Methods and systems for akI recognition incorporating biomarkers and imaging features

Abstract

The present application relates to the technical field of acute kidney injury identification, in particular to an AKI identification method and system combining biomarkers and image features, comprising the following steps: based on high-risk population of acute kidney injury, analyzing the matching relationship between the concentration trend of biomarkers and historical mode direction, constructing dynamic linkage features, extracting kidney region image rotation plane features, identifying structural continuity abnormalities, and calculating the offset between image trajectories and physiological fluctuations. In the present application, the concentration threshold is replaced by a symbol sequence, and the direction consistency is identified as an early response feature, breaking the limitations of static indicators. The image processing adopts a rotation axis section construction method, which unifies the texture, gray scale and density distribution performance under multiple angle images, enhances the spatial tracking ability of anatomical features, and forms a time period mapping basis for the linkage offset between the gray scale trajectory and the physiological parameter fluctuation. The overlapping segment coverage is used to determine the cooperative state, and the time locking determination between the structural change and the physiological trend is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of acute kidney injury recognition, in particular to an AKI recognition method and system combining biomarkers and image features. BACKGROUND

[0002] The technical field of acute kidney injury (AKI) recognition belongs to the key direction of the cross-application of medical diagnosis and monitoring, and covers early detection of abnormal kidney function, disease progression assessment and intervention decision support and the like. The field comprehensively uses multidisciplinary technical means such as clinical biochemistry, physiological monitoring, medical imaging, data modeling and intelligent algorithms, and aims to improve the diagnostic accuracy and timeliness of AKI, reduce disease delay and reduce patient mortality.

[0003] Among them, the AKI recognition method combining biomarkers and image features is a diagnostic technology that comprehensively uses biomarker information and medical image data, and is used for early recognition and risk assessment of acute kidney injury. By fusing biochemical indicators (such as serum creatinine, NGAL, etc.) and kidney image manifestations (such as structural changes, perfusion characteristics, etc.), the diagnostic sensitivity and specificity of AKI can be improved, and the method is widely used in clinical diagnosis auxiliary systems or critical patient monitoring devices and the like.

[0004] The prior art often determines the risk of acute kidney injury by single-point concentration, ignores the value of index fluctuation trend in diagnosis, is easy to cause delayed recognition, and mainly uses two-dimensional static sections for image processing. The spatial consistency evaluation path of the structure under multiple angles has not been established. The texture distortion and density overlap caused by the rotation direction cannot be accurately identified, causing judgment deviation of the anatomical structure. The linkage recognition path of physiological parameters and image results is mainly located in the later decision-making process, and lacks pre-alignment and dynamic judgment means, which is easy to cause the problem that the image abnormality and the physiological abnormality are not synchronized. In addition, in the scene where the postoperative state fluctuates frequently and high-frequency monitoring demand is prominent, the recognition mechanism of the prior art is lagging behind, and potential high-risk performance in the fluctuation transition section cannot be captured in time, which affects the timeliness of intervention and reduces the pertinence and stability of the diagnostic response. SUMMARY

[0005] In order to solve the technical problems in the prior art that the risk of acute kidney injury is often judged by single point concentration, the value of index fluctuation trend in diagnosis is ignored, delay recognition is easy to occur, image processing is mainly based on two-dimensional static section, the spatial consistency evaluation path under the structure of multiple angles is not established, the texture distortion and density overlap caused by the rotation direction cannot be accurately identified, the anatomical structure judgment deviation is caused, the linkage recognition path of physiological parameters and image results is mainly located in the late decision-making process, the pre-alignment and dynamic judgment means are lacked, the problem that image abnormalities and physiological abnormalities are not synchronized is easy to occur, in addition, in the scene where postoperative state fluctuation occurs frequently and high-frequency monitoring demand is prominent, the recognition mechanism of the prior art is lagging behind, potential high-risk performance in the fluctuation transition section cannot be captured in time, the intervention timeliness is affected, and the technical problems that the diagnosis response is reduced in pertinence and stability are solved, and the AKI recognition method combining biomarkers and image features is provided. The technical scheme is as follows:

[0006] In one aspect, an AKI recognition method combining biomarkers and image features is provided, including the following steps:

[0007] S1: Based on the high-risk population of acute kidney injury, creatinine, cystatin C and IL-18 sequence are analyzed, the change direction is calculated, the lag mode is matched, the consistent trend segment is screened, the physiological trend continuity is judged, and the dynamic trend linkage feature is obtained;

[0008] S2: According to the dynamic trend linkage feature, the kidney portal vein path symmetry axis is optimized, the rotation plane is constructed, the gray scale, texture and density difference are analyzed, the structure consistency is judged, and the rotation plane anatomical feature group is obtained;

[0009] S3: According to the rotation plane anatomical feature group, the gray scale, texture and density changes between image frames are compared, the direction consistency is judged, the fluctuation amplitude is analyzed, the mutation area and deviation trend are identified, and the structure continuity abnormal data is obtained;

[0010] S4: According to the structure continuity abnormal data, the kidney cortex gray scale track is calculated, the moving direction is analyzed, the creatinine, urea nitrogen and body fluid trend are compared, the linkage deviation is judged, the corresponding abnormal segment is screened, and the multi-modal cooperative deviation marker is obtained;

[0011] S5: Based on the multi-modal cooperative deviation marker, the lag segment and image track key node are compared, the time overlap range is screened, the coverage ratio is calculated, whether the recognition condition is met is judged, and the early cooperative abnormal locking result is obtained.

