Non-invasive fully automatic cancer diagnosis system based on microfluidics technology

Abstract

The present invention discloses a non-invasive, fully automatic cancer diagnosis system based on microfluidic technology, which relates to the field of medical technology. The system calculates and outputs the cleavage response efficiency REI through a dynamic photothermal response adaptation cleavage module to comprehensively evaluate the cleavage efficiency of the microcapsule. The cleavage response efficiency REI is further preliminarily compared by a cleavage evaluation unit. When the cleavage response efficiency REI is between 3.0 and 5.0, the thermal-optical coordination auxiliary imaging module is automatically triggered to calculate and output the photothermal offset compensation value Gcoord, which is used to evaluate the imaging offset trend and the degree of image response lag. When the photothermal offset compensation value Gcoord is higher than the set threshold, the system will automatically execute an adjustment strategy, extend the heating area time of the electric heating element, and fine-tune the LED irradiation eccentricity angle, thereby realizing dynamic correction of the photothermal excitation path under different sample states, thereby significantly improving the adequacy of the microcapsule cleavage and the stability of the imaging center.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a non-invasive fully automatic cancer diagnosis system based on microfluidics technology. Background Art

[0002] By integrating multiple biological technologies into miniaturized detection platforms, microfluidic devices enable efficient, contamination-free, high-precision, and highly accurate detection of extremely small sample volumes. Microfluidics has also been widely used in nucleic acid detection and analysis for cancer and infectious disease diagnosis. These developed microfluidic platforms have achieved significant advancements in detection speed and convenience, offering a new alternative to traditional, complex, and time-consuming detection systems such as PCR. Many microfluidic chips do not rely on amplification techniques, further accelerating the generation of results.

[0003] At present, mainstream cancer screening devices mostly use a blood sample collection + laboratory analysis process, involving multiple complex steps such as specimen collection, centrifugation, marker fluorescence detection and subsequent PCR. Although these systems are highly sensitive, they generally have a series of problems such as long detection cycles, strong dependence on the detection environment, large equipment size, and frequent manual intervention. In addition, some portable POCT devices that have emerged in recent years, although attempting to use microfluidic chips for detection, are mostly still at the level of manual reading or fixed condition detection, lacking an automatic photothermal control mechanism, and are highly sensitive to individual differences in samples, such as viscosity and protein loading. They are prone to technical bottlenecks such as false negatives or unreadable results due to failure to fully release microcapsules or image imaging offsets;

[0004] The main reason for this situation is that existing portable detection systems generally fail to establish a dynamic response coupling relationship between sample physical properties and reaction conditions, and in particular lack a closed-loop control mechanism for microcapsule release behavior, image offset, and imaging stability. When the sample in actual detection has characteristics such as high viscosity, high molecular background, or incomplete biotin release, the fixed-power LED irradiation or constant heating mechanism cannot adapt to the behavior patterns of different samples, resulting in insufficient lysis of the microcapsules, insufficient biotin release, or severe offset of the imaging area, further affecting the readability of the test strip and the reliability of the diagnostic results. This type of reaction mismatch not only reduces detection sensitivity and repeatability, but also easily leads to false positives, false negatives, or even no results at all, seriously restricting the feasibility and credibility of rapid cancer screening in non-professional environments. Summary of the Invention

[0005] In response to the deficiencies of the prior art, the present invention provides a non-invasive, fully automatic cancer diagnosis system based on microfluidics technology, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a non-invasive, fully automatic cancer diagnosis system based on microfluidics technology, including a sample loading module, a sample data processing module, a dynamic photothermal response adaptive cleavage module, a thermal-optical coordinated auxiliary imaging module, and an imaging stabilization analysis module;

[0007] The sample loading module collects sample data in real time by setting a microfluidic chip and setting a data acquisition node in the microfluidic chip, and wirelessly transmits the sample data to the central control processor by setting a central control processor in the microfluidic chip;

[0008] The sample data processing module pre-processes the sample data in the central control processor to obtain a standardized digital set, and constructs a sample database to store data of the standardized digital set;

[0009] The dynamic photothermal response adaptation cracking module extracts a standardized digital set to calculate and output a cracking response efficiency REI, and performs a preliminary comparative evaluation based on the output result of the cracking response efficiency REI to determine the cracking situation;

[0010] When the thermal-optical coordination auxiliary imaging module finds a cracking abnormality through a preliminary comparison and evaluation, it calculates and outputs a photothermal offset compensation value Gcoord based on the cracking response efficiency REI, and performs a secondary comparison and evaluation based on the output result of the photothermal offset compensation value Gcoord to determine the imaging conditions;

[0011] The imaging stability analysis module calculates and outputs an imaging stability evaluation index FSI through the photothermal offset compensation value Gcoord, and performs a comprehensive evaluation based on the output result of the imaging stability evaluation index FSI to determine the imaging stability.

[0012] Preferably, the sample loading module includes a sample loading unit and a sample transmission unit;

[0013] The sample loading unit loads the saliva sample into a disposable microfluidic chip and sets a data collection node in the microfluidic chip to collect sample data in real time;

[0014] An electric heating element and an LED irradiation window are embedded in the microfluidic chip, and a microcapsule is arranged in the detection channel of the microfluidic chip;

[0015] The data collection nodes include point A, point B, point C and point D;

[0016] Among them, point A represents the sample pretreatment area, point B represents the SPR photothermal cavity, point C represents the release channel, and point D represents the test strip reaction front end;

[0017] The sample data includes flow resistance, reflectivity before irradiation, reflectivity after irradiation, microresistance fluctuation, channel resistance and imaging area center deviation angle Pdef;

[0018] Point A integrates a high-frequency oscillation viscosity sensor array at the entrance of the microfluidic chip. When the saliva sample flows through, the electric heating element slightly heats the saliva sample to 2 degrees Celsius. At the same time, the high-frequency oscillation viscosity sensor array applies frequency vibration to detect the damping change of the sample to the oscillation signal to obtain the flow resistance;

[0019] At point B, a micro-optical detection probe group is set in the gold nanoparticle distribution area in the microfluidic chip. After the saliva sample is loaded, the electric heating element and the LED irradiation window are activated to irradiate the gold nanoparticles, converting light energy into local heat energy, heating the microcapsules, cracking and releasing biotin, and stimulating the plasma resonance (SPR) effect. The micro-optical detection probe group detects the real-time changes of the PR reflection peak position and intensity as the gold nanoparticles and light interact, and records the reflectivity before and after irradiation.

