Description:SENSING DEVICE FOR EARLY DETECTION OF LUNG CANCER

FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of a sensor technology, in particular relates to a sensing device for early detecting of lung cancer and method for operating sensing device for early detection of lung cancer.

BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Lung cancer detection typically involves a combination of imaging tests, such as chest X-rays and CT scans, along with laboratory tests to identify cancerous cells in sputum or through biopsy. Advanced technology may enable the use of artificial intelligence (AI) and machine learning algorithms to analyse imaging data, which may enhance early detection by identifying suspicious nodules that may not be easily noticeable. Early and accurate detection is crucial for improving treatment outcomes, as lung cancer often presents symptoms only in advanced stages.
[0004] Existing lung cancer detection methods may rely on bodily fluids, such as blood tests, may be invasive, time-consuming, and often less sensitive. Further, the existing methods typically involve collection of blood or other fluids to analyse cancer-related biomarkers. However, the existing methods may fail to detect early-stage cancers or provide false negatives, reducing the chances of timely diagnosis. Further, the invasive nature of these tests may also discourage regular screening, which is crucial for early detection.
[0005] According to a patent application “US20100075367A1” titled “Lung cancer detection by optical analysis of body fluids” discloses Lung cancer detection by optical analysis of body fluids. The method for lung cancer detection by optical analysis of body fluids relates to analysing samples of blood, urine and sputum by fluorescence spectroscopy in order to detect the presence of naturally occurring molecules in the fluids that serve as biomarkers indicative of cancer in the human body. The analysis can be carried out based on fluorescence emission spectra, fluorescence excitation spectra and synchronous (emission and excitation) spectra of bio-samples. The early detection and diagnosis of lung cancer may be made by comparison of ratios of fluorescence emissions and/or excitation intensities of tryptophan, tyrosine, elastin, collagen, bile pigments, NADPH, flavins and various species of porphyrins.
[0006] According to another patent application “US20170191118A1” titled “Non-invasive gene mutation detection in lung cancer patients” discloses Non-invasive gene mutation detection in lung cancer patients. A system and method for the detection of saliva biomarkers in bodily fluids is described. In particular, the system is suitable for detecting biomarkers of lung cancer in a subject. The system includes an electrochemical sensor chip having at least one well, wherein the at least one well contains a working electrode coated with a conducting polymer functionalized with at least one capture probe, and at least one labelled detector probe. When the at least one labelled detector probe is mixed with a sample of the subject containing a biomarker of lung cancer and added to the at least one well, an electric current is applied to the sample, such that when at least some of the biomarker binds to the capture probe, a measurable change in electric current in the sample is created that is indicative of lung cancer.
[0007] However, the traditional methods such as gas chromatography-mass spectrometry (GC-MS), Proton Transfer Reaction-Mass Spectrometry (PTR-MS), and etc. are expensive, time-consuming and need highly skilled technicians to operate the bulky instruments may lead to challenges associated with traditional diagnostic methods
OBJECTIVES OF THE INVENTION
[0008] The objective of invention is to provide a sensing device for early detection of lung cancer.
[0009] The objective of invention is to provide a method for operating the sensing device for early detection of lung cancer.
[0010] Furthermore, the objective of present invention is to provide the sensing device for early detection of lung cancer that is capable of utilizing carbon porous Nano material into electrochemical sensors for detecting isoprene biomarkers.
[0011] Furthermore, the objective of present invention is to provide the sensing device for early detection of lung cancer that is capable of utilizing an electrochemical transduction mechanism to measure changes in electrical properties on exposure of isoprene to provide real-time analysis and diagnosis of cancer.
[0012] Furthermore, the objective of present invention is to provide the sensing device for early detection of lung cancer that is portable in nature and eliminates the need for invasive tissue biopsy or blood test to provide comfortable screening and diagnostic services.
