Apparatus and method for determining respiratory infection from exhaled breath

Abstract

The diagnostic device and method for detecting a respiratory infection in a subject include a particle detection unit that acquires data regarding particles contained in the exhaled breath from the subject's airway, and a processing unit that determines a respiratory infection based on the data acquired from the particle detection unit and subject-related characteristics.

Description

[Technical field]

[0001] The present invention relates generally to the field of detecting respiratory infections from exhaled breath, and more particularly to detecting respiratory infections based on variations in mass and / or size of particles collected from exhaled breath. [Background technology]

[0002] Regenerating particles in exhaled breath is being investigated to reduce the need for invasive diagnostic procedures such as bronchoalveolar lavage (BAL) and biopsy.

[0003] In these studies, it was found that particles from exhaled breath can be used to continuously diagnose and monitor subjects on mechanical ventilation. This can prevent structural damage. This is described, for example, in WO 2013 / 117747 and in "Mechanically ventilated patients exhibit decreased particle flow in exhaled breath as compared to normal breathing patients" (Broberg, Ellen et al., ERJ, Open, Res, 2020; 6: 00198-2019). Further studies have been published to make subjects breathe using a machine instead of BAL. This research is described, for example, in "Increased particle flow rate from airways precedes clinical signs of ARDS in a porcine model of LPS-induced acute lung injury" (Stenlo, Martin et al., Am J Physiol Lung Cell Mol Physiol 318: L510-L517, 2020) and "Monitoring lung injury with particle flow rate in LPS-and COVID-19-induced ARDS" (Stenlo, Martin et al., Physiological Reports. 2021;9:e14802).

[0004] According to "Particle flow rate from the airways as fingerprint diagnostics in mechanical ventilation in the intensive care unit: a randomised controlled study" (Hallgren, Filip et al., ERJ Open Res 2021;7:00961-2020), different breathing modes produce unique particle patterns, which serve as clear signatures (fingerprints) for different breathing modes.

[0005] Particle-based functioning lungs are also being studied in the context of lung transplantation and lung cancer surgery. This work is described in the paper "Monitoring lung transplantation and lung cancer surgery using particles in exhaled air Preclinical and clinical implementation" (Ellen, Broberg et al., Doctoral Dissertation Series 2019:113; ISBN 978-91-7619-842-1).

[0006] Other research topics include asthma, as described in "Assessing small airways dysfunction in asthma, asthma remission and healthy controls using particles in exhaled air" (ERJ Open Res 2019;5:00202-2019).

[0007] Exhaled breath has not yet been used to detect respiratory infections.

[0008] There is a need for a rapid and reliable method to detect whether subjects in a hospital setting have a respiratory infection, especially a non-invasive method that would avoid the need to send samples to a laboratory.

[0009] An additional advantage is that non-invasive methods offer improved cost-effectiveness compared to the invasive methods used today. Summary of the Invention

[0010] Accordingly, embodiments of the present invention seek to preferably mitigate, alleviate or eliminate one or more of the deficiencies, drawbacks or problems in the technical field identified above, singly or in any combination, by providing devices, systems and methods for detection of a respiratory infection in a subject.

[0011] A first aspect of the present disclosure relates to a diagnostic device for detecting a respiratory infection in a subject. The device may include a particle detector configured to obtain data related to particles contained in exhaled air from the subject's airway. The device may include a processor configured to determine the respiratory infection based on the data obtained from the particle detector and subject-related characteristics.

[0012] In one example of the apparatus, the particle detector may be a particle counter or particle sizer, such as an optical particle counter or particle sizer.

[0013] In one example of the device, the data may be any of particle number, mass, size, mass distribution, and size distribution.

[0014] In one example of the device, the particles may be airborne particles collected from the subject's respiratory system.

[0015] In one example of the device, the data may be patterns relating to the respiratory infection.

[0016] In one example of the device, the subject-related characteristics may be information about the subject's respiratory system, which may include: a volume of exhaled air, a rate of exhalation, a flow rate of the exhaled air, a relative humidity in the exhaled air, a temperature of the exhaled air, and / or oxygen saturation.

[0017] In one example of the device, the information about the subject's respiratory system may be information about the subject's physical condition and / or health status, which may include weight, height, sex, age, medical records, smoker / non-smoker status, and / or heart rate characteristics.

