Evaluation of vital capacity, respiratory function, abdominal strength and / or chest strength or impairment

Abstract

A diagnostic device configured to assess respiratory function, vital capacity, abdominal strength and / or thoracic strength or impairment of a subject, 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 that emits a long "abat" sound for a predetermined duration; receiving, via the microphone, audio data associated with the diagnostic task; extracting digital biomarker data from the audio data; and applying an analytical model to the extracted digital biomarker data, the analytical model configured to generate an output indicative of the respiratory function, vital capacity, abdominal strength and / or chest strength or impairment of the subject.

Description

Technical Field

[0001] The present invention relates to diagnostic devices and computer-implemented methods configured to assess a subject's respiratory function, vital capacity, abdominal strength and / or chest strength or impairment. Background Art

[0002] People with spinal muscular atrophy (SMA) report difficulty speaking loudly (e.g., making themselves heard in a noisy environment) and may experience shortness of breath when speaking.

[0003] In addition, the Scientific Advisory Working Group (SAWG) suggests that combining measurements from speech and respiratory assessments can help detect deterioration in bulbar function that could indicate an emergency event such as aspiration. In addition, because people with spinal muscular atrophy report difficulty speaking loudly, the sound pressure level of speech is inferred. 1 May be additional outcome indicators.

[0004] Measuring respiratory function, vital capacity and abdominal / thoracic strength / injury is desirable as this can help track the status or progression of various conditions (such as SMA). The present inventors have designed a protocol to do this. Summary of the invention

[0005] The present invention provides a diagnostic device and computer-implemented method for assessing a subject's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment. The output can be used to assess a subject's bulbar function and track the status or progression of conditions affecting bulbar function, such as (but not limited to) SMA.

[0006] More specifically, the first aspect of the present invention provides a diagnostic device configured to assess a subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury, the diagnostic device comprising: a processor; a microphone; and a memory storing computer-readable instructions which, when executed by the processor, cause the diagnostic device to: prompt the subject to perform a diagnostic task of emitting a long "ah" 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 analysis model to the extracted digital biomarker data, the analysis model being configured to generate an output indicative of the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury.

[0008] By measuring respiratory function using a diagnostic device according to the first aspect of the present invention, the progress of various muscle disabilities (such as SMA) of the subject can be effectively tracked through active testing of the subject. In particular, the computer-readable instructions, when executed by the processor, can be further configured to cause the diagnostic device to map the output to a medullary function assessment grade indicating the medullary function of the subject. As described in detail later in this application, the diagnostic device according to the first aspect of the present invention can use an output indicating respiratory function and / or medullary function assessment grade to indicate and / or track the presence or progress of muscle disabilities (such as SMA) of a subject or user.

[0009] In a preferred embodiment, the device is or includes a smartphone. This is advantageous because almost everyone has a smartphone nowadays. By implementing a computer-implemented process (such as the process described on a smartphone), the user does not need to go to, for example, a hospital or other clinical environment in order to measure the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury. Other types of diagnostic devices can be used, such as tablet computers, laptop computers, desktop computers, etc. Alternatively, the diagnostic device can be a dedicated diagnostic device for assessing the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury.

[0010] It is usually preferred to extract digital biomarker data only from the part of the recorded audio data where the user actually utters the sound. However, the recorded audio data may include background noise before the subject begins to perform a diagnostic task and after they complete the diagnostic task, for example. 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 being configured to classify the segments of the audio data into active speech segments and background noise segments. In this article, "active speech segments" refer to the segments where the user actually performs a diagnostic task. Classifying the segments of audio data into active speech segments and background noise segments includes generating a timestamp indicating the start and end time of each corresponding active speech segment and background noise segment. The length of the diagnostic task may be 10 to 60 seconds, 15 to 45 seconds, 20 to 40 seconds, or preferably about 30 seconds.