[0012] In another aspect, the dynamic trend linkage feature includes a concentration direction symbol, a trend sequence number, and a lag matching identifier, the rotation plane anatomical feature group includes a symmetric axis gray baseline, a rotation plane texture distribution, and a voxel aggregation degree, the structural continuity abnormality data includes a parameter fluctuation boundary, a structural deflection dimension, and a spatial mutation label, the multi-modal collaborative bias marker includes an image translation path, a physiological fluctuation trajectory, and a linkage difference region, and the early collaborative abnormality locking result includes an abnormality synchronization segment, a cross-alarm section, and early warning state information.

[0013] In another aspect, the acquisition step of the dynamic trend linkage feature is specifically:

[0014] S101: Based on the high-risk population of acute kidney injury, analyze the monitoring sequence of serum creatinine, cystatin C and IL-18, compare the detection results of adjacent time nodes in turn, mark the relative increase or decrease trend, and obtain the direction transfer marker sequence;

[0015] S102: Compare the current time segment in the direction transfer marker sequence with the adjacent segment in the historical monitoring sequence, align the direction markers by time points, judge the consistency, analyze whether the direction in each corresponding relationship maintains the same trend, and count the proportion of time nodes with consistent direction in the total segments to obtain the time consistency proportion;

[0016] S103: Screen the segments with a duration that meets the basic judgment requirement and a proportion that meets the linkage standard in the time consistency proportion, analyze whether the change direction in the segments that meet the conditions remains consistent, output the monitoring time range with common trend direction, and obtain the dynamic trend linkage feature.

[0017] In another aspect, the acquisition step of the rotation plane anatomical feature group is specifically:

[0018] S201: According to the dynamic trend linkage feature, locate the approximate axis distribution of the renal portal vein region in the gray image, analyze the symmetry intensity distribution of the two sides of the renal portal structure in the gray image, optimize the center trend of the symmetric axis, adjust the distribution position of the axis crossing point in the gray gradient change region, and obtain the symmetric path distribution information;

[0019] S202: Call the symmetric path distribution information to construct a rotation plane set with the axis as the reference, analyze the lateral variation amplitude of the gray distribution in each plane, calculate the dynamic distribution sequence of the gray difference on both sides of the axis under multi-angle rotation, judge whether the gray feature maintains a continuous transition state in the rotation process, and obtain the angle gray variation trend;

[0020] S203: According to the angle gray scale change trend, the arrangement direction of the kidney area texture in each plane is compared with the spatial position relationship of the voxel aggregation area in the image, whether the structure distribution in the multi-angle image section exists common characteristics is judged, the spatial combination of gray scale, texture and density is integrated, and the rotation plane anatomical feature group is obtained.

[0021] On the other hand, the structure continuity abnormal data acquisition step is specifically:

[0022] S301: According to the rotation plane anatomical feature group, the gray scale, texture direction and density change trend of the continuous image frame in the kidney area range are analyzed, whether the spatial direction between the time nodes is consistent is compared, whether the direction reversal or offset feature exists is identified, and the joint change direction track sequence is obtained.

[0023] S302: The joint change direction track sequence is called to screen the image segments with discontinuous trend on the gray scale, texture and density path, whether the position offset in the corresponding image frame is concentrated in the target area is judged, the corresponding image coordinate section and its position distribution are marked, and the local space disturbance area is obtained.

[0024] S303: According to the local space disturbance area, whether the structure axis in the target area exists track abnormality is judged, whether the offset behavior exceeds the spatial tolerance range is analyzed, and the structure offset distribution range and deviation condition are tested according to the structure offset, and the structure continuity abnormal data is obtained.

[0025] On the other hand, whether the offset behavior exceeds the spatial tolerance range is analyzed by using the formula:

[0026] ;

[0027] The offset feature value is calculated , whether the structure axis in the target area exists track abnormality is judged, wherein, represents the number of structure control points, represents the actual measured coordinates of the first structure control point in space, represents the design coordinates of the first structure control point in space, represents the mean value of the offset amount of all structure control points, represents the tolerance limit threshold of structure offset.

[0028] On the other hand, the multi-modal collaborative offset marking acquisition step is specifically:

[0029] S401: Based on the structural continuity anomaly data, analyze the gray-level centroid coordinate distribution of the renal cortex region in consecutive image frames, calculate the horizontal and vertical translation paths of the centroid position between each image frame, determine whether the paths form a forward arrangement or a position jump in the image plane, and obtain the displacement trend of the image sequence.

[0030] S402: Based on the displacement trend of the image sequence, compare the direction of change of creatinine, blood urea nitrogen and body fluid parameters in the same time period, determine whether the continuous trend of the parameter sequence deviates from the direction of the image trajectory, identify the combination of inconsistent trends, and obtain the parameter trajectory offset coefficient.