[0018] In one example of the device, the respiratory infection may be determined by comparing the measured data, with the subject-related characteristics removed, to a reference database of healthy subjects.

[0019] In one example of the device, the respiratory infection may be determined by comparing the measured data, with the subject-related characteristics removed, to a reference database of subjects with respiratory infection.

[0020] In one example of the apparatus, the data may be filtered using the subject-related characteristics before the data is compared to the reference database.

[0021] In one example of the device, the processor may determine the respiratory infection qualitatively or quantitatively.

[0022] In one example of the apparatus, the determination may be based on a predefined number of particles, such as a predetermined number of particles, or a predetermined total mass of the particles counted.

[0023] In one example of the device, the data may be collected during a predetermined screening process.

[0024] In one example of the device, the predetermined screening process may include at least one of a predetermined number of exhalations, a predetermined inhalation and exhalation routine, and an inhalation followed by an exhalation of pure air.

[0025] In one example of the device, a threshold may be used to reduce the effect of the particles being too large and / or small.

[0026] Another aspect of the present disclosure relates to a method for detecting a respiratory infection in a subject, the method may comprise receiving particle data relating to particles contained in exhaled air from the subject's airways from a particle detector, and determining the respiratory infection based on the particle data obtained from the particle detector and subject-related characteristics.

[0027] The term "comprising" as used herein is understood to specify the presence of stated features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps, components or groups thereof. [Brief description of the drawings]

[0028] These and other aspects, as well as features and advantages, which may be realized by embodiments of the present invention will become apparent and elucidated from the following description, in which reference is made to the accompanying drawings, in which: [Figure 1] FIG. 1 is a schematic diagram illustrating an example of an apparatus for detecting respiratory infection. [Diagram 2] FIG. 2 is a schematic flow chart illustrating an example of a method for detecting a respiratory infection. [Figure 3A] 3A and 3B are examples of data measured from a population suffering from a respiratory infection and a healthy population. [Figure 3B] 3A and 3B are examples of data measured from a population suffering from a respiratory infection and a healthy population. [Figure 4A]4A and 4B are examples of data measured from a population suffering from a respiratory infection and a healthy population. [Figure 4B] 4A and 4B are examples of data measured from a population suffering from a respiratory infection and a healthy population. [Diagram 5] FIG. 5 shows an example of the difference in particle size between a subject with a respiratory infection and a healthy subject. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Hereinafter, specific examples of the present disclosure will be described with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be construed as being limited to the examples described herein. Rather, these examples are provided so that the present disclosure is detailed and complete, and the scope of the present disclosure is fully presented to those skilled in the art. The terms used in the detailed description of the embodiments illustrated in the accompanying drawings are not intended to limit the present disclosure. In the drawings, like reference numerals refer to like elements.

[0030] The following description is directed to examples of the present disclosure, which may be applied to devices, systems, and methods for detecting and / or diagnosing infections in a respiratory system, such as the airways and / or lungs. The subject may be a mammal, such as a human. The subject may be a patient. The detection and / or diagnosis is performed by quantifying particles in the subject's exhaled breath. For example, a respiratory infection may be detected and / or diagnosed by detecting deviations from a normal state in a respiratory system, such as the airways or lungs.

[0031] Respiratory infections can be upper respiratory tract infections, lower respiratory tract infections, cough, flu, Covid-19, pneumonia, influenza, tuberculosis, inflammation, endothelial dysfunction, sepsis, septic shock, etc., diseases caused by various types of viruses and / or bacteria that affect the respiratory system.

[0032] The particles herein may be non-volatile particles, such as airborne particles, which may be collected from the airways of the patient, and are believed to be generated from the surface of the airway mucus or airway lining fluid (RTLF) that covers the epithelial surface of the distal lungs.

[0033] Infection of a portion of a subject's respiratory system, such as the airways and / or lungs, can affect the composition and structure of surfactant and mucins within the respiratory system. These changes can alter droplet formation and droplet size during breathing.

[0034] These changes may affect the composition of particles in the exhaled breath, which may show changes in particle composition, such as size distribution, mass distribution and / or quantity, in the exhaled breath of infected subjects compared to particles in healthy subjects.

[0035] Other causes that may cause changes in surfactant and mucin composition and structure include physiological changes in the subject's condition.