SUMMARY
[0013] According to an aspect, the present embodiments the sensing device for early detection of lung cancer, the sensing device comprises a circuit board fabricated with at least two electrodes, Further, the at least two electrodes is coated with carbon nanomaterials. Further, a hollow tube having at least one inlet is fabricated in a proximity of the at least two electrodes. Further, the hollow tube facilitates a patient to blow air, via the at least one inlet. Further, at least one processor is operationally coupled with the at least two electrodes. Further, the at least one processor is configured to receive one or more signals generated by the at least two electrodes in response to a change in electrical conductivity/or impedance. Further, the change in electrical conductivity/ or impedance correspond to adsorption of isoprene onto a surface of the carbon nanomaterials. Further, analyse the one or more signals by using artificial intelligence (AI)/machine learning (ML) algorithms to obtain one or more results. Further, a computing device is communicatively coupled with the at least one processor. Further, the computing device is configured to display the one or more results in a real time.
[0014] In one embodiment, a method for operating the sensing device for early detection of lung cancer comprises fabricating at least two electrodes over a circuit board. Further, the method comprises receiving air within a hollow tube having at least one inlet. Further, the method comprises receiving, via at least one processor, one or more signals generated by the at least two electrodes in response to a change in electrical conductivity/or impedance. Further, the method comprises analysing, via the at least one processor, the one or more signals by using Artificial intelligence (AI)/Machine learning (ML) algorithms to obtain one or more results. Further, the method comprises displaying, via computing device, the one or more results in a real time.

BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g. boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. It may be that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Furthermore, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions are described with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles.
[0016] FIG. 1 illustrates a block diagram of a system for a sensing device for early detection of lung cancer, according to an embodiment of the present invention;
[0017] FIG. 2 illustrates a schematic diagram of a sensing device for early detection of lung cancer, according to an embodiment of the present invention;
[0018] FIG. 3 illustrates a graphical representation of change in impedance with change in the concentration of isoprene, according to an embodiment of the present invention;
[0019] FIG. 4 illustrates a graphical representation of a selectivity of isoprene among other analytes present in the breath sample, according to an embodiment of the present invention;
[0020] FIG. 5A illustrates a graphical representation of a comparative analysis between real and simulated data for varying concentration of isoprene (A) 10ppb, according to an embodiment of present invention;
[0021] FIG. 5B illustrates a graphical representation of a comparative analysis between real and simulated data for varying concentration of isoprene (B) 62 ppb, according to an embodiment of present invention;
[0022] FIG. 5C illustrates a graphical representation of a comparative analysis between real and simulated data for varying concentration of isoprene (C) 90 ppb, according to an embodiment of present invention;
[0023] FIG. 5D illustrates a graphical representation of a comparative analysis between real and simulated data for varying concentration of isoprene (D) 134 ppb, according to an embodiment of present invention;
[0024] FIG. 6 illustrates a Randle’s circuit diagram of the at least two electrodes fabricated with the circuit board, according to an embodiment of the present invention;
[0025] FIG. 7 illustrates a tabular representation of a values of circuit parameters at various concentration of isoprene, according to an embodiment of the present invention; and
[0026] FIG. 8 illustrates a method for operating the sensing device for early detection of lung cancer, according to an embodiment of present invention.

DETAILED DESCRIPTION
[0027] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0028] Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described. Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.
[0029] The present invention discloses about a sensing device for early detection of lung cancer that is capable of utilizing carbon porous nanomaterial into electrochemical sensors for detecting isoprene biomarkers for earlier diagnosis of lung cancer in an inexpensive and less-time consuming manner.
[0030] FIG. 1 illustrates a block diagram (100) of a system for a sensing device (100) for early detection of lung cancer, according to an embodiment of the present invention. FIG. 2 illustrates a schematic diagram (200) of a sensing device (100) for early detection of lung cancer, according to an embodiment of the present invention. FIG. 2 is described in conjunction with FIG. 1.
[0031] In some embodiments, the sensing device (100) comprises a circuit board (102). Further, the circuit board (102) is fabricated with at least two electrodes (104). Further, the at least two electrodes (104) are coated with carbon nanomaterials. Further, a hollow tube having at least one inlet is fabricated in a proximity of the at least two electrodes (104). Further, the sensing device (100) comprises at least one processor (106), memory (108), AI/ML model (110), input/output circuitry (112), communication circuitry (114) and computing device (116).