[0011] During each active speech segment, there may be times when the subject is making an "ah" sound, and times when the user must pause, for example, to breathe, to start another "ah" sound. These may be referred to as voiced speech subsegments 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 segment of the audio data, the second algorithm being configured to classify the subsegments into voiced speech subsegments and non-voiced speech subsegments. The classification of subsegments may be achieved in the same manner as the classification of segments, i.e., classifying subsegments of the active speech segment of the audio data into voiced speech segments and non-voiced speech segments may include generating timestamps indicating the start and end times of each respective voiced speech subsegment and non-voiced speech subsegment. The term "voiced speech subsegment" may correspond to a subsegment where the subject's vocal cords (or folds) actually vibrate.

[0012] The nature of digital biomarker data and its extraction is now discussed in more detail. Various types of digital biomarker data can be extracted from the recorded audio data, and the list of examples listed below is by no means exhaustive. Essentially, the types of digital biomarkers parameterize various aspects of the subject's respiratory function, vital capacity, abdominal strength, and / or chest strength, which aspects may be affected by decreased bulbar muscle function (e.g., due to SMA).

[0013] In some cases, the digital biomarker data may include the total duration of the 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.

[0014] In some cases, the digital biomarker data may include the total number of voiced speech subsegments in an active speech segment of the audio data. In these cases, extracting the digital biomarker data may include counting the total duration of voiced speech subsegments in the active speech segment based on, for example, the generated timestamps.

[0015] In some cases, the digital biomarker data may include the total duration of the unvoiced speech subsegments within a predetermined duration of the diagnostic task. In these cases, extracting the digital biomarker data may include calculating the total duration of the unvoiced speech subsegments based on, for example, the generated timestamps.

[0016] In some cases, the digital biomarker data may include one or more of the duration of the longest unvoiced speech subsegment and the shortest unvoiced speech subsegment in the active speech segment of the audio data.

[0017] When obtaining such data, the relative distance and orientation of the microphone relative to the subject's mouth is important, for example, to ensure consistency of the measurement results. Therefore, in some cases, the computer-readable instructions, when executed by the 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 the processor, may further cause the device to prompt the subject to place the device at a predetermined position.

[0018] In some cases, the computer readable instructions, when executed by the processor, may further cause the apparatus to: receive noise data via the microphone; calculate background noise based on the noise data; and apply a correction to the audio data using the background noise.

[0019] In some examples, the output indicative of the subject's respiratory function can correspond to the digital biomarker data. For example, the output indicative of respiratory function can correspond to the following: the total duration of voiced speech subsegments within a predetermined duration, the total number of voiced speech subsegments in an active speech segment, the total duration of unvoiced speech subsegments within a predetermined duration, the duration of the longest unvoiced speech subsegment in an active speech segment, or the duration of the shortest unvoiced speech subsegment in an active speech segment.

[0020] Now discuss how to use the output indicating respiratory function to indicate the presence or progress of muscle disability (such as SMA). Computer readable instructions can cause the diagnostic device to apply the clinical interpretation model to the output indicating respiratory function when executed by the at least one processor. The clinical interpretation model can be configured to output an indication of the presence or absence of muscle disability (such as SMA) for the user, or an indication of the progress of muscle disability for the user. The clinical interpretation model can be configured to compare the output indicating respiratory function with a predetermined value, and output an indication of the presence or absence of muscle disability (such as SMA) based on the comparison. In particular, the clinical interpretation model can be configured to determine whether the output indicating respiratory function is greater than a predetermined threshold. In some instances, the clinical interpretation model can be configured to output an indication of the presence of muscle disability (e.g., the user is PlwSMA) if it is determined that the output indicating respiratory function is greater than a predetermined threshold, and / or if it is determined that the output indicating respiratory function is less than or equal to a predetermined threshold, then output an indication that muscle disability does not exist. In other instances, the clinical interpretation model can be configured to output an indication that muscle disability exists (e.g., the user is a PlwSMA) if it is determined that the output indicative of respiratory function is less than a predetermined threshold, and / or to output an indication that muscle disability does not exist if it is determined that the output indicative of respiratory function is greater than or equal to a predetermined threshold.