[0036] In their research, the inventors have found that the distribution of particles collected from the respiratory system, in particular particles generated in the airways and lungs, can be used as a marker (like a fingerprint) to detect and / or diagnose infection.

[0037] 1 is a schematic diagram of a diagnostic device 1 for detecting a respiratory infection in a subject 11. The device comprises a particle detection unit 10 which the subject 11 can exhale.

[0038] The breath may be exhaled into a mouthpiece connected to a conduit, which may in turn be connected to a particle detection unit 10.

[0039] When the subject exhales, the particle detection unit 10 may instantly digitize the particles.

[0040] The particle detection unit 10 may determine the distribution of particles by classifying the particles according to, for example, their size or mass.

[0041] In particle distribution, the particle distribution profile may be a measure of how many particles of a particular mass or size (or range of masses or sizes) are present in exhaled breath.

[0042] The particle detection unit 10 may be a particle counter, such as, for example, a Grimm 1.108 optical particle counter (Grimm Aerosol Technik, Einring, Federal Republic of Germany), which is capable of counting and classifying particles in size intervals of 0.3 micrometers and up to 20 micrometers, although other optical particle counters such as Grimm 1.107 and 1.109 may also be used.

[0043] Time-of-flight devices may also be used as particle detector 10, as well as sizers from other manufacturers such as TSI.

[0044] Other options may include non-optical electrostatic conductance, condensation particle counters, quartz crystal microbalance (QCM), surface plasmon resonance (SPR), or surface acoustic wave (SAW).

[0045] The particle detector 10 may determine a number distribution of the measured particles, or a mass distribution calculated from the measured number distribution. For example, in the particle detector 10, a gas containing particles passes through a small, well-defined, strongly illuminated space, so that only one particle at a time is illuminated by the light. Illuminated particles produce pulses of scattered light, the intensity of which is measured. Since the intensity of the scattered light depends on the size of the particle, the particles in the air stream can be counted and classified by size.

[0046] The measurement principle of the particle detector 10 may also be time-of-flight. Here, the propagation time of a particle from one laser beam to another is measured. The time it takes for a particle to travel from one beam to another depends on the mass and / or size of the particle. Thus, the mass and / or size of the particle can be measured and characterized.

[0047] The device 1 may further comprise a processing unit 12. The processing unit is configured to determine a respiratory infection based on the data obtained from the particle detection unit 10. The determination may include the use of characteristics related to the patient to improve the determination.

[0048] The processing unit 10 or data processing device may be implemented with dedicated software (or firmware) running on one or more general-purpose or dedicated computing devices. In this context, each "element" or "means" of such computing devices refers to the conceptual equivalent of a method step, and it should be understood that there is not always a one-to-one correspondence between the elements or means and specific hardware or software routines. A single piece of hardware may include different means / elements. For example, a processing unit functions as one element / means when executing one instruction, and another element / means when executing another instruction. Also, an element / means may be implemented by one instruction, but may also be implemented by multiple instructions. Such software-controlled computing devices may comprise one or more processing units, for example a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), discrete analog and / or digital components, or any other programmable logic device, such as a Field Programmable Gate Array. The data processing unit 10 may further include a system memory and a system bus coupling various system components including the system memory to the processing unit. The system bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory may include computer storage media in the form of volatile and / or non-volatile memory such as Read-only Memory (ROM), Random Access Memory (RAM), and Flash memory. Specialized software may be stored in the system memory or in removable or built-in volatile or non-volatile computer storage media.The computer storage medium may be internal to the computer device or external to the computer device, and may be magnetic media, optical media, flash memory cards, digital tape, solid state RAM, solid state ROM, etc. The data processing unit 10 may include one or more communication interfaces, such as a serial interface, a parallel interface, a USB interface, a wireless interface, a network adapter, etc., and one or more data acquisition devices, such as an A / D converter.

[0049] The dedicated software may be provided to the control unit or data processing device by any suitable computer-readable medium, including a recording medium and a read-only memory.

[0050] There are various ways to determine infection based on the characteristics of the data acquired by the particle detection unit 10. Infection may be determined qualitatively or quantitatively, i.e., whether the patient is determined to have an infection or what type of infection the patient has. These determinations may be made by analyzing the data. The data received from the particle detection unit 10 may be any of the number, mass, size, mass distribution and / or size distribution of particles.