[0032] In some embodiments, the circuit board (102) is a flat board crafted with an insulating material. Further, the circuit board (102) may correspond to printed circuit board (PCB). Further, the circuit board (102) may electrically connect electronic components using conductive pathways, tracks, or signal traces etched from copper sheets laminated onto the circuit board (102). Further, the components like resistors, capacitors, and integrated circuits are soldered onto the circuit board (102) to form a functioning electronic circuit.
[0033] Further, the circuit board (102) may be fabricated with at least two electrodes (104) coated with carbon nanomaterials. Further, the carbon nanoparticles may correspond to carbon porous nanomaterial. Further, the coated carbon nanomaterials may correspond to a sensing element. Further, the carbon porous nanomaterials may enhance selectivity and sensitivity by providing a high surface area and customizable pore structure to allow selective adsorption and interaction with specific molecules or ions. Further, the porous nature of the carbon porous nanomaterials may provide numerous active sites for chemical reactions.
[0034] Further, a hollow tube having at least one inlet is fabricated in a proximity of the at least two electrodes (104). Further, the hollow tube may facilitate a patient to blow air, via the at least one inlet. Further, the air may correspond to a breath sample of the patient or a healthy individual. Further, the breath sample may be directed into the sensing element through the at least one inlet to ensure that the breath sample may come in contact with the sensing element. The at least one inlet may be connected to a tube or mouthpiece that collects the exhaled breath sample.
[0035] Further, the carbon nanoparticles having the porous structure may provide the high surface area for interaction. Further, isoprene molecules may interact with the carbon nanomaterials-coated at least two electrodes (104). Further, the isoprene molecules present within the breath sample may be adsorbed by the carbon nanomaterials due to high affinity of the isoprene molecules.
[0036] Further, the one or more electric signals are generated in response to binding and adsorption of the isoprene molecules. Further, the one or more electric signals may correspond to change in electrical conductivity/or impedance. Further, the isoprene molecules may adhere to the surface of the carbon nanomaterials through physical adsorption (i.e. van der Waals forces) or chemical adsorption (i.e. covalent or ionic bonding). Further, the nature of interaction depends on the properties of both the isoprene and the carbon nanomaterials that comprises surface functionalization and porosity.
[0037] Further, the adsorption of isoprene introduces additional charge carriers or modifies the distribution of existing charge carriers within the carbon nanomaterials. Further, the interaction may change the local electronic density to create new energy states or altering the band structure of the carbon nanomaterial. For example, if the carbon nanomaterial is a semiconductor, the adsorption of the isoprene molecule may lead to changes in conductivity by either donating or withdrawing electrons from the conduction or valence bands.
[0038] Further, the alternations in the electronic structure may result in measurable changes in the carbon nanomaterials electrical properties. Further, the sensing element may detect the changes as a variation in an electrical signal. Further, the variation may be directly proportional to the concentration of isoprene molecules in the breath sample. Further, the magnitude of the change is proportional to the number of isoprene molecules adsorbed, to provide a quantitative measure of isoprene presence in the breath sample.
[0039] Further, the at least one processor (106) may be operationally coupled with the at least two electrodes (104). In one embodiment, the at least one processor (106) may be communicatively coupled to the memory (108). The at least one processor (106) may include suitable logic, input/ output circuitry (112), and communication circuitry (114) that are operable to execute one or more instructions stored in the memory (108) to perform predetermined operations. In one embodiment, the at least one processor (106) may be configured to decode and execute any instructions received from one or more other electronic devices or server(s). The at least one processor (106) may be configured to execute one or more computer-readable program instructions, such as program instructions to carry out any of the functions described in this description. Further, the at least one processor (106) may be implemented using one or more processor technologies known in the art. Examples of the at least one processor (106) include, but are not limited to, one or more general purpose processors and/or one or more special purpose processors.