[0020] The C point is embedded in the release channel of the microfluidic chip, and the contents released after the microcapsule is lysed are measured by the embedded microelectrode pair at a sampling frequency of 10kHz to obtain microresistance fluctuations;

[0021] The point D is provided with an embedded CMOS image sensor upstream of the test strip of the microfluidic chip to track the center position of the stripe generated by the development reaction of the microcapsule releasing the contents in real time, and compare the deviation angle between the ideal central axis and the actual imaging position to obtain the imaging area center offset angle Pdef;

[0022] The sample transmission unit is configured with a central control processor in the microfluidic chip, and transmits the sample data acquired in real time to the central control processor via Bluetooth communication in the microfluidic chip.

[0023] Preferably, the sample data processing module includes a pre-processing unit and a data storage unit;

[0024] The pre-processing unit receives sample data in real time in the central control processor and pre-processes the sample data to obtain a standardized digital set;

[0025] The preprocessing includes feature extraction and standardization;

[0026] The standardized digital set includes relative viscosity change rate Urel, local SPR response change rate Espr, transient cracking resistance signal Srupt and imaging area center offset angle Pdef;

[0027] The feature extraction is performed by combining and calculating the sample data set in the central control processor to obtain the relative viscosity change rate Urel, the local SPR response change rate Espr and the transient cracking resistance signal Srupt;

[0028] The standardization process is performed by using a Z-Score standardization method to standardize the viscosity change rate Urel, the local SPR response change rate Espr, and the transient cracking resistance signal Srupt obtained by feature extraction, combined with the imaging area center offset angle Pdef, to remove the dimensional influence between the parameters, convert them into computer numbers, and then summarize the standardized parameters to obtain a standardized digital set;

[0029] The data storage unit constructs a sample database in the microfluidic chip, connects the sample database to the central control processor, and sets an automatic write port and an automatic write-out port for the sample database. After preprocessing, the standardized digital set obtained in real time is automatically stored in the sample database.

[0030] Preferably, the dynamic photothermal response adaptation cleavage module includes a cleavage analysis unit and a cleavage evaluation unit;

[0031] The pyrolysis analysis unit constructs a nonlinear coupling mapping algorithm model, which is comprehensively modeled by inputting energy, sample characteristics, release difficulty and heating time. The standardized digital set obtained in real time is extracted through an automatic writing port and input into the nonlinear coupling mapping algorithm model to calculate and output the pyrolysis response efficiency REI, thereby analyzing the effectiveness of microcapsule pyrolysis.

[0032] Preferably, the lysis evaluation unit performs a preliminary comparative evaluation based on the output result of the lysis response efficiency REI to determine the lysis status of the microcapsules in the microfluidic chip, and triggers the thermal-optical coordinated auxiliary imaging module based on the evaluation result. The specific evaluation content is as follows;

[0033] When the cleavage response efficiency REI ≥ 5.0, it indicates that the cleavage is normal, and imaging is performed directly, and the test paper color development stage is entered;

[0034] When 3.0≤cracking response efficiency REI<5.0, it indicates cracking abnormality, and the thermal-optical coordinated auxiliary imaging module is triggered;

[0035] When the lysis response efficiency REI is less than 3.0, it indicates lysis failure. At this time, the detection is stopped and resampling is prompted.

[0036] Preferably, the thermal-optical coordinated auxiliary imaging module includes a thermal-optical offset compensation analysis unit and an execution unit;

[0037] The thermal-optical offset compensation analysis unit calculates and outputs a photothermal offset compensation value Gcoord based on the currently acquired cracking response efficiency REI and the imaging area center offset angle Pdef after preliminary comparative assessment of cracking anomalies, and adaptively compensates for signal imaging cost and intensity loss.

[0038] Preferably, the execution unit performs a secondary comparative evaluation based on the output result of the photothermal offset compensation value Gcoord, and executes an adjustment strategy based on the secondary comparative evaluation result. The specific evaluation content is as follows:

[0039] When the light-thermal offset compensation value Gcoord ≥ 4.0, the adjustment strategy is triggered.

[0040] When the photothermal offset compensation value Gcoord is less than 4.0, no adjustment is required and development can be performed directly.

[0041] The adjustment strategy sends control instructions to the electric heating element and LED irradiation window through the central control processor, automatically extending the heating area time of the electric heating element by +5s, and at the same time fine-tuning the eccentric angle of the irradiation window by 5°. After the adjustment, the system is iteratively executed until the development iteration is completed.

[0042] Preferably, the imaging stability analysis module includes an imaging data extraction unit, an imaging analysis unit and an imaging stability evaluation unit;

[0043] The imaging data extraction unit performs image intensity analysis and colorimetric rate fitting on the test strip area after development;

[0044] The image intensity analysis of the test strip area is performed by using an embedded CMOS image sensor to perform grayscale intensity integration processing on the developed strip image to obtain the average pixel value Iavg of the central area and the background area value Ibg;

[0045] The colorimetric rate fitting is performed by recording the reaction development time tpeak;

[0046] The average pixel value Iavg, background area value Ibg and reaction development time tpeak are subjected to data standardization to eliminate parameter dimensions.