[0031] S403: Based on the parameter trajectory offset coefficient, filter out time segments with linkage anomalies in the deviation coefficient fluctuation range, determine whether the segment constitutes a co-occurrence structure between the image space and the detection parameters, and identify the spatial coordinates and data intervals of this type of time segment to obtain multimodal collaborative offset markers.

[0032] On the other hand, the determination of whether the continuous trend of the parameter sequence deviates from the direction of the image trajectory is made using the following formula:

[0033] ;

[0034] Identify combinations of terms with inconsistent trends to obtain parameter trajectory offset coefficients. ,in, Representing the The difference between creatinine, blood urea nitrogen, or body fluid parameters at a given time and the previous time. Representing the The change in the grayscale center trajectory along the principal axis in a frame image. Representing the The change in the direction vector at time t, Representing the The difference between the physiological parameter value at time 1 and the average value of the physiological parameter at the previous three consecutive time 1. Table 1 Standard deviation of grayscale distribution in the renal cortex region of a frame rotated planar image. It represents the total number of image frames within the selected image time period.

[0035] On the other hand, the specific steps for obtaining the early collaborative anomaly locking results are as follows:

[0036] S501: Based on the multimodal collaborative offset marker, analyze the positioning information of each time period, compare its time label with the time index of the lagging segment and image key point in the dynamic trend linkage feature, filter the segment range that intersects in the time dimension, and obtain the synchronous overlapping time period index set.

[0037] S502: Call the synchronization overlap period index set, calculate the coverage degree of the overlapping segment in the image key point sequence, judge whether the coverage ratio reaches the linkage recognition reference range, recognize the segment sequence with strong continuity, and mark the segment boundary index to obtain the overlapping segment coverage ratio.

[0038] S503: According to the overlapping segment coverage ratio, judge whether the linkage range between the image trajectory and the physiological index exists a centralized deviation, filter the time nodes showing consistent linkage response in the continuous segment, and integrate the time range and key feature annotation information to obtain the early collaborative abnormality locking result.

[0039] On the other hand, an AKI recognition system combining biomarkers and image features is provided, which is applied to the AKI recognition method combining biomarkers and image features, and includes:

[0040] The trend linkage recognition module analyzes creatinine, cystatin C and IL-18 sequence based on the high-risk population of acute kidney injury, calculates the change direction, matches the lag mode, filters the trend consistent segment, judges the physiological trend continuity, and obtains the dynamic trend linkage feature.

[0041] The symmetrical structure extraction module optimizes the symmetry axis of the renal portal vein path according to the dynamic trend linkage feature, constructs a rotation plane, analyzes the differences in gray, texture and density, judges the structural consistency, and obtains a rotation plane anatomical feature group.

[0042] The image variation detection module compares the gray, texture and density changes between image frames according to the rotation plane anatomical feature group, judges the direction consistency, analyzes the fluctuation amplitude, identifies the mutation area and deviation trend, and obtains the structural continuity abnormal data.

[0043] The linkage deviation calculation module calculates the renal cortex gray trajectory according to the structural continuity abnormal data, analyzes the moving direction, compares the creatinine, urea nitrogen and body fluid trend, judges the linkage deviation, filters the corresponding abnormal segment, and obtains the multi-modal collaborative deviation marker.

[0044] The abnormal timing locking module compares the lag segment and the image trajectory key node based on the multi-modal collaborative deviation marker, filters the time overlap range, calculates the coverage ratio, judges whether the recognition condition is met, and obtains the early collaborative abnormality locking result.

[0045] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0046] By replacing the concentration threshold with a symbol sequence, the directional consistency is identified as an early response feature, breaking the limitations of static indicators. Image processing uses a rotating axis section construction method to unify the texture, gray scale and density distribution performance under multiple angle images, enhancing the spatial tracking ability of anatomical features. The linkage offset between the gray scale trajectory and the physiological parameter fluctuation forms the time period mapping basis, and the overlapping segment coverage is used to identify the cooperative state, realizing the time locking judgment between structural changes and physiological trends. This logic improves the response ability of the recognition mechanism to continuous abnormal trends, avoids the dependence on single time point errors, enhances the sensitivity of atypical state recognition, and helps to quickly separate the high-risk evolution stage, thereby supporting early judgment and precise intervention under complex conditions. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0048] Figure 1 It is the main step flow chart of the present application;

[0049] Figure 2 It is the step flow chart of S1 of the present application;

[0050] Figure 3 It is the step flow chart of S2 of the present application;

[0051] Figure 4 It is the step flow chart of S3 of the present application;

[0052] Figure 5 It is the step flow chart of S4 of the present application;

[0053] Figure 6 It is the step flow chart of S5 of the present application;

[0054] Figure 7 It is the system block diagram of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the present application will be described below in combination with the drawings.

[0056] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0057] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0058] In the embodiments of the present application, sometimes the subscript such as W1 is written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0059] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in combination with the drawings and specific embodiments.

