing device. The device 105 includes a display screen 160. In some cases, the display screen 160 may be a touchscreen. The device 105 includes at least one processor 115 and a memory 125 that stores computer instructions for a symptom monitoring application 130 that, when executed by the at least one processor 115, causes the device 105 to assess the subject's respiratory function, lung capacity, abdominal strength, and / or thoracic strength or dysfunction. The device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. In some cases, the one or more sensors associated with the device are at least one of sensors disposed within the device and sensors worn by the subject and configured to communicate with the device. In FIG. 1, the sensors associated with the device 105 include a first sensor 120 , such as a microphone, located within the device 105 .

[0033] The device 105 extracts digital biomarker data from the received first sensor data that can be used to determine the subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or dysfunction.

[0034] The device 105 determines the subject's 110's respiratory function, lung capacity, abdominal and / or thoracic strength or impairment based on the extracted features. Optionally, the device 105 transmits the extracted features to the server 150 via the network 180. Optionally, the device 105 transmits first sensor data to the server 150 via the network 180. The server 150 includes at least one processor 155 and a memory 161 storing computer instructions for a symptom assessment application 170, which, when executed by the server's processor 155, cause the processor 155 to determine the subject's 110's respiratory function, lung capacity, abdominal and / or thoracic strength or impairment based on the extracted features received by the server 150 from the device 105. Optionally, the symptom assessment application 170 may cause the processor 155 to extract features from the sensor data received from the device 105. In some cases, the symptom assessment application 170 may determine the subject's 110's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment based on extracted features of the sensor data, which may be received from the device 105, and a subject database 175 stored in the memory 160. In some cases, the subject database 175 may include subject data and / or clinical data. In some cases, the subject database 175 may include in-clinic and sensor-based measures of respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment. In some cases, the subject database 175 may be independent from the server 150. In some cases, the server 150 transmits the determined subject's 110's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment to the device 105. In some cases, the device 105 may output the subject's 110's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment. In some cases, the device 105 may communicate information to the subject 110 based on the assessment.In some cases, an assessment of the subject's 110 respiratory function, lung capacity, abdominal strength and / or chest strength or dysfunction may be communicated to a clinician, who may determine an individualized treatment for the subject 110 based on the assessment.

[0035] In some cases, the computer instructions of the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to determine the subject's 110's respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment based on active testing of the subject 110. The device 105 prompts the subject 110 to perform one or more tasks. In some cases, prompting the subject to perform the one or more diagnostic tasks includes prompting the subject to make a continuous "aaah" sound for as long as possible.

[0036] In response to the subject 110 performing one or more diagnostic tasks, the diagnostic device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. The device 105 extracts various digital biomarker data from the received first sensor data from which an assessment of the subject's 110's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment may be made. Symptoms of myopathy in the subject 110, particularly in SMA, may include symptoms affecting the subject's 110's respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment.

[0037]

[0023] Figure 2 illustrates an exemplary method for assessing a subject's 110 respiratory function, lung capacity, abdominal strength, and / or chest strength or impairment based on active testing of the subject using the exemplary device 105 of Figure 1. While Figure 2 is described with reference to Figure 1, it should be noted that the steps of the method of Figure 2 may be performed by other systems. The computer-implemented method includes, at step 205, prompting the subject to perform diagnostic tasks such as those outlined above. The method includes receiving a plurality of sensor data (step 210), e.g., via a microphone, in response to the subject performing one or more tasks.

[0038] Next, in step 215, digital biomarker data is extracted from the sensor data and an analytical model is applied to the digital biomarker data.

[0039] In step 220, data indicative of the subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment is output, for example, by processor 107 generating instructions that, when executed by display component 160 of device 105, cause display component 160 to display an output indicative of the subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment. Alternatively, as outlined elsewhere in this application, the calculated data indicative of the subject's respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment may be transmitted to server 150.

[0040] As explained above, assessment of the severity and progression of symptoms of muscle disorders, particularly SMA, using diagnostics according to the present disclosure correlates well with clinical outcome-based assessments and may therefore replace clinical subject monitoring and testing.