[0051] By analyzing the size and / or mass distribution, the number of particles within a certain range and / or the total mass of particles in the exhaled breath and the distribution of said particles, the number of particles within a certain range and / or the total mass of said particles may be different in the case of a patient compared to a healthy person, which may indicate a respiratory infection.

[0052] Infection may be determined by analyzing the characteristics of particles in the breath. For example, infection may change the mass and / or size distribution of particles. The distribution characteristics in infected patients tend to change towards larger or heavier particles compared to the distribution characteristics of healthy people. This means that infection can be qualitatively determined by analyzing the distribution of particles, the number of particles within a certain range, or the total mass of collected particles. This may be done by determining the total mass of a given number of particles in the breath, or the number of particles within a certain range. Thus, a higher total mass of particles compared to healthy subjects may be indicative of infection. Additionally and / or alternatively, a higher number of particles within a certain size range compared to healthy subjects may also be indicative of infection.

[0053] In some infections, particles may become smaller and / or lighter. By analyzing the size and / or mass distribution of the exhaled breath, if the distribution changes to have more particles smaller or lighter compared to the breath of a healthy person, it may be indicative that the subject has a respiratory infection. Similarly, by observing the total mass of particles in the exhaled breath, if the total mass is smaller compared to a healthy subject, it may be indicative of a respiratory infection. Additionally and / or alternatively, a smaller number of particles present within a particular size range compared to a healthy subject may also be indicative of an infection.

[0054] Furthermore, respiratory infections may alter the mass and / or size distribution of particles, both to lighter / smaller particles and larger / heavier particles, and thus, if the distribution is analyzed and differs from that of a healthy person, a respiratory infection may be indicative.

[0055] Additionally, the determination of infection may be based on a predefined number of particles, such as a predetermined number of particles. Alternatively and / or additionally, the determination may be based on a predetermined total mass of particles counted.

[0056] Different types of respiratory infections may have different effects on the properties of particles in the exhaled breath. For example, different types of respiratory infections may result in different size and / or mass distributions of particles in the exhaled breath. By analyzing the size and / or mass distribution characteristics of particles in the exhaled breath, the respiratory infection may be quantitatively determined. That is, not only the presence of a respiratory infection may be determined, but also the type of respiratory infection may be determined. The size and / or mass distribution characteristics may be a pattern related to the respiratory infection. This pattern may serve as a unique (like a fingerprint) signature of the respiratory infection.

[0057] A patient may be determined to be suffering from a respiratory infection by comparing the characteristics and / or patterns of the size and / or mass distribution of the patient's exhaled breath with a reference database of healthy subjects.

[0058] The characteristics and / or patterns of size and / or mass distribution of the patient's exhaled breath may be compared to a reference database of subjects suffering from respiratory infections to determine the type of respiratory infection the patient is suffering from.

[0059] It has been found that the size and / or mass distribution characteristics of particles in the exhaled breath, as well as the total weight or number of particles in a particular range, may vary between subjects due to their individual characteristics. Each of these characteristics may include information about the subject's airway. Information about the subject's airway may include the volume of exhaled breath, the number of exhaled breaths, the flow rate of the exhaled breath, the relative humidity of the exhaled breath, the temperature of the exhaled breath, and / or the oxygen saturation. Most of these may be data that can be measured and used in determining the size and / or mass distribution of particles in the exhaled breath, as well as the total weight and / or number of said particles in a particular range. Therefore, the device may further comprise a device for measuring the individual characteristics of the subject.

[0060] By taking such characteristics into account, the mass and / or size distributions can be used to remove differences between subjects, which can improve the determination of respiratory infections, particularly when size and / or mass distributions are used, which can improve the accuracy of determining the type of respiratory infection a subject is suspected to have.

[0061] One way to remove differences due to subject characteristics is to normalize the data based on the subject characteristics. Another way is to filter the data based on the subject characteristics. For example, the breath data is compared to breath data from subjects with similar characteristics.

[0062] As part of the method for determining whether a subject has a respiratory infection, characteristics of each subject may first be determined.

[0063] Additionally and / or alternatively, the information regarding the subject's respiratory system may be information regarding the subject's physical condition and / or health status. This information may include weight, height, sex, age, medical records, smoker / non-smoker, and / or heart rate characteristics. The information that may affect the size and / or mass distribution may be information unrelated to respiratory infection.