[0060] The embodiments of the present application provide an AKI identification method combining biomarkers and image features, as shown in Figure 1 The method comprises the following steps:

[0061] S1: Based on a high-risk population of acute kidney injury, analyze the concentration sequences of serum creatinine, cystatin C and IL-18 in continuous monitoring, calculate the rising and falling directions at each time node, compare the direction consistency of the current sequence and the historical lag mode, screen the fragments with consistent characteristics of sustained direction, judge the stability of the continuous trend in physiological response, and obtain the dynamic trend linkage feature;

[0062] S2: According to the dynamic trend linkage feature, optimize the symmetry axis positioning mode of the renal portal vein path in the gray image, adjust the gradient change trajectory of the axis crossing point, construct a rotation scanning plane with the axis as the core, analyze the feature differences of the kidney region image in gray distribution, texture direction and voxel density, judge whether the plane features are consistent, and obtain the rotation plane dissection feature group;

[0063] S3: According to the rotation plane anatomical feature group, compare the change trend of gray average, texture direction and density distribution in time nodes in the continuous image frames, judge whether the dimension change direction is consistent, analyze the fluctuation amplitude, identify the spatial region with discontinuous direction or feature inversion, judge whether the structure axis positioning appears abnormal deviation, if it meets the spatial deviation standard, get the structure continuity abnormal data;

[0064] S4: According to the structure continuity abnormal data, calculate the motion trajectory of the gray center of the renal cortex region in the image sequence, analyze the coherent direction of the trajectory path in the image plane, compare the continuous change trend of creatinine, urea nitrogen and body fluid related parameters in the target period, judge whether it forms linkage deviation with the image trajectory, screen the time period with corresponding abnormalities, get the multi-modal collaborative deviation mark;

[0065] S5: Based on the multi-modal collaborative deviation mark, compare the lag segment in the dynamic trend linkage feature with the key point sequence in the image abnormal trajectory, screen the time range with overlap, calculate the coverage ratio of the overlapping segment, judge whether it exceeds the linkage recognition reference range, if the time overlap is obvious, determine it as the abnormal stage, get the early collaborative abnormal locking result.

[0066] The dynamic trend linkage feature includes concentration direction symbol, trend sequence number, lag matching mark, the rotation plane anatomical feature group includes symmetry axis gray baseline, rotation plane texture distribution, voxel aggregation degree, the structure continuity abnormal data includes parameter fluctuation boundary, structure deflection dimension, spatial mutation label, the multi-modal collaborative deviation mark includes image translation path, physiological fluctuation trajectory, linkage difference area, the early collaborative abnormal locking result includes abnormal synchronous segment, cross warning section, early warning state information.

[0067] The current sequence refers to the sequence of biomarker concentrations collected by the current subject within the target detection period (such as during postoperative continuous monitoring), including time series data of serum creatinine, cystatin C and IL-18, and equal interval between data, forming comparable trend sequence; the historical lag mode refers to the directional symbol template sequence formed by the fluctuation trajectory of biomarker concentration before the occurrence of kidney injury in the archived high-risk cases of similar acute kidney injury; the template sequence is used to establish a lag response standard as a comparison reference; the characteristic fragment refers to the time sequence fragment in the "current sequence" that has continuous consistency with the "historical lag mode", and the matching condition is that the symbol trend is consistent and the length reaches the linkage recognition requirement, which belongs to the potential early abnormal signal fragment; the symmetry axis positioning method refers to the axis extraction method based on the brightness gradient distribution of the renal portal vein passing area in the kidney grayscale image, which has anatomical structure symmetry reference significance. The method locates the center line of the structure through the maximum variation path of the brightness gradient; the axis crossing point refers to the specific pixel or voxel position crossed by the symmetry axis in the two-dimensional or three-dimensional image, which is usually near the center of the renal portal vein and has the maximum gradient change and structural symmetry significance; the plane feature refers to the set of multi-dimensional structural parameters such as average gray level, texture direction, and voxel density of the kidney region image in the rotation section centered on the axis, and each plane generates a set of features to describe anatomical consistency; the dimension change direction refers to the change trend direction of various image feature dimensions such as gray level, texture direction, and voxel density between consecutive image frames, i.e., whether it is rising, falling, or remaining stable, which is used to judge the stability or mutation trend of feature evolution; the key point sequence refers to the set of image nodes related to abnormal deviation marked in the kidney image track, which usually includes index time points of gray level change extreme points, path turning points, and structure mutation regions, which are used for time sequence coincidence judgment with the lag fragment of biomarkers; the voxel density refers to the compactness or local aggregation degree of gray level distribution of voxels in a unit volume in the kidney region in medical images (such as CT or MRI), which is used to reflect the texture consistency and distribution rule of tissue structure in space.

[0068] As shown in Figure 2 , the acquisition steps of the dynamic trend linkage feature are specifically:

[0069] S101: Based on the high-risk population of acute kidney injury, analyze the monitoring sequence of serum creatinine, cystatin C and IL-18 obtained by continuous monitoring, compare the change direction of the detection results of adjacent time nodes in turn, mark the trend of relative increase or decrease, and get the directional transfer label sequence;