[0047] Preferably, the imaging analysis unit calculates and outputs the imaging stability evaluation index FSI based on the photothermal offset compensation value Gcoord output after development, combined with the obtained average pixel value Iavg, background area value Ibg and reaction development time tpeak, to analyze the readability and model uniformity of the image strips.

[0048] Preferably, the imaging stability evaluation unit performs a comprehensive evaluation based on the output result of the imaging stability evaluation index FSI to determine the stability of the imaging. The specific evaluation content is as follows:

[0049] When the imaging stability evaluation index FSI ≥ 2.5, it indicates that the imaging is stable, the results are automatically output, and the current imaging content is remotely transmitted to the diagnosis user end;

[0050] When the imaging stability evaluation index FSI is less than 2.5, it indicates imaging abnormality. At this time, the transmission is blocked and the microfluidic chip is restarted for re-imaging.

[0051] The present invention provides a non-invasive, fully automated cancer diagnosis system based on microfluidics technology. It has the following beneficial effects:

[0052] (1) The system sets up four highly sensitive data acquisition nodes at points A, B, C, and D in the microfluidic chip to collect sample data in real time. Feature extraction and Z-score normalization are performed on the sample data in the central control processor to construct a standardized digital set. This structure significantly improves the system's adaptability to complex sample viscosity changes, photothermal response differences, microcapsule lysis behavior, and other characteristics without changing the hardware conditions. It provides a unified data foundation for subsequent photothermal control and imaging quality assessment, and effectively enhances the system's detection stability and spectral adaptability.

[0053] (2) The system constructs a nonlinear coupling mapping model of the cleavage response efficiency REI through the dynamic photothermal response adaptation cleavage module, and comprehensively evaluates the cleavage efficiency of the microcapsules based on factors such as energy input, sample viscosity, release resistance and LED irradiation time. The cleavage response efficiency REI is further preliminarily compared through the cleavage evaluation unit. When the cleavage response efficiency REI is between 3.0 and 5.0, the thermal-optical coordination auxiliary imaging module is automatically triggered to calculate the output photothermal offset compensation value Gcoord, which is used to evaluate the imaging offset trend and the degree of image response lag. When the photothermal offset compensation value Gcoord is higher than the set threshold, the system automatically executes the adjustment strategy, extends the heating area time of the electric heating element and fine-tunes the LED irradiation eccentricity angle, realizing dynamic correction of the photothermal excitation path under different sample states, thereby significantly improving the adequacy of microcapsule cleavage and the stability of the imaging center.

[0054] (3) The system uses the imaging stability analysis module to extract three types of image feature parameters after the test strip is developed: the average pixel value Iavg, the background area value Ibg, and the reaction development time tpeak. The system then calculates the imaging stability evaluation index FSI in combination with the compensation value Gcoord. The system makes a graded judgment based on the FSI value. When the imaging stability evaluation index FSI ≥ 2.5, the system considers the image clear and readable, automatically outputs the test results, and remotely transmits the image to the user via a wireless module. When the imaging stability evaluation index FSI < 2.5, the system determines that there is an image blur or imaging offset problem, blocks the diagnostic data output, and restarts the microfluidic chip for retesting. This intelligent judgment and output mechanism ensures that each test result has imaging integrity and diagnostic credibility, effectively reduces the risk of false negatives and image misjudgments, and improves the system's clinical application safety and on-site detection reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the steps of the non-invasive fully automatic cancer diagnosis system based on microfluidic technology of the present invention;

[0056] Figure 2 Schematic diagram of the microfluidic chip;

[0057] Figure 3 Schematic diagram of the central processing unit of the microfluidic chip. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Example 1: The present invention provides a non-invasive fully automatic cancer diagnosis system based on microfluidics technology, please refer to Figure 1 , including sample loading module, sample data processing module, dynamic photothermal response adaptation cracking module, thermal-optical coordinated auxiliary imaging module and imaging stabilization analysis module;

[0060] The sample loading module collects sample data in real time by setting up a microfluidic chip and setting up a data acquisition node in the microfluidic chip. The central control processor is set in the microfluidic chip and the sample data is wirelessly transmitted to the central control processor.

[0061] The sample data processing module pre-processes the sample data in the central control processor to obtain a standardized digital set, and builds a sample database to store the standardized digital set;

[0062] The dynamic photothermal response adaptation cracking module extracts a standardized digital set, calculates and outputs the cracking response efficiency REI, and conducts a preliminary comparative evaluation based on the output results of the cracking response efficiency REI to determine the cracking situation;

[0063] When the thermal-optical coordination auxiliary imaging module finds a cracking abnormality through preliminary comparison and evaluation, it calculates and outputs the photothermal offset compensation value Gcoord based on the cracking response efficiency REI, and performs a secondary comparison and evaluation based on the output result of the photothermal offset compensation value Gcoord to determine the imaging conditions;

[0064] The imaging stability analysis module calculates the output of the imaging stability evaluation index FSI through the photothermal offset compensation value Gcoord, and performs a comprehensive evaluation based on the output result of the imaging stability evaluation index FSI to determine the imaging stability.