[0064] As mentioned above, this information can be used to improve the detection of infection by normalizing the data or filtering the data to remove variations in size and / or mass distribution that are not related to variations caused by respiratory infection.

[0065] To further improve detection of respiratory infections, the collection of data from exhaled breath may be performed during a routine screening process that helps standardize the data and may reduce the variability of the collected distribution that is not attributable to respiratory infections, which may improve the comparison of the collected data with data in a reference database.

[0066] The device may be provided with means, such as a screen, to prompt the user on how to carry out a given screening process, either by displaying text or a diagram visualising the steps carried out during a given screening process.

[0067] The devices may perform different predefined screening processes from which to choose.

[0068] The predetermined screening process may include at least one of a predetermined number of exhalations, a predetermined inhalation and exhalation routine, and an inhalation of pure air followed by an exhalation.

[0069] The device for detecting respiratory infections may also include means for carrying out a screening process. For example, the device may include pure air introduced into the same mouthpiece through which exhaled air passes.

[0070] Data may also be collected by having the subject exhale for a predetermined period of time and / or at a predetermined number of times.

[0071] Additionally and / or alternatively, the collected data may be standardized by using a particle threshold. For example, a threshold may be used to reduce the effect of particles having a size and / or mass that is not within the expected range used to detect respiratory infection. A threshold may be used to remove data for particles that are deemed to be excessively large and / or small. For example, a threshold may be used to collect only particles of a particular size range that are known to show large variations when comparing healthy subjects to infected subjects. The total mass of a range of particles in a subject may be compared to the total mass of a known healthy subject from the same group to determine whether the subject is infected. Additionally and / or alternatively, the number of particles within a particular range in a subject may be compared to the number of particles within the same particular range in a known healthy subject from the same group to determine whether the subject is infected.

[0072] Additionally, for example, a threshold may be useful when using the total mass of particles in exhaled breath to detect respiratory infections: if a given number of particles are collected relative to the total mass, excessively large or small particles (outliers) may affect the data, which may provide a positive negative or negative positive.

[0073] The device may be used to monitor, for example continuously, the progression of a respiratory infection by analyzing the variation of particles in the exhaled breath.The device may be used to determine whether a subject's condition is worsening or whether the subject's health is improving.The device may be used to ascertain the effect of medication on a subject.

[0074] FIG. 2 is a schematic flow chart 2 showing an example of a method for detecting a respiratory infection in a subject. The method may be computer-implemented. The computer-implemented method may be implemented as computer software, the computer software having code for executing on a computer or processor to implement the method steps. The software may be part of the computer of the device described above, or may be running on an external device, such as in the cloud. The detection device may communicate with the external device via known protocols.

[0075] The method 2 may comprise the step of receiving 100 particle data from a particle detector. Alternatively, the method 2 may comprise the step of acquiring particle data using a particle detector. The particle data relates to particles contained in exhaled breath from the subject's airway.

[0076] The method may then comprise a step 110 of inputting the subject related characteristics. The subject related characteristics may be input using an input device connected to the processing unit of the detection device. The input device may be a keyboard or a touch screen. Additionally and / or alternatively, the subject related characteristics may be input by measuring using a measuring means / device connected to a meter and a device that includes a spirometer or the like for measuring the rate and / or volume of exhaled breath. Some of these characteristics may be measured simultaneously with the collection of data on particles in the exhaled breath.

[0077] Method 2 may further comprise determining 120 a respiratory infection based on particle data received from or acquired by the particle detection unit and subject-related characteristics.

[0078] FIG. 3A shows Data 3, which measures particles in subjects with pneumonia. Data 3 shows the relative distribution of particle counts in the exhaled breath of nine pneumonia patients. FIG. 3B shows Data 4, which measures particles in healthy subjects. Data 4 shows the relative distribution of particle counts in the exhaled breath of six healthy subjects.

[0079] From these data, it is clear that there is a change in distribution between healthy and infected subjects, which can be seen as a unique feature of pneumonia and can be used to determine whether a subject has pneumonia using any of the methods described above.

[0080] Figures 4A and 4B show data from measurements of particles taken from a subject 6 with Covid-19 and a healthy subject 5. The data show the size distribution of the number (median) of particles measured in 10 subjects diagnosed with Covid-19 and 100 healthy subjects. Each number on the x-axis represents a bin. Each bin is assigned a range of particles. Again, it is clear from these data that there is a change in distribution between healthy and infected subjects. The distribution in infected subjects can be seen as a unique feature of Covid-19 and may be used to determine whether a subject has Covid-19 using any of the methods described above.