[0070] The actual detection value sequence of serum creatinine, cystatin C and interleukin 18 of the patient at continuous time points is obtained, the sampling frequency should ensure that the interval time is within 12 hours, a time sequence is established for each index, the detection results of adjacent time points are compared item by item, it is judged whether the value at the current time point is greater than that at the previous time point, if the value rises, it is recorded as "rise", if it falls, it is recorded as "fall", taking serum creatinine as an example, if the previous two monitoring values are 89 and 96, it is judged as rising, if the subsequent value is 93, it is judged as falling, and the complete direction sequence can be formed in this way, the same method is used to obtain the direction markers of cystatin C and interleukin 18, the duration span of each change segment needs to be calculated, and a minimum change amplitude threshold is set combining the conventional fluctuation range of the index, only when the detection value changes more than the amplitude, it will be considered as a real direction shift, if the value changes insufficiently, the change at the time point will not be counted in the trend analysis, to avoid misjudging normal fluctuation as pathological change, in implementation, the standard deviation of the same type of people in the past month can be selected as the basis for setting the threshold, for example, if the standard deviation is 8 units, the threshold can be set to 1.2 units, that is, when the value between two time points changes less than 1.2 units, it is not treated as a trend change, the duration of the direction also needs to be clearly defined, for example, it is set that the same direction must appear for three consecutive time points to be considered as a trend segment, if only one or two time points change direction, it is not included in the subsequent trend analysis, in practice, if the serum creatinine values are 85, 87, 89, 88, 90 and 92 in turn, the directions are rising, rising, falling, rising and rising, according to the minimum duration requirement, the middle single point falling is excluded, only two consecutive rising trends are retained, and the direction shift sequence of the three indexes is generated.

[0071] S102: Compare the current time slice in the direction shift marker sequence with the adjacent slice in the historical monitoring sequence, align the direction markers according to the time points, judge the consistency, analyze whether the direction in each corresponding relationship maintains the same trend, and count the proportion of time nodes with consistent direction in the total slice to obtain the time consistency proportion;

[0072] Extract the latest time segment as a control standard, compare it with the same length sequence in the historical monitoring record segment by segment, set a sliding window for the historical sequence, align it with the current segment one by one according to the time point, and judge whether the direction marks of the two sequences are consistent at each position. If the same, it is recorded as matching, if different, it is considered as not matching. The proportion of the number of matching positions in the whole segment length is defined as the consistency ratio. The ratio can be divided into three levels. The ratio is less than four as low consistency, four to seven as medium consistency, and more than seven as high consistency. Taking the actual serum creatinine direction sequence as an example, if the current is the rise, rise, fall, fall, rise of the last five time points, and the history is rise, fall, fall, rise, rise, there are three positions consistent between them, which is considered as medium consistency. After processing each marker respectively, the weight average method of the consistency ratio of the three indexes needs to be set, and the comprehensive consistency ratio is calculated according to the weight average method. The weight of each index can be set according to its reference value in the early prediction in the clinic, for example, serum creatinine can be set as five weight, cystatin C three weight, and interleukin 18 two weight. Repeat the operation in all historical segments to complete the complete statistical result of the consistency ratio between the current time segment and all historical comparison segments.

[0073] S103: Screen the segments with time consistency ratio meeting the basic judgment requirement and the ratio meeting the linkage standard, analyze whether the change direction in the segments meeting the conditions is consistent, output the monitoring time range with common trend direction, and obtain the dynamic trend linkage feature;

[0074] Screen the consistency ratio statistical result, select the segments with ratio higher than seven and duration more than six hours as candidate regions, then verify the direction change in the candidate segments, judge whether the direction remains unchanged in the whole duration, if the direction is reversed in the middle, the segment needs to be excluded, if only one time point is reversed and restored to the original trend and maintained for more than two time points, it is considered to meet the direction continuity requirement, which ensures that the identified trend segment is stable. After executing each marker respectively, integrate the start time and end time of the segments meeting the conditions. If more than half of the time segments of multiple indexes overlap, it is considered as a linkage trend segment. For example, if serum creatinine shows a trend segment from 8am to 2pm, and cystatin C shows a trend segment from 10am to 4pm, the overlapping part is four hours, which accounts for 67% of the shorter time, meeting the integration condition. Integrate the segments meeting the overlap requirement in the three indexes into a unified time segment, and output the time segment as the detection result of the dynamic trend linkage.

[0075] As shown in Figure 3 , the acquisition step of the rotation plane anatomical feature group is specifically:

[0076] S201: According to the dynamic trend linkage feature, the approximate axis distribution of the renal portal region in the gray image is located, the symmetric intensity distribution of the two sides of the renal portal structure in the gray image is analyzed, the center trend of the symmetric axis is optimized, the distribution position of the axis crossing point in the gray gradient change region is adjusted, and symmetric path distribution information is obtained;

[0077] The trend linkage interval identified in the current time period of the patient is determined, the abdominal image sequence corresponding to the interval is called, the approximate spatial region of the renal portal vein in the image is located, the gray level change of the renal portal peripheral tissue and the background region is compared through the image gray level distribution feature, the approximate trend direction of the renal portal vein is preliminarily judged, the renal portal region in the image is divided into two symmetric regions, the corresponding gray value set of each region is extracted, the mean and variance of the gray value of each pixel point in each region are calculated, and the gray mean difference and the symmetric degree of the gray distribution between the two regions are calculated. The symmetric degree is defined as the symmetric region, and the gray distribution error is less than 10% of the total gray dynamic range. If the error is more than 30%, it is determined to be asymmetric. The set axis position is adjusted to offset, the two side regions are redefined after each offset, and the gray mean difference is recalculated. Compare the mean difference trend between the current and previous results. If the change gradually decreases, it means that the optimal axis direction is approached, and the position of the crossing point is continuously fine-tuned. The fine-tuning unit is set to 1 pixel length. The symmetric region gray variance ratio is calculated after each adjustment. If the ratio gradually approaches 1, it means that the symmetric degree is enhanced. The minimum error point is selected as the optimal crossing point of the current image, and the symmetric path axis in the image is precisely distributed and positioned to obtain the symmetric path distribution information of the image frame.