[0065] In this embodiment, the sample loading module of the system realizes non-invasive loading of non-blood samples such as saliva through a disposable microfluidic chip, and sets four high-sensitivity acquisition nodes A, B, C, and D inside the chip to obtain multi-source data such as the sample's flow resistance, SPR reflectivity changes, microresistance fluctuations, and imaging offset angle in real time, and then transmits it to the external processing unit through wireless communication by the embedded central control processor. The sample data processing module performs feature extraction and Z-score normalization on the collected multidimensional sample data within the central control processor, generating a standardized digital set that is stored in the sample database and constructing a dynamic sample profile. The dynamic photothermal response adaptive cleavage module constructs a nonlinear coupling mapping model based on the extracted digital set and outputs the cleavage response efficiency (REI) to evaluate and determine the release behavior of the microcapsules. If the REI falls below the stable range, the thermal-optical coordinated auxiliary imaging module is automatically triggered. Based on the REI value and the imaging offset angle, it calculates and outputs the photothermal offset compensation value (Gcoord). The photothermal path is corrected through methods such as LED irradiation eccentricity adjustment and heating delay. The imaging stability analysis module further outputs the imaging stability evaluation index (FSI) based on parameters such as the Gcoord value, image intensity, and colorimetric time. This determines the visibility and readability of the imaging bands and, based on this information, determines whether the test results should be directly output or the image reacquisition mechanism should be initiated. This system, without changing the hardware structure of the microfluidic chip, achieves intelligent adaptation and high-stability detection under different sample conditions through the coordinated linkage of acquisition parameter optimization, model guidance, automatic control, and image decision-making. By introducing three core calculation indicators, namely the cleavage response efficiency REI, the thermal offset compensation index Gcoord, and the imaging stability evaluation index FSI, algorithm-driven closed-loop control of the entire process from "sample loading, cleavage evaluation, image imaging and imaging interpretation" is achieved, which greatly improves the system's tolerance to sample differences, its self-repair ability for abnormal conditions, and the interpretability and credibility of the overall detection results, providing a more efficient, accurate and portable non-invasive diagnostic solution for early cancer screening.

[0066] Example 2: Please refer to Figure 1 、 Figure 2 and Figure 3 ,Specifically: the sample loading module includes a sample loading unit and a sample transmission unit;

[0067] The sample loading unit loads the saliva sample into a disposable microfluidic chip and sets a data acquisition node in the microfluidic chip to collect sample data in real time;

[0068] An electric heating element and an LED irradiation window are embedded in the microfluidic chip, and a microcapsule is set in the detection channel of the microfluidic chip;

[0069] The data collection nodes include point A, point B, point C and point D;

[0070] Among them, point A represents the sample pretreatment area, point B represents the SPR photothermal cavity, point C represents the release channel, and point D represents the test strip reaction front end;

[0071] The sample data include flow resistance, reflectivity before irradiation, reflectivity after irradiation, microresistance fluctuation, channel resistance and imaging area center offset angle Pdef;

[0072] Point A integrates a high-frequency oscillation viscosity sensor array at the entrance of the microfluidic chip. When the saliva sample flows through, the electric heating element slightly heats the saliva sample to 2 degrees Celsius. At the same time, the high-frequency oscillation viscosity sensor array applies frequency vibration, detects the damping change of the sample to the oscillation signal, and obtains the flow resistance.

[0073] At point B, a micro-optical detection probe group is set up in the gold nanoparticle distribution area of ​​the microfluidic chip. After the saliva sample is loaded, the electric heating element and the LED irradiation window are activated to irradiate the gold nanoparticles, converting light energy into local heat energy, heating the microcapsules, cracking and releasing biotin, and stimulating the plasma resonance SPR effect. The micro-optical detection probe group detects the real-time changes of the PR reflection peak position and intensity as the gold nanoparticles and light interact, and records the reflectivity before and after irradiation;

[0074] At point C, an embedded microelectrode pair is embedded in the release channel of the microfluidic chip. After the microcapsules are lysed, the contents released, such as biotin and ionic solution, are detected. The microresistance fluctuation is measured by the embedded microelectrode pair at a sampling frequency of 10kHz to determine whether the microcapsules are fully lysed, whether multiple points are lysed simultaneously, and to screen out false positive background or non-specific interference from the solution.

[0075] At point D, an embedded CMOS image sensor is placed upstream of the test strip on the microfluidic chip to track the center position of the stripe generated by the development reaction of the microcapsule releasing its contents in real time. The deviation angle between the ideal central axis and the actual imaging position is compared to obtain the imaging area center offset angle Pdef.

[0076] The sample transmission unit is configured with a central control processor in the microfluidic chip, and transmits the sample data acquired in real time to the central control processor via Bluetooth communication in the microfluidic chip.

[0077] In this embodiment, the system introduces saliva samples into a disposable microfluidic chip through a sample loading unit and sets multiple data acquisition nodes inside the chip, including the sample pretreatment area at point A, the SPR photothermal cavity at point B, the release channel at point C, and the test strip reaction front end at point D, to achieve real-time dynamic acquisition of key parameters of the sample throughout the entire diagnostic process. At point A, an integrated high-frequency oscillation viscosity sensor array works in conjunction with an electrothermal element to microheat and oscillate the sample during sample loading, detecting the damping effect of saliva on the frequency-oscillation signal in real time and extracting the sample flow resistance and viscosity change trend. At point B, a micro-optical detection probe group captures the dynamic changes in the SPR reflection peak of the gold nanoparticle area before and after LED irradiation, recording the reflectivity before and after irradiation, enabling precise monitoring of the local photothermal response. At point C, an embedded microelectrode pair continuously tracks the resistance fluctuations during microcapsule lysis at a 10kHz sampling frequency, determining whether lysis behavior has occurred and its spatial uniformity, effectively eliminating false signal interference caused by non-specific release. At point D, a CMOS image sensor module is installed to capture the offset angle Pdef between the center of the test strip imaging strip and the ideal alignment center in real time, providing basic data for subsequent imaging correction and quality assessment. The sample transmission unit completes the real-time reception and caching of all collected data through the microfluidic chip's built-in central control processor, and stably transmits the multi-dimensional sample data to an external analysis unit or mobile terminal via a Bluetooth communication module. The overall implementation of this module not only achieves accurate perception of the entire process of sample status, cleavage behavior and imaging quality within the microchip, but also enables the system to complete real-time adaptation and feedback control of complex sample behavior without adding additional hardware structure.