[0041] FIG. 3 illustrates an example of a network architecture and data processing devices that may be used to implement one or more exemplary embodiments described herein, such as those described in FIGS. 1 and 2. Various network nodes 303, 305, 307, and 309 may be interconnected via a wide area network (WAN) 301, such as the Internet. Other networks, including private intranets, corporate networks, LANs, wireless networks, and personal networks (PANs), may also or instead be used. Network 301 is illustrative and may be replaced by fewer or additional computer networks. The local area network (LAN) may have one or more of any known LAN topologies and may use one or more of a variety of protocols, such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) may be connected to one or more of the networks via twisted pair wire, coaxial cable, optical fiber, radio waves, or other communication media.

[0042] As used herein and shown in the drawings, the term "network" refers not only to a system in which remote storage devices are coupled together through one or more communication paths, but also to stand-alone devices that may be coupled to such a system from time to time with storage capabilities. Thus, the term "network" includes not only a "physical network" but also a "content network" consisting of data—belonging to a single entity—that resides across all physical networks.

[0043] The components may include a data server 303, a web server 305, and client computers 307, 309. The data server 303 provides overall access, control, and management of the databases and control software for implementing one or more exemplary embodiments described herein. The data server 303 may be connected to a web server 305 with which users interact and obtain requested data. Alternatively, the data server 303 may itself operate as a web server and be connected directly to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet) through a direct or indirect connection, or through some other network. Users may interact with the data server 303 using remote computers 307, 309, for example, by using a web browser to connect to the data server 303 through one or more publicly accessible websites hosted by the web server 305. The client computers 307, 309 may be used in conjunction with the data server 303 to access data stored in the data server 303, or may be used for other purposes. For example, from client device 307, a user may access web server 305 using an internet browser as known in the art or by executing a software application that communicates with web server 305 and / or data server 303 over a computer network (such as the internet). In some cases, client computer 307 may be a smartphone, smartwatch, or other mobile computing device and may implement a diagnostic device such as device 105 shown in FIG. 1. In some cases, data server 303 may implement a server such as server 150 shown in FIG. 1.

[0044] The servers and applications may be combined on the same physical device, maintain separate virtual or logical addresses, or may reside on separate physical devices. Figure 1 shows only one example of a network architecture that may be used, and those skilled in the art will appreciate that the specific network architecture and data processing devices used may vary and are secondary to the functions they provide, as described further herein. For example, the services provided by web server 305 and data server 303 may be combined on a single server.

[0045] Each of the components 303, 305, 307, and 309 may be any type of known computer, server, or data processing device. The data server 303 may include, for example, a processor 311 that controls the overall operation of the rate server 303. The data server 303 may further include RAM 313, ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.), and memory 321. The I / O 319 may include various interface units and drivers for reading, writing, displaying, and / or printing data or files. The memory 321 may further store operating system software 323 for controlling the overall operation of the data processing device 303, control logic 325 for directing the data server 303 to perform aspects described herein, and other application software 327 that provides secondary support and / or other functions that may or may not be used in combination with other aspects described herein. The control logic is sometimes referred to herein as data server software 325. The functionality of the data server software may refer to actions or decisions that are made automatically based on rules coded into the control logic, actions or decisions that are made manually by a user by providing input to the system, and / or a combination of automated processing based on user input (e.g., queries, data updates, etc.).

[0046] Additionally, memory 321 may store data used to perform one or more aspects described herein, including first database 329 and second database 331. In some cases, the first database may include a second database (e.g., as another table, report, etc.). That is, information may be stored in a single database or, alternatively, separated into different logical, virtual, or physical databases, depending on the system design. Devices 305, 307, and 309 may have architectures similar to or different from that described with respect to device 303. Those skilled in the art will understand that the functionality of data processing device 303 (or devices 305, 307, and 309) described herein may be distributed across multiple data processing devices, for example, to distribute processing load across multiple computers, to segregate transactions based on geographic location, user access level, quality of service (QoS), etc.