[0021] The second aspect of the present invention provides a computer-implemented method for assessing a subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury, the computer-implemented method comprising the following steps: prompting the subject to perform a diagnostic task of emitting a long "ah" 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 analysis model to the extracted digital biomarker data, the analysis model being configured to generate an output indicating the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury. Preferably, the computer-implemented method of the second aspect of the present invention is performed by a processor of a diagnostic device such as the diagnostic device of the first aspect of the present invention. It should be understood that the optional features described above with respect to the first aspect of the present invention are equally well applicable to the second aspect of the present invention, unless the context clearly specifies otherwise, or the combination of such features is obviously technically incompatible.

[0022] A third aspect of the invention provides a computer program comprising instructions which, when executed by a processor of a computer (or other suitable data processing apparatus), cause the processor to perform the computer-implemented method of the second aspect of the invention. Another aspect of the invention provides a computer-readable storage medium having stored thereon the computer program of the third aspect of the invention.

[0023] The present invention includes any combination of described aspects and preferred features unless such a combination is expressly impermissible or explicitly avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Embodiments of the present invention will now be described with reference to the accompanying drawings, in which:

[0025] - Figure 1 is a simplified diagram of an exemplary environment in which a diagnostic device is provided for assessing respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of a subject.

[0026] - Figure 2 is a flow chart of a computer-implemented method for assessing respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of a subject.

[0027] - Figure 3 An example of a network architecture and data processing device that can be used to implement one or more illustrative aspects described herein is shown.

[0028] Detailed description with drawings

[0029] Aspects and embodiments of the present invention will now be discussed with reference to the accompanying drawings. Other aspects and embodiments will be apparent to those skilled in the art. All documents mentioned in this text are incorporated herein by reference.

[0030] In the following description of the various aspects, reference is made to the accompanying drawings which form a part hereof and in which are shown by way of illustration various embodiments in which the aspects described herein may be practiced. It should be understood that other aspects and / or embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the described aspects and embodiments.

[0031] The aspects described herein can be used in other embodiments and can be practiced or executed in various ways. In addition, it should be understood that the words and terms used herein are for illustrative purposes and should not be considered restrictive. On the contrary, the phrases and terms used herein will be given their broadest interpretation and meaning. The use of "including" and "comprising" and their variations is meant to cover the items listed thereafter and their equivalents, as well as other items and their equivalents. The use of the terms "install", "connect", "couple", "position", "engage" and similar terms is intended to include direct and indirect installation, connection, coupling, positioning and engagement.

[0032] The systems, methods, and devices described herein provide diagnostic devices and computer-implemented methods for assessing, measuring, or determining respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment in a subject (e.g., a patient suffering from muscle disability, such as, in particular, SMA). In some cases, the diagnostic device may be in the form of a mobile device, in particular a smartphone, having a specific software application installed thereon. The software application may be configured to execute (or cause a processor of the mobile device) a corresponding computer-implemented method.

[0033] In some cases, the diagnosis obtains or receives sensor data from one or more sensors associated with the mobile device when the subject interacts with the software application using the mobile device. In some cases, the sensor can be within the mobile device. In some cases, data indicating the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or injury is derived, calculated or extracted from the received or obtained sensor data. In some cases, an assessment of the severity and progression of the subject's muscle disability, particularly SMA, can be determined based on the extracted sensor features.