[0078] Example 3: Please refer to Figure 1 ,Specifically: the sample data processing module includes a pre-processing unit and a data storage unit;

[0079] The preprocessing unit receives sample data in real time in the central control processor and preprocesses the sample data to obtain a standardized digital set;

[0080] Preprocessing includes feature extraction and normalization;

[0081] The standardized digital set includes the relative viscosity change rate Urel, the local SPR response change rate Espr, the transient cleavage resistance signal Srupt, and the imaging area center offset angle Pdef;

[0082] Feature extraction is performed by combining and calculating the sample data sets in the central control processor to obtain the relative viscosity change rate Urel, the local SPR response change rate Espr and the transient cracking resistance signal Srupt;

[0083] The relative viscosity change rate Urel is obtained by extracting the flow resistance before and after micro-heating and calculating the change rate;

[0084] The local SPR response change rate Espr is obtained by calculating the change rate of the reflectivity before irradiation and the reflectivity after irradiation;

[0085] The transient rupture resistance signal Srupt is obtained by extracting the microresistance fluctuation after the instantaneous rupture of the microcapsule;

[0086] Standardization processing is performed by using the Z-Score normalization method to normalize the viscosity change rate Urel, local SPR response change rate Espr and transient cracking resistance signal Srupt obtained by feature extraction, combined with the imaging area center offset angle Pdef, to remove the dimensional influence between the parameters, convert them into computer numbers, and then summarize the normalized parameters to obtain a standardized digital set;

[0087] The data storage unit constructs a sample database in the microfluidic chip and connects the sample database to the central control processor. At the same time, it sets automatic write ports and automatic write-out ports for the sample database. After preprocessing, the standardized digital set obtained in real time is automatically stored in the sample database.

[0088] In this embodiment, the pre-processing unit of the system is set in the central control processor, which is used to receive data streams from the four collection points A, B, C, and D in the microfluidic chip in real time, and perform feature extraction and standardization on the data stream to finally generate a standardized digital set. Specifically, the system calculates the rate of change of the flow resistance difference collected before and after micro-heating to obtain the relative viscosity change rate Urel; extracts the local SPR response change rate Espr by analyzing the change in SPR reflectivity before and after irradiation; and obtains the transient micro-resistance fluctuation caused by microcapsule lysis by high-frequency sampling of microelectrode signals to obtain the transient lysis resistance signal Srupt. The above three characteristic parameters are combined with the imaging area center offset angle Pdef extracted from the image recognition module to form a multi-dimensional sample parameter set. In order to eliminate the dimensional differences between various physical parameters and improve the uniformity and comparability of data processing, the system uses the Z-Score standardization method to normalize the above characteristic parameters and output a dimensionless standardized digital set, so that the subsequent algorithm model has better computational stability and generalization ability. After processing, the standardized digital set is stored in real time in the sample database built in the microfluidic chip through the automatic write port, and supports data exchange with other modules through the automatic write port, forming a closed-loop information management mechanism.

[0089] Example 4: Please refer to Figure 1 ,Specifically: the dynamic photothermal response adaptation cleavage module includes a cleavage analysis unit and a cleavage evaluation unit;

[0090] The cracking analysis unit constructs a nonlinear coupling mapping algorithm model. The nonlinear coupling mapping algorithm model is comprehensively modeled by inputting energy, sample characteristics, release difficulty and heating time. The standardized digital set obtained in real time is extracted through the automatic writing port and input into the nonlinear coupling mapping algorithm model to calculate and output the cracking response efficiency REI to analyze the effectiveness of microcapsule cracking.

[0091] The cracking response efficiency REI is calculated and output by the following nonlinear coupling mapping algorithm model;

[0092] ;

[0093] Where ln represents the natural logarithm function, k1 represents the light and heat transfer rate constant, the device calibration dimensionless value, t led represents the excitation duration of the LED light source, which is controlled by the LED driver of the microfluidic chip with precise timing counting, and e represents an exponential function;

[0094] in, represents the energy input factor;

[0095] represents the lysis resistance factor;

[0096] represents the time response excitation factor.

[0097] The pyrolysis evaluation unit performs a preliminary comparative evaluation based on the output of the pyrolysis response efficiency REI to determine the pyrolysis status of the microcapsules in the microfluidic chip. Based on the evaluation results, the thermal-optical coordinated auxiliary imaging module is triggered. The specific evaluation contents are as follows;

[0098] When the cleavage response efficiency REI ≥ 5.0, it indicates that the cleavage is normal, and imaging is performed directly, and the test paper color development stage is entered;

[0099] When 3.0≤cracking response efficiency REI<5.0, it indicates cracking abnormality, and the thermal-optical coordinated auxiliary imaging module is triggered;

[0100] When the lysis response efficiency REI is less than 3.0, it indicates lysis failure. At this time, the detection is stopped and resampling is prompted.