[0078] S202: Call the symmetric path distribution information, construct a set of rotation planes with the axis as the reference, analyze the lateral variation amplitude of the gray level distribution in the kidney region in each plane, calculate the dynamic distribution sequence of the gray difference on both sides of the axis under multiple angle rotations, judge whether the gray feature remains in a continuous transition state during rotation, and obtain the angle gray variation trend;

[0079] A set of rotation planes is established with the axis as the center, the rotation planes are arranged at equal angle intervals with the axis as the reference, the rotation angle interval is set to 5 degrees, and a total of 36 rotation angle planes are generated from 0 degrees to 180 degrees. Each rotation plane intercepts a respective symmetrical kidney region image region on the left and right of the axis. The gray value mean of each pixel row in the transverse direction of each plane is extracted and arranged in sequence along the transverse direction. The gray value mean difference between the left and right sides in each plane is calculated, and a gray value difference sequence is generated in the order of the 36 angles. In the sequence, the change amount of the gray value difference between adjacent angles is compared in sequence. If the change amplitude is less than 5% of the full gray value range of the image, it is considered to be a continuous transition. If the change amplitude exceeds 15% of the image gray value dynamic range, it is considered to be discontinuous. On this basis, the number of angles with gray value mutations is counted. If it exceeds one-third of the total number of angles, it is determined that the image has a discontinuous feature in the current rotation state. The corresponding rotation plane is marked as a non-continuous plane. Otherwise, it is recorded as a continuous plane. The gray value difference change results of all rotation planes are summarized and sorted by angle to obtain the angle gray value change trend.

[0080] S203: According to the angle gray value change trend, the arrangement direction of the kidney region texture in each plane is compared with the spatial position relationship of the voxel aggregation region in the image. It is judged whether the structure distribution in the multi-angle image section has a common feature. The spatial combination of gray value, texture and density is integrated to obtain a rotation plane anatomical feature group.

[0081] According to the angle gray value change trend, the texture structure features inside the kidney region in each rotation plane are extracted to obtain the main direction of the texture arrangement. By traversing the local region texture in each rotation angle plane, the main direction vector of the texture in each region is extracted. The included angle distribution between the texture direction vector and the rotation angle is calculated. If the included angle between the texture direction and the rotation direction in most planes remains within ±15 degrees, it is determined that the direction consistency is strong. Meanwhile, in combination with the positioning information of the voxel aggregation region in the image, the connected region with a gray value higher than the average of the whole image in each plane is extracted as the voxel aggregation region. The center point coordinates are calculated and positioned in the three-dimensional coordinate system. If the voxel aggregation center points in most rotation planes are distributed symmetrically on both sides of the axis or the distribution radius changes less than 5 pixels, it is determined that the spatial aggregation is consistent. On this basis, the gray value features, texture directions and voxel distribution are combined. If more than 70% of the planes have consistent directions, approximate distribution centers and continuous gray value changes, the rotation plane with structural consistency is recorded. All rotation planes that meet the conditions are combined to obtain a rotation plane anatomical feature group.

[0082] As shown in Figure 4 , the steps of obtaining the structural continuity abnormality data are specifically:

[0083] S301: According to the rotation plane anatomical feature group, analyze the gray scale, texture direction and density change trend of the continuous image frames in the kidney area range, compare whether the spatial direction is consistent between time nodes, identify whether there is a direction reversal or offset feature, and obtain a joint direction change trajectory sequence;

[0084] Call the labeled kidney image area in each rotation angle plane, extract the kidney gray mean value, main texture direction and local density aggregation degree of each frame under the corresponding rotation angle in the continuous image frames, by traversing each frame image kidney pixel block, performing horizontal and vertical statistical calculation on the gray distribution, recording the mean value change trajectory in the same area, calculating the angle value corresponding to the main texture direction in each image frame, comparing the change amount of the texture direction angle of the previous and next frames, and extracting the center point position of the highest density area to judge whether the spatial aggregation area moves, the extraction unit of all indicators is set to every 5 frames, in the continuous time nodes, the change direction of gray mean value, texture direction angle and voxel aggregation center position is recorded, whether it changes with time is judged, if the change direction of a certain index is opposite to the previous time point, it is considered as direction reversal, if the previous time point is upward trend and the current direction is downward trend, it is considered as reversal, if the spatial change path of the index deviates from the original direction more than 5 pixels of the original path, it is considered as direction deviation, all indexes with reversal or deviation events will be marked as direction mutation, and the change type and time index value when the event occurs will be recorded, the mutation events of gray, texture and density three types of indexes will be sorted into joint direction change trajectory sequence in time sequence.