[0034] In an embodiment of the present invention, the diagnostic device can prompt the subject to perform a diagnostic task. In some cases, the diagnostic task is anchored in an established method and standardized test, or the diagnostic task is modeled after an established method and standardized test. In some cases, in response to the subject performing the diagnostic task, the diagnosis obtains or receives sensor data via one or more sensors. In some cases, the sensor may be in a mobile device or a wearable sensor worn by the subject. In some cases, sensor features associated with muscle disability, particularly symptoms of SMA, are extracted from the received or obtained sensor data. In some cases, an assessment of the severity and progression of the symptoms of muscle disability, particularly SMA, of the subject is determined based on the extracted sensor data features.

[0035] Assessment of symptom severity and progression of muscle disability, particularly SMA, using diagnostics according to the present disclosure is well correlated with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing. Exemplary diagnostic methods according to the present disclosure can be used outside of a clinical setting and therefore have advantages in terms of cost, ease of monitoring a subject, and convenience for the subject. This facilitates frequent, particularly daily, subject monitoring and testing, thereby providing a better understanding of the disease stage and providing disease insights useful to both the clinical and research communities. Exemplary diagnostic methods according to the present disclosure can provide earlier detection of even minor changes in a subject's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment, which can indicate the presence or progression of a subject's muscle disability, particularly SMA, and can therefore be used for better disease management, including personalized therapy.

[0036] Figure 1 is a simplified diagram of an exemplary environment in which a diagnostic device 105 is used to assess respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of a subject 110. In some cases, device 105 may be a smartphone, smartwatch, or other mobile computing device. Device 105 includes a display screen 160. In some cases, display screen 160 may be a touch screen. Device 105 includes at least one processor 115 and a memory 125 storing computer instructions for a symptom monitoring application 130, which, when executed by the at least one processor 115, causes device 105 to assess respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of a subject. Device 105 receives a plurality of sensor data via one or more sensors associated with device 105. In some cases, the one or more sensors associated with the device are at least one of: a sensor disposed within the device or a sensor worn by the subject and configured to communicate with the device. In Figure 1 In FIG. 1 , the sensors associated with the device 105 include a first sensor 120 , such as a microphone located in the device 105 .

[0037] The device 105 extracts digital biomarker data from the received first sensor data, which digital biomarker data can be used to determine the subject's respiratory function, vital capacity, abdominal strength and / or chest strength or impairment.

[0038] The device 105 determines the respiratory function, vital capacity, abdominal strength and / or chest strength or impairment of the subject 110 based on the extracted features. In some cases, the device 105 sends the extracted features to the server 150 via the network 180. In some cases, the device 105 sends the 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 the symptom assessment application 170, which, when executed by the server processor 155, causes the processor 155 to determine the respiratory function, vital capacity, abdominal strength and / or chest strength or impairment of the subject 110 based on the extracted features received by the server 150 from the device 105. In some cases, the symptom assessment application 170 can cause the processor 115 to extract features from the sensor data received from the device 105. In some cases, the symptom assessment application 170 can determine the respiratory function, vital capacity, abdominal strength and / or chest strength or injury of the subject 110 based on the extracted sensor data features that can be received from the device 105 and the 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 clinical indicators and sensor-based indicators of respiratory function, vital capacity, abdominal strength and / or chest strength or injury. In some cases, the subject database 175 may be independent of the server 150. In some cases, the server 150 sends the determined respiratory function, vital capacity, abdominal strength and / or chest strength or injury of the subject 110 to the device 105. In some cases, the device 105 can output the respiratory function, vital capacity, abdominal strength and / or chest strength or injury of the subject 110. In some cases, the device 105 can convey information to the subject 110 based on the assessment. In some cases, an assessment of subject 110's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment may be communicated to a clinician, who may determine an individualized treatment for subject 110 based on the assessment.

[0039] In some cases, the computer instructions for the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to determine the respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of the subject 110 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 "ah" sound for as long as possible.

[0040] 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 sensor data, and an assessment of the respiratory function, vital capacity, abdominal strength and / or chest strength or injury of the subject 110 may be performed based on the digital biomarker data. The muscle disability of the subject 110, in particular the symptoms of SMA, may include symptoms that affect the respiratory function, vital capacity, abdominal strength and / or chest strength or injury of the subject 110.