[0101] In this embodiment, the system constructs a nonlinear coupling mapping algorithm model based on the standardized digital set extracted by the central control processor through the pyrolysis analysis unit, comprehensively considering the input energy, represented by the SPR response change rate Espr, sample characteristics, such as the relative viscosity change rate Urel, the pyrolysis release difficulty, such as the transient resistance signal Srupt, and the LED irradiation duration t ledThrough the nonlinear coupling mapping algorithm model, the system can quantitatively analyze the lysis efficiency in the early stage of chip operation to determine whether the microcapsules have reached the critical conditions for full release. The lysis evaluation unit performs a preliminary comparative evaluation based on the numerical results of the lysis response efficiency REI and sets a judgment threshold: when the lysis response efficiency REI ≥ 5.0, it indicates that the lysis is sufficient and the test strip development imaging process can be directly entered; when the lysis response efficiency REI is between 3.0–5.0, it indicates that the lysis is abnormal, and the system automatically triggers the thermal-optical coordination auxiliary imaging module to compensate and optimize subsequent imaging; when the lysis response efficiency REI is < 3.0, it is determined that the lysis has failed, and the system will terminate the detection process and prompt resampling or parameter reset. Through the implementation of this module, the present invention has for the first time constructed an adaptive decision-making mechanism with lysis efficiency as the core indicator, overcoming the problems of fixed photothermal parameters, poor sample adaptability, and no early warning of lysis failure in traditional microfluidic detection. This mechanism enables the system to realize intelligent judgment and response control of the lysis status in the early stages, effectively improving the transparency of the detection process, judgment accuracy and abnormal fault tolerance, and significantly enhancing the stability, reliability and adaptability of the overall system. It is especially suitable for early cancer screening scenarios with large sample diversity and strong demand for non-invasive detection.

[0102] Example 5: Please refer to Figure 1 ,Specifically: the thermal-optical coordinated auxiliary imaging module includes a thermal-optical offset compensation ,analysis unit and an execution unit;

[0103] After the initial comparison and evaluation shows abnormal cleavage, the thermal-optical offset compensation analysis unit calculates and outputs the thermal-optical offset compensation value Gcoord based on the currently acquired cleavage response efficiency REI and the imaging area center offset angle Pdef, and adaptively compensates for signal imaging cost and intensity loss.

[0104] The optical thermal offset compensation value Gcoord is calculated and output by the following algorithm formula:

[0105] ;

[0106] Where, Represents the micro-offset stability constant, dimensionless, calibrated value, t corr It represents the automatic compensation time, that is, the time for the system to perform feedback control, and T0 represents the standard LED irradiation time.

[0107] The execution unit performs a secondary comparative evaluation based on the output result of the optical thermal offset compensation value Gcoord, and executes the adjustment strategy based on the secondary comparative evaluation result. The specific evaluation content is as follows;

[0108] When the light-thermal offset compensation value Gcoord ≥ 4.0, the adjustment strategy is triggered.

[0109] When the photothermal offset compensation value Gcoord is less than 4.0, no adjustment is required and development can be performed directly.

[0110] The adjustment strategy sends control instructions to the electric heating element and LED irradiation window through the central control processor, automatically extending the heating area time of the electric heating element by +5s, and at the same time fine-tuning the eccentric angle of the irradiation window by 5°. After the adjustment, the system is iteratively executed until the development iteration is completed.

[0111] In this embodiment, after a preliminary assessment detects that the cracking response efficiency REI is in the abnormal range, the system's thermal offset compensation analysis unit calculates and outputs a photothermal offset compensation value Gcoord based on the imaging area's central offset angle Pdef. The execution unit performs a secondary comparative evaluation based on the output of the photothermal offset compensation value Gcoord. If the photothermal offset compensation value Gcoord is ≥4.0, the system determines that the current imaging conditions are insufficient for direct high-quality imaging. At this point, the central control processor issues instructions to the electric heating element and LED illumination window, automatically extending the heating time by +5 seconds and fine-tuning the illumination window's eccentricity by 5° to change the local distribution of photothermal energy and optimize the coupling between the microcapsule release area and the test paper development strip. If the photothermal offset compensation value Gcoord is <4.0, the offset is considered acceptable and the system directly enters the imaging phase. All adjustment processes are automatically iterated under system control until imaging is complete and imaging parameters are stable. Through the implementation of this module, the present invention establishes a thermo-optical compensation algorithm model with REI and the image center offset angle Pdef as core input variables. This model dynamically adjusts the photothermal excitation path and heating strategy, achieving intelligent transition control from insufficient lysis to readable imaging. This mechanism effectively addresses imaging offset, band skew, or inconsistent signal intensity caused by uneven energy input or differences in sample rheology. It improves the accuracy, alignment precision, and color integrity of test strip imaging, enhances the system's self-repair capabilities and imaging robustness under abnormal conditions, and ensures controllable image quality and reliable data output for non-invasive cancer detection results.

[0112] Example 6: Please refer to Figure 1 ,Specifically: the imaging stability analysis module includes an imaging data extraction unit, an ,imaging analysis unit and an imaging stability evaluation unit;

[0113] The imaging data extraction unit performs image intensity analysis and colorimetric rate fitting on the test strip area after development;

[0114] Image intensity analysis of the test strip area is performed by using an embedded CMOS image sensor to perform grayscale intensity integration processing on the developed strip image to obtain the average pixel value Iavg of the central area and the background area value Ibg;

[0115] Colorimetric rate fitting monitors the rising process of the image from zero to maximum intensity and records the reaction development time tpeak, which is the time point when the image signal first exceeds 95% of the maximum value;

[0116] The average pixel value Iavg, background area value Ibg and reaction development time tpeak are subjected to data standardization to eliminate parameter dimensions.

[0117] The imaging analysis unit calculates and outputs the imaging stability evaluation index FSI based on the photothermal offset compensation value Gcoord output after development, combined with the obtained average pixel value Iavg, background area value Ibg and reaction development time tpeak, to analyze the readability and model uniformity of the image strips;

[0118] The imaging stability evaluation index FSI is calculated and output by the following algorithm formula;

[0119] ;

[0120] Where ln represents the natural logarithm function, k0 represents the colorimetric response constant, which is determined by the reagent concentration and the imaging amplification system, and a represents the colorimetric time index. Represents the fluid correction coefficient, and the above values ​​are dimensionless parameters;

[0121] It indicates the binding strength of streptavidin and is used to quantitatively reflect the binding activity strength of biotin released from microcapsules with streptavidin fixed on the test strip. It is one of the core reference quantities for imaging intensity and diagnostic reliability.