[0040] In one embodiment, the memory (108) may be configured to store a set of instructions and data executed by the at least one processor (106). Further, the memory (108) may include the one or more instructions that are executable by the at least one processor (106) perform specific operations.
[0041] Further, the at least one processor (106) may be configured to receive the one or more signals generated by the at least two electrodes (104) in response to the change in electrical conductivity or impedance. Further, when the isoprene molecules are absorbed onto the carbon nanomaterial coated to the at least two electrodes (104). The at least two electrodes (104) may alter the electrical conductivity which may be transmitted to the at least one processor (106). Further, the at least one processor (106) may be configured to receive the one or more signals and compares the changes in conductivity or impedance against a predefined baseline or reference value.
[0042] Further, the at least one processor (106) may be configured to analyse the one or more signals by using Artificial intelligence (AI)/Machine learning (ML) algorithms (i.e. AI/ML model (110)) to obtain one or more results. In some embodiments, the AI/ML algorithms are computational methods used in artificial intelligence (AI) and machine learning (ML) to enable system to learn from data, identify patterns, and make decisions or predictions without being explicitly programmed for each task. Further, the AI refers to the broader concept of machines being able to perform tasks that typically require human intelligence, while ML is a subset of AI that focuses on developing algorithms that may learn from and make decisions based on data. Further, the AI/M algorithms may range from simple linear regression models to more complex neural networks and deep learning model.
[0043] Further, the AI/ML model (110) is trained to recognize specific patterns or signatures in a data that are associated with lung cancer biomarkers. Further, the at least one processor (106) may be configured to analyse the patterns, and may differentiate between normal and cancerous breath samples by using AI/ML algorithms to provide the one or more results. Further, the AI/ML algorithms may identify the specific patterns in the one or more signals that correlate with different concentrations of isoprene. Further, the one or more results may correspond to the concentration of isoprene in the breath sample. Further, the one or more result is a quantifiable value that may represent the presence of isoprene in the breath sample.
[0044] In some embodiments, the computing device (116) is communicatively coupled with the at least one processor (106). Further, the computing device (116) may be configured to display the one or more results in a real time. Further, the computing device (116) may comprise at least one of a mobile phone, tablet, etc. Further, the computing device (116) may be configured to enable the user to perform one or more operations. Further, the one or more operations may include but not limited to communicating with other external systems or devices, performing computation oriented tasks, providing audio-visual outputs. In one example, the computing device (116) may comprise a touch enabled display panel, one or more speakers, one or more microphones, antenna(s), etc. Further, the computing unit is configured to display the information clearly and concisely, often in numerical form, showing the exact concentration of isoprene detected. Further, LCD display of the computing unit may comprise additional information, such as reference ranges, alerts if the concentration is above a certain threshold, or graphical representations of the data.
[0045] In an example embodiment, clinicians may use the real-time data to make informed decisions about the patient’s health. Further, the displayed one or more results comprises the isoprene concentration may help clinicians assess whether further diagnostic tests are necessary or if immediate intervention is required. Further, the LCD display of the computing device (116) may allow the prompt action, which is critical in conditions like lung cancer. Further, the early detection may significantly improve outcomes. Further, the one or more results are stored and documented in a database for further analysis, comparison with previous tests, or sharing with other healthcare professionals. Further, the change in electrical conductivity/ or impedance in response to change in the concentration range may be displayed on the LCD display of the computing device (116). Further the concentration range may correspond to 10 ppb to 134 ppb.
[0046] FIG. 3 illustrates a graphical representation (300) of change in impedance with change in the concentration of isoprene, according to an embodiment of the present invention.
[0047] As illustrated in FIG. 3, the graphical representation displays the relations between impedance (|Z|) in kilo-ohms (kO) and concentration in parts per billion (ppb). Further, an X-axis represent the concentration of isoprene in part per million (ppb). Further, the concentration ranges from 0 to 140 ppb. Further, Y-axis shows the impedance in Kilo-ohms (kO). Further, the range of the impedance may correspond to 650 kO to 950 kO. Further, the blue data points may represent measured impedance values at various concentrations. Further, a trend line is linear having strong negative slope. Further, the impedance decreases with increase in concentration of isoprene.