[0047] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions, such as in one or more program modules executed by one or more computers or other devices described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. Modules may be written in source code programming languages ​​that are subsequently compiled for execution, or in scripting languages ​​such as, but not limited to, HTML or XML. Computer-executable instructions may be stored in computer-readable media such as hard disks, optical disks, removable storage media, solid-state memory, RAM, etc. As will be appreciated by those skilled in the art, the functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, the functionality may be embodied, in whole or in part, in firmware or hardware equivalents, such as integrated circuits, field-programmable gate arrays (FPGAs), etc. Particular data structures may be used to more effectively implement one or more aspects, and such data structures are contemplated within the scope of the computer-executable instructions and computer-usable data described herein.

[0048] The features disclosed in the foregoing description, or in the following claims, or in the accompanying drawings, and expressed in a specific form or in terms of means for performing a disclosed function or a method or process for obtaining a disclosed result, may be utilized, individually or in any combination of such features, as appropriate, to realize the invention in its various forms.

[0049] While the present invention has been described in conjunction with the foregoing exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art given this disclosure. Accordingly, the exemplary embodiments of the invention described above are considered to be illustrative and not limiting. Various changes may be made to the described embodiments without departing from the spirit and scope of the invention.

[0050] For the avoidance of doubt, any theoretical explanations provided herein are provided for the purpose of enhancing the understanding of the reader, and the inventors do not wish to be bound by any of these theoretical explanations.

[0051] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0052] Throughout this specification, including the claims which follow, unless the context requires otherwise, the words "comprise" and "include", as well as variations such as "comprises", "comprising", and "including", are understood to mean the inclusion of a stated integer or step or steps but not the exclusion of any other integer or step or steps.

[0053] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. The term "about" with respect to numerical values ​​is optional and means, for example, + / - 10%.

Claims

1. 1. A diagnostic device configured to assess respiratory function, lung capacity, abdominal strength and / or thoracic strength or dysfunction of a subject, comprising: a processor; A microphone and a memory for storing computer readable instructions; Equipped with The computer readable instructions, when executed by the processor, cause the diagnostic device to: prompting the subject to perform a diagnostic task of making a long "aaah" sound for a predetermined duration; receiving audio data associated with the diagnostic task via the microphone; extracting digital biomarker data from the audio data; applying an analytical model to the extracted digital biomarker data, the analytical model configured to generate an output indicative of the subject's respiratory function, vital capacity, abdominal strength and / or thoracic strength or impairment; A diagnostic device that performs the following.

2. the audio data includes a plurality of segments; 2. The diagnostic device of claim 1, wherein extracting the digital biomarker data comprises applying a first algorithm to the audio data, the first algorithm configured to classify the segments of the audio data into active speech segments and background noise segments.

3. 3. The diagnostic device of claim 2, wherein classifying the segments of the audio data into active speech segments and background noise segments includes generating timestamps indicating start and end times of each respective active speech segment and background noise segment.

4. each active speech segment includes a plurality of sub-segments; 4. The diagnostic device of claim 2 or 3, wherein extracting the digital biomarker data comprises applying a second algorithm to the active speech segments of the audio data, the second algorithm configured to classify the subsegments into voiced speech subsegments and non-voiced speech subsegments.

5. 5. The diagnostic apparatus of claim 4, wherein classifying the sub-segments of the active speech segment of the audio data into voiced speech segments and non-voiced speech segments includes generating timestamps indicating start and end times of each respective voiced and non-voiced speech sub-segment.

6. The diagnostic device of claim 5 , wherein the digital biomarker data includes a total duration of voiced speech sub-segments within the predetermined duration of the diagnostic task.

7. 7. The diagnostic device of claim 5 or 6, wherein the digital biomarker data includes a total number of voiced speech sub-segments within the active speech segments of the audio data.

8. 8. The diagnostic device of claim 5, wherein the digital biomarker data includes one or more of the duration of a longest voiced speech subsegment and a shortest voiced speech subsegment within the active speech segment of the audio data.