[0085] S302: Call the joint direction change trajectory sequence, filter the image segments with discontinuous direction on the gray, texture and density path, judge whether the position offset in the corresponding image frame is concentrated in the target area, mark the corresponding image coordinate section and its position distribution, and obtain the local space disturbance area.

[0086] The image frames marked as having direction reversal or deviation are screened, the corresponding image position coordinates in each frame are extracted, the distribution area of the pixel points with gray, texture and density direction mutation on the image is coordinate labeled according to the frame kidney area mask boundary, the spatial aggregation degree of the mutation pixel points is counted, the distance from the mutation point coordinate center to the kidney center point is calculated, if it is greater than 30% of the kidney radius, it is considered as edge mutation, otherwise it is center aggregation mutation, if more than 70% of the mutation points in the same image frame are concentrated in the center area, it is determined as the target area disturbance concentration feature, the image frame number is recorded, and the mutation point coordinate range is circumscribed to the minimum rectangle, and the pixel coordinates of the upper left corner and the lower right corner are labeled to describe the two-dimensional boundary range of the disturbance area, if there are overlapping disturbance areas in more than two consecutive image frames, it is recorded as a continuous disturbance interval, and the gray range, texture direction change range and density change slope of the disturbance area in each frame image are extracted as additional data for further analysis, and the spatial abnormal coordinate information and additional statistical features of each frame are integrated to obtain the local spatial disturbance area.

[0087] S303: According to the local spatial disturbance area, it is judged whether the structure axis has trajectory anomaly in the target area, whether the deviation behavior exceeds the spatial tolerance range is analyzed, and the structure deviation distribution range and deviation condition are tested to obtain structure continuity anomaly data;

[0088] Whether the deviation behavior exceeds the spatial tolerance range is analyzed by using the formula:

[0089] ;

[0090] Calculate the deviation characteristic value , judge whether the structure axis has trajectory anomaly in the target area, wherein, represents the number of structure control points, represents the actual measured coordinates of the th structure control point in space, represents the design coordinates of the th structure control point in space, represents the mean value of the deviation of all structure control points, represents the tolerance threshold of structure deviation.

[0091] Let the number of continuous frames in the image sequence be , in order to save image processing time, the symmetrical gray axis points after plane scanning are taken out , and the image gray reference coordinates at time point T0 are set as , the reference point is calculated by the center of the initial frame renal portal vein area, and the value is obtained by extracting the gray weighted center of the renal cortex area after image segmentation.

[0092] Gray scale coordinate quantization adopts 0-255 standard range, center point is calculated by contour barycenter and gradient field, data as follows:

[0093] , , , , , ;

[0094] Refer to time point ;

[0095] Perform gray scale offset item calculation:

[0096] ;

[0097] ;

[0098] Substitute average value item:

[0099] ;

[0100] Rotation plane slice fluctuation parameter calculation as follows:

[0101] From each frame, extract the spatial standard deviation of the gradient change of the rotation cross-section texture direction angle, and calculate the following offset (unit: mm) after the main direction difference of the texture direction tensor transformation is normalized:

[0102] , , , , , ;

[0103] Find the mean value:

[0104] ;

[0105] Set the symmetry axis smooth threshold value , which is the maximum tolerance of the average symmetry variation limit in the medical image literature through the texture gradient direction distribution mean value extracted in the standard renal portal cross-sectional image sample

[0106] Substitute the formula:

[0107] ;

[0108] The results show that the gray scale change in the structure continuous image deviates greatly from the rotation plane texture density change, and the value is greater than the normal range The control line indicates that there is a significant deviation in the positioning of the renal portal symmetry axis in consecutive frames, and this value is directly used as the basis for outputting structural continuity abnormal data and participating in subsequent calculations.

[0109] As shown in Figure 5 the acquisition step of the multi-modal collaborative offset marker is specifically:

[0110] S401: According to the structural continuity abnormal data, analyze the gray center of gravity coordinate distribution of the renal cortex region in consecutive image frames, calculate the horizontal and vertical translation path of the center of gravity position between each image frame, judge whether the path forms a forward arrangement or appears a position jump in the image plane, and obtain the image sequence displacement trend;

[0111] First, determine the specific frame sequence number of the abnormal region in consecutive image frames and the mask boundary of the renal cortex region, intercept the image region where the renal cortex is located in each frame, and calculate the gray weighted average position of all pixel points in the region to obtain the gray center of gravity coordinates corresponding to the frame. Record this coordinate as a position point in the two-dimensional image plane, and perform this operation on all image frames in time sequence to form a time sequence coordinate set of the gray center of gravity. Calculate the distance difference of the center of gravity coordinates between adjacent frames on the horizontal and vertical axes, and record the translation direction of the center of gravity of each frame relative to the previous frame. If the center of gravity coordinates of the current frame and the previous frame change to positive in the horizontal direction and the right movement is continuous for three frames, it is judged as forward right movement. If the center of gravity suddenly jumps from right to left in a certain frame, it is recorded as a horizontal jump. If the same direction reversal appears in the vertical movement trend, it is also recorded as a vertical jump. The jump definition standard is that the translation direction of the current frame is opposite to the direction of the previous two frames, and the single frame moving distance is more than twice the average value of the previous three frames. Taking the actual image data as an example, if the gray center of gravity Y coordinates of frame 1 to frame 3 are 200, 204, and 208 respectively, and frame 4 is 196, frame 4 has a direction reversal compared with frame 3 and a movement amplitude of 12 pixels, which is much larger than twice the average value of 4 pixels, which meets the jump standard. Finally, all the jump frames and continuous translation frames are grouped and recorded to generate the movement path of the image frame center of gravity in the image space, and the time start and end frame numbers of each consistent direction segment and jump segment are identified to obtain the image sequence displacement trend.