[0048] Further, the R² value is a statistical measure of the trend line that fits the data points. Further, the R² value of 0.995 indicates an excellent fit. Further, the linear model (110) explains that 99.5% of the variance in impedance due to changes in concentration of the isoprene.
[0049] In an example embodiment, the at least two electrodes (104) may show linear decrease in impedance with increased isoprene concentration. The sensing element may generate 10 second response time. Further, the sensitivity of the sensing element is 2.4 O/ppb/mm2. Further, a limit of detection (LOD) is the lowest signal and the lowest corresponding quantity to be determined from the one or more signal. Further, the limit of detection may correspond to 10 ppb. Further, the LOD of 10 ppb may suggest that sensing elements may detect extremely low amount of isoprene in the breath sample in an efficient manner.
[0050] FIG. 4 illustrates a graphical representation (400) of a selectivity of isoprene among other analytes present in the breath sample, according to an embodiment of the present invention.
[0051] In some embodiments, the sensing element may detect the isoprene molecules in a lower concentration. Further, the X- axis represent different chemical analytes that are need to be tested. Further, the chemical analytes may correspond to nitrogen, ethanol, water vapour, toluene, acetone and isoprene. Further, the Y-axis may represent the impedance values in kilo-ohms (kO), ranging from about 470 kO to 950 kO. Further, the graph indicates that the sensing element may show different impedance values for different analytes. Further, the isoprene may show highest impedance as the sensing element is selective for the isoprene as compared to the other analytes. Further, the higher impedance for isoprene as compared to other analytes like nitrogen and ethanol may indicate that the sensing element is particularly sensitive to isoprene. In an example embodiment, the graphical representation may indicate higher electrode impedance upon exposure of isoprene, indicating good selectivity to isoprene to enhance the reliability for lung cancer diagnosis.
[0052] FIG. 5A illustrates a graphical representation (500) of a comparative analysis between real and simulated data for varying concentration of isoprene (A) 10ppb, according to an embodiment of present invention. FIG. 5B illustrates a graphical representation (502) of a comparative analysis between real and simulated data for varying concentration of isoprene (B) 62 ppb, according to an embodiment of present invention. FIG. 5C illustrates a graphical representation (504) of a comparative analysis between real and simulated data for varying concentration of isoprene (C) 90 ppb, according to an embodiment of present invention. FIG. 5D illustrates a graphical representation (506) of a comparative analysis between real and simulated data for varying concentration of isoprene (D) 134 ppb, according to an embodiment of present invention. FIGS. 5A-5D are described in conjunction with FIGS. 1-4.
[0053] In some embodiments, the X- axis represents the real part of the impedance, measured in kilo-ohms (kO) that may ranges approximately from 0 to 120 kO across all subplots. Further, Y-Axis may show the imaginary part of the impedance measured in kilo-ohms (kO) that may ranges from 0 to 1000 kO. Further, the blue points may correspond to real data. Further, the orange points may correspond to simulated data. Further, a Nyquist plots of sensing element are exposed to different concentrations of isoprene using z view software. Further, the simulations provide insights into the modelled behaviour within the defined concentration range.
[0054] In some embodiments, the graphical representation (500) of FIG. 5A may provide a strong correlation between the real and simulate data, with the points lying close to a linear trend. Further, the real and simulated data may overlap well to predict the sensing element response at low concentrations of isoprene.
[0055] In some embodiments, the graphical representation (502) of FIG. 5B may indicate that the real and simulated data closely follow each other along a linear trend. Further, some slight deviations may be observed. Further, the minor discrepancies may occur as the concentration increases.
[0056] In some embodiments, the graphical representation (504) of FIG. 5C may indicate that the trend remains linear, with real and simulated data points aligning closely, though the deviations between real and simulated data. Further, the AI/ML model (110) continues to perform well but shows slight limitations as the concentration increases further.