9. The diagnostic apparatus of any one of claims 5 to 8, wherein the digital biomarker data comprises a total duration of non-voiced speech sub-segments within the predetermined duration of the diagnostic task.

10. 10. The diagnostic device of claim 5, wherein the digital biomarker data includes one or more of the duration of a longest non-voiced speech sub-segment and a shortest non-voiced speech sub-segment within the active speech segment of the audio data.

11. 11. The diagnostic device of claim 1, wherein the computer-readable instructions, when executed by the processor, further cause the device to prompt the subject to position the device a predetermined distance from the subject.

12. 12. The diagnostic device of claim 1, wherein the computer-readable instructions, when executed by the processor, further cause the device to prompt the subject to place the device in a predetermined position.

13. The computer-readable instructions, when executed by the processor, cause the device to: receiving noise data via the microphone; calculating background noise from the noise data; applying correction to the audio data using the background noise; The diagnostic device according to any one of claims 1 to 10, further comprising:

14. Diagnostic apparatus according to any preceding claim, wherein the audio data is received over a period of 30 seconds.

15. The diagnostic device according to any one of claims 1 to 14, wherein the device is a smartphone.

16. 16. The diagnostic device of claim 1, wherein the computer readable instructions, when executed by the at least one processor, cause the diagnostic device to apply a clinical interpretation model to the output indicative of the respiratory function, the clinical interpretation model outputting an indication of the presence or absence of a muscle disorder.

17. 17. The diagnostic apparatus of claim 16, wherein the clinical interpretation model is configured to compare the output indicative of the respiratory function with a predetermined value and output an indication of the presence or absence of the myopathy based on the comparison.

18. The clinical interpretation model comprises: determining whether the output indicative of respiratory function is greater than a predetermined threshold; outputting an indication of the presence of a myopathy if the output indicative of respiratory function is determined to be greater than the predetermined threshold; outputting an indication of the absence of myopathy if the output indicative of respiratory function is determined to be equal to or less than the predetermined threshold.

20. The diagnostic device of claim 17, configured to:

19. The clinical interpretation model comprises: determining whether the output indicative of respiratory function is less than a predetermined threshold; outputting an indication of the presence of a myopathy if the output indicative of respiratory function is determined to be less than the predetermined threshold; outputting an indication of the absence of myopathy if it is determined that the output indicative of respiratory function is equal to or greater than the predetermined threshold.

20. The diagnostic device of claim 17, configured to:

20. 1. A computer-implemented method configured to assess respiratory function, lung capacity, abdominal strength and / or thoracic strength or impairment of a subject, the method comprising: prompting the subject to perform a diagnostic task of making a long "aaah" sound for a predetermined duration; receiving audio data associated with the diagnostic task via a microphone; extracting digital biomarker data from said audio data; applying an analytical model to the extracted digital biomarker data, the analytical model configured to generate an output indicative of the subject's respiratory function, vital capacity, abdominal strength and / or thoracic strength or impairment; 11. A computer-implemented method comprising:

21. 21. The computer-implemented method of claim 20, further comprising applying a clinical interpretation model to the output indicative of the respiratory function, the clinical interpretation model outputting an indication of the presence or absence of myopathy or an indication of progression of myopathy.

22. A computer-implemented method according to claim 20 or 21, executed by a processor of a diagnostic device according to any one of claims 1 to 19.

23. the steps of prompting the subject and receiving the audio data are performed by a processor of a diagnostic device, the steps of extracting digital biomarker data and applying a respiratory function assessment model are performed by a processor of a server, and the diagnostic device is configured to transmit the audio data to the server, and the diagnostic device: at least one processor; A microphone and a memory for storing computer readable instructions; Equipped with The computer readable instructions, when executed by the at least one processor, cause the diagnostic device to: prompting the subject to perform a diagnostic task of making a long "aaah" sound for a predetermined duration; receiving, via the microphone, audio data associated with the diagnostic task; 22. The computer-implemented method of claim 20 or 21,

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