[0081] Figure 5 shows the ratio of exhaled particles in the range <.41-0.55 mua in a subject with Covid-19 20 and a healthy subject 21. Again, a significant change is detected between healthy and infected subjects. This difference may be used to determine whether a subject is infected or not.

[0082] Embodiments of the present invention are described herein with reference to flowcharts and / or block diagrams. It will be understood that some or all of the illustrated block diagrams may be implemented by computer program instructions. These computer program instructions are provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to implement the apparatus. The instructions, executed via the processor of the computer or other programmable data processing apparatus, implement means for implementing the functions / acts specified in the flowchart and / or block diagram blocks.

[0083] It should be understood that the functions / acts illustrated in the figures may not be performed in the order shown in the operational diagrams. For example, two blocks shown in succession may in fact be executed substantially simultaneously. Or, the two blocks may be executed in the reverse order depending on the functions / acts involved. Some figures include arrows on communication paths to indicate the primary direction of communication, but it should be understood that communication may occur in the opposite direction to that of the illustrated arrows.

[0084] The invention described above has been described with reference to specific embodiments. However, other embodiments than those described above may be realized as well within the scope of the invention. Other method steps than those described above, which implement the method by hardware or software, may be provided within the scope of the invention. The various features and steps of the invention may be combined in other combinations than those described. The scope of the invention is limited only by the appended claims.

Claims

1. 1. A diagnostic device for detecting a respiratory infection in a subject, comprising: a particle detection unit that acquires data regarding particles contained in the exhaled air from the subject's airway; a processing unit configured to determine the respiratory infection based on the data acquired from the particle detection unit and subject-related characteristics.

2. 2. The diagnostic device of claim 1, wherein the particle detector is a particle counter or particle sizer, such as an optical particle counter or particle sizer.

3. 3. The diagnostic device according to claim 1, wherein the data is any one of the number, mass, size, mass distribution, and size distribution of particles.

4. The diagnostic device according to claim 1 , wherein the particles are airborne particles collected from the respiratory system of the subject.

5. 2. The diagnostic device of claim 1, wherein the data is a pattern related to the respiratory infection.

6. 2. The diagnostic device of claim 1, wherein the subject-related characteristics are information about the subject's respiratory system, including exhaled air volume, exhalation frequency, flow rate of the exhaled air, relative humidity of the exhaled air, temperature of the exhaled air, and / or oxygen saturation.

7. 7. The diagnostic device of claim 6, wherein the information about the subject's respiratory system is information about the subject's physical condition and / or health status, including weight, height, sex, age, medical records, smoker / non-smoker, or heart rate characteristics.

8. 10. The diagnostic device of claim 1, wherein the respiratory infection is determined by comparison with a reference database of healthy subjects.

9. 10. The diagnostic device of claim 1, wherein the respiratory infection is determined by comparing with a reference database of subjects with respiratory infections.

10. 10. A diagnostic device according to claim 8 or 9, characterized in that the data is filtered using the subject-related characteristics before being compared with the reference database.

11. The diagnostic device according to claim 1 , wherein the processing unit determines the respiratory infection qualitatively or quantitatively.

12. 10. The diagnostic device of claim 1, wherein the determination is based on a predefined number of particles, such as a predetermined number of particles, or a predetermined total mass of the counted particles.

13. 10. The diagnostic device of claim 1, wherein the data is collected during a predetermined screening process.

14. 14. The diagnostic device of claim 13, wherein the predetermined screening process includes at least one of a predetermined number of exhalations, a predetermined inhalation and exhalation routine, and an inhalation followed by an exhalation of pure air.

15. 2. The diagnostic device of claim 1, wherein a threshold is used to reduce the effect of excessively large and / or small particles.

16. 1. A computer-implemented method for detecting a respiratory infection in a subject, comprising: receiving particle data relating to particles contained in exhaled air from the subject's airway from a particle detection unit; determining the respiratory infection based on the particle data acquired from the particle detection unit and subject-related characteristics.

17. A computer program comprising instructions that cause a computer to carry out the method of claim 16 when said program is executed by a computer.

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