[0041] Figure 2 An exemplary method is shown for using Figure 1 The exemplary apparatus 105 of the present invention assesses the respiratory function, vital capacity, abdominal strength and / or chest strength or impairment of the subject 110 based on active testing of the subject. Figure 2 It is a reference Figure 1 To describe, it should be noted that Figure 2 The method steps may be performed by other systems. The computer-implemented method includes prompting the subject to perform diagnostic tasks as outlined above in step 205. The method includes, in response to the subject performing one or more tasks, receiving a plurality of sensor data via, for example, a microphone (step 210).

[0042] Then, in step 215 , digital biomarker data is extracted from the sensor data, and the analytical model is applied to the digital biomarker data.

[0043] In step 220, data indicative of the subject's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment is output, for example, by generating instructions by processor 107 that, when executed by display component 160 of device 105, cause display component 160 to display output indicative of the subject's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment. Alternatively, the calculated data indicative of the subject's respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment may be transmitted to server 150, as outlined elsewhere in this application.

[0044] As described above, assessment of symptom severity and progression of muscle disability, particularly SMA, using diagnostics according to the present disclosure correlates well with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing.

[0045] Figure 3 A method is shown that can be used to implement one or more illustrative aspects described herein (such as Figure 1 and Figure 2 301 and 302. The present invention provides an example of a network architecture and data processing device of the aspects described in the above. Various network nodes 303, 305, 307 and 309 can be interconnected via a wide area network (WAN) 301 (such as the Internet). Other networks can also or alternatively be used, including private intranets, corporate networks, LANs, wireless networks, personal networks (PANs), etc. Network 301 is for illustrative purposes and can be replaced with fewer or other computer networks. The local area network (LAN) can have one or more of any known LAN topology and can use one or more of a variety of different protocols, such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) can be connected to one or more networks via twisted pair, coaxial cable, optical fiber, radio waves or other communication media.

[0046] The term "network" as used herein and depicted in the accompanying drawings refers not only to a system in which remote storage devices are coupled together via one or more communication paths, but also to independent devices that may occasionally be coupled to a system having storage functions. Thus, the term "network" includes not only a "physical network" but also a "content network", which consists of data (attributed to a single entity) residing in all physical networks.

[0047] 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 database and control software for performing one or more illustrative aspects described herein. The data server 303 may be connected to the web server 305, through which users interact and obtain data upon request. Alternatively, the data server 303 itself may act as a web server and be directly connected to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet), via a direct or indirect connection, or via some other network. Users may interact with the data server 303 using a remote computer 307, 309 (e.g., using a web browser) via one or more externally disclosed websites hosted by the web server 305 to connect to the data server 303. The client computers 307, 309 may be used together with the data server 303 to access data stored therein, or may be used for other purposes. For example, as is known in the art, a user may access the web server 305 from a client device 307 using an Internet browser, or by executing a software application that communicates with the web server 305 and / or data server 303 over a computer network (such as the Internet). In some cases, the client computer 307 may be a smartphone, smart watch, or other mobile computing device, and may implement a diagnostic device such as a Figure 1 In some cases, the data server 303 may implement a server such as Figure 1 The server 150 shown in FIG.

[0048] The server and application can be combined on the same physical computer and retain separate virtual or logical addresses, or they can reside on separate physical computers. Figure 1 Only one example of a network architecture that can be used is shown, and those skilled in the art will appreciate that the specific network architecture and data processing devices used can vary and assist in the functions they provide, as further described herein. For example, the services provided by the network server 305 and the data server 303 can be combined on a single server.