[0057] In some embodiments, the graphical representation (506) of FIG. 5D may indicate that at the at the highest concentration of 134 ppb, the linear correlation remains, but there are more pronounced deviations between real and simulated data. Further, concentrations, the simulated data closely matches the real data, indicating that the model (110) used to simulate the sensing elements response is generally accurate at all concentrations. Further, the strongest correlation is observed at lower concentrations (10 ppb), with slight deviations becoming more noticeable at higher concentrations (134 ppb).
[0058] FIG. 6 illustrates a Randle’s circuit diagram (600) of the at least two electrodes (104) fabricated with the circuit board (102), according to an embodiment of the present invention. Further, the Randle’s circuit may comprise solution resistance (Rs), double layer capacitance (Cdl) and charge transfer resistance (Rct). Further, the double layer capacitance (Cdl) remains almost constant regardless of isoprene exposure which may indicate interfacial properties related to capacitance are stable. However, both the series resistance (Rs ) and polarization resistance (Rct) decreases with increase in isoprene concentration. Further, the reduction in resistance may show that the isoprene enhances the conductivity of the at least two electrodes (104) to facilitate charge transfer at the electrode interface. Further, the unchanged charge transfer resistance and double layer capacitance may support the hypothesis that the primary effect of isoprene is on the resistive components rather than the capacitive properties.
[0059] FIG. 7 illustrates a tabular representation (700) of a values of circuit parameters at various concentration of isoprene, according to an embodiment of the present invention.
[0060] In some embodiments, the table of FIG. 7 may represent the effect of increases isoprene concentration (from 10 ppb to 134 ppb) on the solution resistance (Rs), double layer capacitance (Cdl) and charge transfer resistance (Rct) in a sensing device (100). As the concentration of isoprene increases, Rs decreases significantly from 25,000 O at 10 ppb to 6,250 O at 134 ppb, indicating reduced resistance in the solution due to enhanced conductivity. Further, Rct also decreases from 12,000,000 O (1.2E7) to 6,190,000 O (6.19E6), indicating more efficient charge transfer at higher isoprene concentrations. Further, Cdl remains relatively stable, with only minor fluctuations, indicating that the capacitance at the electrode-electrolyte interface is unaffected by isoprene concentration.
[0061] FIG. 8 illustrates a method (800) for operating the sensing device (100) for early detection of lung cancer, according to an embodiment of present invention.
[0062] At operation 802, fabricating at least two electrodes (104) over a circuit board (102). Further, the circuit board (102) may electrically connect electronic components using conductive pathways, tracks, or signal traces etched from copper sheets laminated onto the circuit board (102). Further, the components like resistors, capacitors, and integrated circuits are soldered onto the circuit board (102) to form a functioning electronic circuit.
[0063] Further, the circuit board (102) may be fabricated with at least two electrodes (104) coated with carbon nanomaterials. Further, the carbon nanoparticles may correspond to carbon porous nanomaterial. Further, the coated carbon nanomaterials may correspond to a sensing element. Further, the carbon porous nanomaterials may enhance selectivity and sensitivity by providing a high surface area and customizable pore structure to allow selective adsorption and interaction with specific molecules or ions.
[0064] At operation 804, receiving air within a hollow tube having at least one inlet. Further, the hollow tube may facilitate a patient to blow air, via the at least one inlet. Further, the air may correspond to a breath sample of the patient or a healthy individual. Further, the breath sample may be directed into the sensing element through the at least one inlet to ensure that the breath sample may come in contact with the sensing element. The at least one inlet may be connected to a tube or mouthpiece that collects the exhaled breath sample.
[0065] At operation 806, receiving, via at least one processor (106), one or more signals generated by the at least two electrodes (104) in response to a change in electrical conductivity/or impedance. Further, when the isoprene molecules are absorbed onto the carbon nanomaterial coated to the at least two electrodes (104). The at least two electrodes (104) may alter the electrical conductivity which may be transmitted to the at least one processor (106). Further, the at least one processor (106) may be configured to receive the one or more signals and compares the changes in conductivity or impedance against a predefined baseline or reference value.