[0122] The imaging stability evaluation unit performs a comprehensive evaluation based on the output results of the imaging stability evaluation index (FSI) to determine the stability of the image. The specific evaluation contents are as follows:

[0123] When the imaging stability evaluation index FSI ≥ 2.5, it indicates that the imaging is stable, clear, accurate in positioning, with high contrast in strips, and automatic result output, the current imaging content is remotely transmitted to the diagnostic user end;

[0124] When the imaging stability evaluation index FSI is less than 2.5, it indicates abnormal imaging, with image blur, distortion, unreadable and slightly offset steady state. At this time, the transmission is blocked and the microfluidic chip is restarted for re-imaging.

[0125] In this embodiment, after the test strip is developed, the system's imaging data extraction unit uses an embedded CMOS image sensor to capture images of the developed area. The grayscale intensity integration method is used to extract the average pixel value Iavg of the strip area and the grayscale value Ibg of the background area, and the signal intensity contrast is further calculated. Simultaneously, a colorimetric rate fitting method is used to monitor the development process from the initial state to the point where the image intensity reaches 95% of its maximum value in real time, and the reaction development time tpeak is extracted. These three parameters constitute the core characteristic parameters of the image response. Next, the imaging analysis unit uses these extracted parameters, combined with the photothermal offset compensation value Gcoord output by the photothermal coordination auxiliary imaging module in the previous stage, to comprehensively calculate the imaging stability evaluation index FSI. Finally, the imaging stability evaluation unit sets a grading judgment mechanism based on the output results of the FSI: when the imaging stability evaluation index FSI ≥ 2.5, the image quality is judged to be stable, clear, and accurately aligned. The system automatically outputs the test results and remotely transmits the test strip image to the diagnostic user terminal through the wireless module; when the imaging stability evaluation index FSI < 2.5, it is judged to be an imaging abnormality, and there may be phenomena such as image blur, distortion, or slight offset. The system suspends the output and automatically restarts the microfluidic chip for secondary imaging. Through the implementation of this module, the present invention realizes an intelligent image quality judgment mechanism based on imaging data, and establishes a complete set of stability assurance links from physical imaging signals to final diagnostic output. Compared with the traditional detection method in which image results require manual judgment or simple threshold judgment, this module realizes self-assessment, self-screening and self-correction of imaging data through multi-dimensional parameter fusion and nonlinear computational models, significantly improving the system's ability to control the output of readable images, identify imaging abnormalities, and ensure the credibility of terminal results, thereby improving the overall automation, reliability and remote adaptability of the system in the early cancer screening scenario.

[0126] While embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A non-invasive, fully automated cancer diagnosis system based on microfluidics, characterized by: It includes sample loading module, sample data processing module, dynamic photothermal response adaptation cracking module, thermal-optical coordinated auxiliary imaging module and imaging stabilization analysis module; The sample loading module collects sample data in real time by setting a microfluidic chip and setting a data acquisition node in the microfluidic chip, and wirelessly transmits the sample data to the central control processor by setting a central control processor in the microfluidic chip; The sample data processing module pre-processes the sample data in the central control processor to obtain a standardized digital set, and constructs a sample database to store data of the standardized digital set; The dynamic photothermal response adaptation cracking module extracts a standardized digital set to calculate and output a cracking response efficiency REI, and performs a preliminary comparative evaluation based on the output result of the cracking response efficiency REI to determine the cracking situation; When the thermal-optical coordination auxiliary imaging module finds a cracking abnormality through a preliminary comparison and evaluation, it calculates and outputs a photothermal offset compensation value Gcoord based on the cracking response efficiency REI, and performs a secondary comparison and evaluation based on the output result of the photothermal offset compensation value Gcoord to determine the imaging conditions; The imaging stability analysis module calculates and outputs an imaging stability evaluation index FSI through the photothermal offset compensation value Gcoord, and performs a comprehensive evaluation based on the output result of the imaging stability evaluation index FSI to determine the imaging stability; The sample loading module includes a sample loading unit and a sample transmission unit; The sample loading unit loads the saliva sample into a disposable microfluidic chip and sets a data collection node in the microfluidic chip to collect sample data in real time; An electric heating element and an LED irradiation window are embedded in the microfluidic chip, and a microcapsule is arranged in the detection channel of the microfluidic chip; The data collection nodes include point A, point B, point C and point D; Among them, point A represents the sample pretreatment area, point B represents the SPR photothermal cavity, point C represents the release channel, and point D represents the test strip reaction front end; The sample data includes flow resistance, reflectivity before irradiation, reflectivity after irradiation, microresistance fluctuation, channel resistance and imaging area center deviation angle Pdef; Point A integrates a high-frequency oscillation viscosity sensor array at the entrance of the microfluidic chip. When the saliva sample flows through, the electric heating element slightly heats the saliva sample to 2 degrees Celsius. At the same time, the high-frequency oscillation viscosity sensor array applies frequency vibration to detect the damping change of the sample to the oscillation signal to obtain the flow resistance; At point B, a micro-optical detection probe group is set in the gold nanoparticle distribution area in the microfluidic chip. After the saliva sample is loaded, the electric heating element and the LED irradiation window are activated to irradiate the gold nanoparticles, converting light energy into local heat energy, heating the microcapsules, cracking and releasing biotin, and stimulating the plasma resonance (SPR) effect. The micro-optical detection probe group detects the real-time changes of the PR reflection peak position and intensity as the gold nanoparticles and light interact, and records the reflectivity before and after irradiation. The C point is embedded in the release channel of the microfluidic chip, and the contents released after the microcapsule is lysed are measured by the embedded microelectrode pair at a sampling frequency of 10kHz to obtain microresistance fluctuations; The point D is provided with an embedded CMOS image sensor upstream of the test strip of the microfluidic chip to track the center position of the stripe generated by the development reaction of the microcapsule releasing the contents in real time, and compare the deviation angle between the ideal central axis and the actual imaging position to obtain the imaging area center offset angle Pdef; The sample transmission unit is configured to be a central control processor in the microfluidic chip, and transmits the sample data acquired in real time to the central control processor via Bluetooth communication in the microfluidic chip; The sample data processing module includes a pre-processing unit and a data storage unit; The pre-processing unit receives sample data in real time in the central control processor and pre-processes the sample data to obtain a standardized digital set; The preprocessing includes feature extraction and standardization; The standardized digital set includes relative viscosity change rate Urel, local SPR response change rate Espr, transient cracking resistance signal Srupt and imaging area center offset angle Pdef; The feature extraction is performed by combining and calculating the sample data set in the central control processor to obtain the relative viscosity change rate Urel, the local SPR response change rate Espr and the transient cracking resistance signal Srupt; The standardization process is performed by using a Z-Score standardization method to standardize the viscosity change rate Urel, the local SPR response change rate Espr, and the transient cracking resistance signal Srupt obtained by feature extraction, combined with the imaging area center offset angle Pdef, to remove the dimensional influence between the parameters, convert them into computer numbers, and then summarize the standardized parameters to obtain a standardized digital set; The data storage unit constructs a sample database in the microfluidic chip, connects the sample database to the central control processor, and sets an automatic write port and an automatic write-out port for the sample database. After preprocessing, the standardized digital set obtained in real time is automatically stored in the sample database.

2. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 1, characterized in that: The dynamic photothermal response adaptation cleavage module includes a cleavage analysis unit and a cleavage evaluation unit; The pyrolysis analysis unit constructs a nonlinear coupling mapping algorithm model, which is comprehensively modeled by inputting energy, sample characteristics, release difficulty and heating time. The standardized digital set obtained in real time is extracted through an automatic writing port and input into the nonlinear coupling mapping algorithm model to calculate and output the pyrolysis response efficiency REI, thereby analyzing the effectiveness of microcapsule pyrolysis.

3. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 2, characterized in that: The lysis evaluation unit performs a preliminary comparative evaluation based on the output results of the lysis response efficiency REI to determine the lysis status of the microcapsules in the microfluidic chip, and triggers the thermal-optical coordinated auxiliary imaging module based on the evaluation results. The specific evaluation contents are as follows; When the cleavage response efficiency REI ≥ 5.0, it indicates that the cleavage is normal, and imaging is performed directly, and the test paper color development stage is entered; When 3.0≤cracking response efficiency REI<5.0, it indicates cracking abnormality, and the thermal-optical coordinated auxiliary imaging module is triggered; When the lysis response efficiency REI is less than 3.0, it indicates lysis failure. At this time, the detection is stopped and resampling is prompted.

4. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 2, characterized in that: The thermal-optical coordinated auxiliary imaging module includes a thermal-optical offset compensation analysis unit and an execution unit; The thermal-optical offset compensation analysis unit calculates and outputs a photothermal offset compensation value Gcoord based on the currently acquired cracking response efficiency REI and the imaging area center offset angle Pdef after preliminary comparative assessment of cracking anomalies, and adaptively compensates for signal imaging cost and intensity loss.

5. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 4, characterized in that: The execution unit performs a secondary comparative evaluation based on the output result of the photothermal offset compensation value Gcoord, and executes an adjustment strategy based on the secondary comparative evaluation result. The specific evaluation content is as follows; When the light-thermal offset compensation value Gcoord ≥ 4.0, the adjustment strategy is triggered. When the photothermal offset compensation value Gcoord is less than 4.0, no adjustment is required and development can be performed directly. The adjustment strategy sends control instructions to the electric heating element and LED irradiation window through the central control processor, automatically extending the heating area time of the electric heating element by +5s, and at the same time fine-tuning the eccentric angle of the irradiation window by 5°. After the adjustment, the system is iteratively executed until the development iteration is completed.

6. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 5, characterized in that: The imaging stabilization analysis module includes an imaging data extraction unit, an imaging analysis unit, and an imaging stability evaluation unit; The imaging data extraction unit performs image intensity analysis and colorimetric rate fitting on the test strip area after development; The image intensity analysis of the test strip area is performed by using an embedded CMOS image sensor to perform grayscale intensity integration processing on the developed strip image to obtain the average pixel value Iavg of the central area and the background area value Ibg; The colorimetric rate fitting is performed by recording the reaction development time tpeak; The average pixel value Iavg, background area value Ibg and reaction development time tpeak are subjected to data standardization to eliminate parameter dimensions.

7. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 6, characterized in that: The imaging analysis unit calculates and outputs the imaging stability evaluation index FSI based on the photothermal offset compensation value Gcoord output after development, combined with the obtained average pixel value Iavg, background area value Ibg and reaction development time tpeak, and analyzes the readability and model uniformity of the image strips.

8. The non-invasive, fully automatic cancer diagnosis system based on microfluidics technology according to claim 7, characterized in that: The imaging stability evaluation unit performs a comprehensive evaluation based on the output result of the imaging stability evaluation index FSI to determine the stability of the imaging. The specific evaluation contents are as follows; When the imaging stability evaluation index FSI ≥ 2.5, it indicates that the imaging is stable, the results are automatically output, and the current imaging content is remotely transmitted to the diagnosis user end; When the imaging stability evaluation index FSI is less than 2.5, it indicates imaging abnormality. At this time, the transmission is blocked and the microfluidic chip is restarted for re-imaging.

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