[0049] Each component 303, 305, 307, 309 can 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 a RAM 313, a ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.) and a 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 instructing the data server 303 to perform aspects described herein, and other application software 327 that provides assistance, support and / or other functions, which may or may not be used in conjunction with other aspects described herein. The control logic may also be referred to as data server software 325 in this article. The functionality of the data server software may refer to a combination of operations or decisions made automatically based on rules encoded into the control logic, operations made manually by users providing input to the system, and / or automatic processing based on user input (e.g., queries, data updates, etc.).

[0050] The memory 321 may also store data for performing one or more aspects described herein, including a first database 329 and a second database 331. In some cases, the first database may include the second database (e.g., as a separate table, report, etc.). That is, depending on the system design, information may be stored in a single database or divided into different logical, virtual or physical databases. The devices 305, 307, 309 may have an architecture similar to or different from that described with respect to the device 303. Those skilled in the art will appreciate that the functionality of the data processing device 303 (or devices 305, 307, 309) as described herein may be distributed across multiple data processing devices, for example, to distribute processing loads between multiple computers to separate processing performed based on geographic location, user access level, quality of service (QoS), etc.

[0051] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions executed by one or more computers or other devices described herein, such as in one or more program modules. Typically, program modules include routines, programs, targets, components, data structures, etc., which perform specific tasks or implement specific abstract data types when executed by a processor in a computer or other device. Modules may be written in a source code programming language and then compiled to execute the module, or modules may be written in a scripting language (such as, but not limited to, HTML or XML). Computer-executable instructions may be stored on a computer-readable medium, such as a hard disk, an optical disk, a removable storage medium, a solid-state memory, a RAM, etc. As will be appreciated by those skilled in the art, the functions of the program modules may be combined or distributed as needed in various embodiments. In addition, the functions may be embodied in firmware or equivalent hardware (such as an integrated circuit, a field programmable gate array (FPGA), etc.) in whole or in part. Specific data structures may be used to more effectively implement one or more aspects, and such data structures are included within the scope of computer-executable instructions and computer-usable data described herein.

[0052] The features disclosed in the foregoing description, or in the following claims, in terms of the manner of expressing or implementing the disclosed functions in their specific forms, or in terms of the methods or processes for obtaining the disclosed results, may be appropriately used alone in their various forms, or in any combination to implement the present invention.

[0053] Although the present invention has been described in conjunction with the above exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art when this disclosure is given. Therefore, the above exemplary embodiments of the present invention are considered to be illustrative rather than restrictive. Various changes may be made to the described embodiments without departing from the spirit and scope of the present invention.

[0054] For the avoidance of any doubt, any theoretical explanations provided herein are intended to improve the reader's understanding. The inventors do not wish to be bound by any of these theoretical explanations.

[0055] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0056] Throughout the specification, including the following claims, unless the context requires otherwise, the words "comprise" and "include" and variations such as "comprises and comprising" and "including", will be understood to imply the inclusion of stated integers or steps or groups of integers or steps but not the exclusion of any other integers or steps or groups of integers or steps.

[0057] It must 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" in relation to a numerical value is optional and means, for example, + / - 10%.

Claims

1. A diagnostic device configured to assess respiratory function, vital capacity, abdominal strength and / or chest strength or impairment of a subject, the diagnostic device comprising: processor; microphone; as well as a memory storing computer-readable instructions that, when executed by the processor, cause the diagnostic device to: Prompting the subject to perform a diagnostic task of emitting a long "ah" sound for a predetermined duration; receiving, via the microphone, audio data associated with the diagnostic task; extracting digital biomarker data from the audio data; as well as An analytical model is applied to the extracted digital biomarker data, the analytical model being configured to generate an output indicative of the respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of the subject.

2. The diagnostic device according to claim 1, wherein: The audio data comprises a plurality of segments; and Extracting the digital biomarker data includes 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. The diagnostic device according to 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. The diagnostic device according to claim 2 or claim 3, wherein: Each active speech segment includes a plurality of sub-segments; and Extracting the digital biomarker data includes applying a second algorithm to the active speech segment of the audio data, the second algorithm configured to classify the sub-segments into voiced speech sub-segments and unvoiced speech sub-segments.