[0066] At operation 808, analysing, via the at least one processor (106), the one or more signals by using artificial intelligence (AI)/Machine learning (ML) algorithms to obtain one or more results. Further, the AI/ML model (110) is trained to recognize specific patterns or signatures in a data that are associated with lung cancer biomarkers. Further, the at least one processor (106) may be configured to analyse the patterns, and may differentiate between normal and cancerous breath samples by using AI/Ml algorithms to provide the one or more results. Further, the AI/ML algorithms may identify the specific patterns in the one or more signals that correlate with different concentrations of isoprene. Further, the one or more results may correspond to the concentration of isoprene in the breath sample. Further, the one or more result is a quantifiable value that may represent the presence of isoprene in the breath sample.
[0067] At operation 810, displaying, via computing device (116), the one or more results in a real time. Further, the computing device (116) is communicatively coupled with the at least one processor (106). Further, the computing device (116) may be configured to display the one or more results in a real time. Further, the computing unit is configured to display the information clearly and concisely, often in numerical form, showing the exact concentration of isoprene detected. Further, LCD display of the computing unit may comprise additional information, such as reference ranges, alerts if the concentration is above a certain threshold, or graphical representations of the data.
[0068] It has thus been seen that the sensing device (100) for early detecting of lung cancer and the method (800) for operating sensing device (100) for early detecting of lung cancer, as described. The sensing device (100) for early detecting of lung cancer and the method (800) for operating sensing device (100) for early detecting of lung cancer in any case could undergo numerous modifications and variants, all of which are covered by the same innovative concept; moreover, all of the details can be replaced by technically equivalent elements. In practice, the components used, as well as the numbers, shapes, and sizes of the components can be whatever according to the technical requirements. The scope of protection of the invention is therefore defined by the attached claims.
, Claims:1. A sensing device (100) for early detection of lung cancer, the sensing device (100) comprising:
a circuit board (102) fabricated with at least two electrodes (104), wherein the at least two electrodes (104) are coated with carbon nanomaterials;
a hollow tube having at least one inlet is fabricated in a proximity of the at least two electrodes (104), wherein the hollow tube facilitates a patient to blow air, via the at least one inlet;
at least one processor (106) is operationally coupled with the at least two electrodes (104), wherein the at least one processor (106) is configured to:
receive one or more signals generated by the at least two electrodes (104) in response to a change in electrical conductivity/or impedance,
wherein the change in electrical conductivity/ or impedance correspond to adsorption of isoprene onto a surface of the carbon nanomaterials,
analyse the one or more signals by using artificial intelligence (AI)/machine learning (ML) algorithms to obtain one or more results; and
a computing device (116) is communicatively coupled with the at least one processor (106), wherein the computing device (116) is configured to display the one or more results in a real time.

2. The sensing device (100) as claimed in claim 1, further the coated carbon nanomaterials corresponds to a sensing element.
3. The sensing device (100) as claimed in claim 1, wherein the carbon nanomaterials exhibits selectivity and sensitivity towards the isoprene.
4. The sensing device (100) as claimed in claim 1, wherein the isoprene corresponds to an indicative biomarkers of the lung cancer.
5. The sensing device (100) as claimed in claim 1, wherein the air may correspond to a breath sample.
6. The sending device as claimed in claim 1, wherein the at least one processor (106) is configured to compare normal air sample and cancerous air sample based on patterns or signatures associated with lung cancer biomarkers, via AI/ML algorithms.
7. The sensing device (100) as claimed in claim 1, wherein the one or more results may correspond to concentration of isoprene in the breath sample.
8. A method (800) comprising:
fabricating at least two electrodes (104) over a circuit board (102);
receiving air within a hollow tube having at least one inlet;
receiving, via at least one processor (106), one or more signals generated by the at least two electrodes (104) in response to a change in electrical conductivity/or impedance;
analysing, via the at least one processor (106), the one or more signals by using artificial intelligence (AI)/machine learning (ML) algorithms to obtain one or more results; and
displaying, via computing device (116), the one or more results in a real time.