5. The diagnostic device according to claim 4, wherein: Classifying the sub-segments of the active speech segment of the audio data into voiced speech segments and unvoiced speech segments includes generating timestamps indicating start and end times of each respective voiced speech sub-segment and unvoiced speech sub-segment.

6. The diagnostic device according to 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. The diagnostic device according to claim 5 or claim 6, wherein: The digital biomarker data includes a total number of voiced speech sub-segments in the active speech segment of the audio data.

8. The diagnostic device according to any one of claims 5 to 7, wherein: The digital biomarker data includes one or more of a duration of a longest voiced speech sub-segment and a shortest voiced speech sub-segment in the active speech segment of the audio data.

9. The diagnostic device according to any one of claims 5 to 8, wherein: The digital biomarker data includes a total duration of unvoiced speech subsegments within the predetermined duration of the diagnostic task.

10. The diagnostic device according to any one of claims 5 to 9, wherein: The digital biomarker data includes one or more of a duration of a longest unvoiced speech subsegment and a shortest unvoiced speech subsegment in the active speech segment of the audio data.

11. The diagnostic device according to any one of claims 1 to 10, wherein: The computer readable instructions, when executed by the processor, further cause the device to prompt the subject to place the device a predetermined distance from the subject.

12. The diagnostic device according to any one of claims 1 to 11, 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 location.

13. The diagnostic device according to any one of claims 1 to 10, wherein: The computer readable instructions, when executed by the processor, further cause the apparatus to: receiving noise data via the microphone; Calculating background noise based on the noise data; and A correction is applied to the audio data using the background noise.

14. The diagnostic device according to any one of claims 1 to 13, wherein: The audio data is received in 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. A diagnostic device according to any of the preceding claims, wherein the computer-readable instructions, when executed by at least one processor, cause the diagnostic device to apply a clinical interpretation model to the output indicative of the respiratory function, wherein the clinical interpretation model outputs an indication of the presence or absence of muscle disability.

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 to output an indication of the presence or absence of the muscle disability based on the comparison.

18. The diagnostic apparatus of claim 17, wherein the clinical interpretation model is configured to: determine whether the output indicative of the respiratory function is greater than a predetermined threshold; and, if it is determined that the output indicative of the respiratory function is greater than the predetermined threshold, output an indication of the presence of muscle disability; and, If it is determined that the output indicative of the respiratory function is less than or equal to the predetermined threshold, an indication of the absence of the muscle disability is output.

19. The diagnostic apparatus of claim 17, wherein the clinical interpretation model is configured to: determine whether the output indicative of the respiratory function is less than a predetermined threshold; and, if it is determined that the output indicative of the respiratory function is less than the predetermined threshold, output an indication of the presence of muscle disability; and, If it is determined that the output indicative of the respiratory function is greater than or equal to the predetermined threshold, an indication of the absence of the muscle disability is output.

20. A computer-implemented method configured to assess respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of a subject, the method comprising: Prompting the subject to perform a diagnostic task of emitting a long "ah" sound for a predetermined duration; receiving, via a microphone, audio data associated with the diagnostic task; extracting digital biomarker data from the audio data; as well as An analytical model is applied to the extracted digital biomarker data, the analytical model being configured to generate an output indicative of the respiratory function, vital capacity, abdominal strength, and / or chest strength or impairment of the subject.

21. The computer-implemented method of claim 20, wherein the computer-implemented method further comprises the steps of: A clinical interpretative model is applied to the output indicative of the respiratory function, wherein the clinical interpretative model outputs an indication of the presence or absence of muscle disability or an indication of progression of muscle disability.

22. A computer implemented method according to claim 20 or claim 21, wherein: The computer-implemented method is performed by the processor of the diagnostic device according to any one of claims 1 to 19.

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

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