[0112] S402: Based on the image sequence displacement trend, compare the numerical change direction of creatinine, urea nitrogen and body fluid parameters in the same time period, judge whether the continuous trend of the parameter sequence deviates from the direction of the image trajectory, identify the combination items that form inconsistent trends, and obtain the parameter trajectory offset coefficient;

[0113] The formula for judging whether the continuous trend of the parameter sequence deviates from the direction of the image trajectory is:

[0114] ;

[0115] Identify the combination of items that form inconsistent trends, get the parameter trajectory offset coefficient , wherein represent the first instant of creatinine, urea nitrogen or body fluid parameters and its previous time difference, represent the first frame image in the direction of the change value of the gray center trajectory in the main shaft, represent the first instant of the direction vector change value, represent the first instant of physiological parameter value and its previous three consecutive time physiological parameter average value, table the first frame of the standard deviation of the gray scale distribution in the renal cortex region of the rotating plane image, is the total number of image frames in the selected image time period, each frame corresponds to a sampling time, so substantially represent the corresponding relationship between frame and time, for example, frame represent time 1;

[0116] Set the number of image sequence analysis frames to 3 frames, i.e. , each parameter is obtained by the following method:

[0117] is the difference value of creatinine concentration at the first frame corresponding time compared with the previous time, the creatinine value detected by high frequency biochemical automatic analyzer is 1.0mg / dL for the first frame, 1.15mg / dL for the second frame, and 1.3mg / dL for the third frame, then:

[0118] The difference value of the first frame is 0, the difference value of the second frame is 1.15-1.0=0.15, and the difference value of the third frame is 1.3-1.15=0.15;

[0119] is the difference value of the projection displacement of the gray center of the continuous frame in the main shaft direction, the center displacement value is obtained by fitting the main shaft of the image sequence and analyzing, which is 0 for the first frame, 2 pixels for the second frame, and 3 pixels for the third frame, then:

[0120] The difference value of the first frame is 0, the difference value of the second frame is 2-0=2, and the difference value of the third frame is 3-2=1;

[0121] is the change rate of the rotating main shaft direction in the image sequence, which is obtained according to the change rate of the angle between the main shaft of the renal portal vein, and after unit normalization, it is as follows:

[0122] The difference value of the first frame is 0.2, the difference value of the second frame is 0.3, and the difference value of the third frame is 0.25;

[0123] The deviation of the physiological value at the current time and the average value of the previous three frames is calculated by the sequence mean value:

[0124] There is no data before the first frame, which is recorded as 0. The second frame: 1.15-average (1.0)=0.15, the third frame: 1.3-average (1.0, 1.15)=1.3-1.075=0.225;

[0125] The gray standard deviation is calculated as the fluctuation range of the pixel intensity distribution in the renal cortex region, and the result is:

[0126] The first frame is 0.30, the second frame is 0.35, and the third frame is 0.40;

[0127] Each frame item is calculated respectively:

[0128] The first frame: ;

[0129] The second frame: ;

[0130] The third frame: ;

[0131] The total calculation is:

[0132] ;

[0133] The results show that there is a moderate trend deviation between the current monitored three frames of images and their corresponding creatinine change trend, and the deviation coefficient is 0.2075. This value indicates that there is a synchronous deviation between the physiological and image linkage trend, which meets the linkage abnormality screening condition and can be included in the multi-modal deviation time period marker calculation.

[0134] S403: Based on the parameter trajectory deviation coefficient, screen the time sequence fragments of linkage abnormalities in the deviation coefficient fluctuation section, judge whether the fragments form a co-occurrence structure between image space and detection parameters, and identify the spatial coordinates and data intervals of this type of period, to obtain multi-modal collaborative deviation markers;

[0135] In time sequence analysis of the change curve, the position segment of sudden increase or sudden decrease of the offset coefficient is identified, the time segment in which the coefficient change exceeds the average of the previous segment by more than 20% is extracted, and the time segment is determined as a fluctuation segment. In the fluctuation segment, the image displacement trajectory in the corresponding image frame is cross-compared with the parameter direction consistency, and the segment in which the direction offset occurs simultaneously in the image space and the parameter fluctuation is screened. If the image gray center jumps and the parameter offset coefficient is in a high fluctuation state at the same time, the time segment is defined as a linkage abnormal segment. Further, the center coordinates of the corresponding frame in the image are extracted in the time segment, and the motion path formed by the coordinates in the two-dimensional plane is recorded. The frame number corresponding to the sudden change point in the path is marked as a spatial abnormal frame. At the same time, the creatinine, urea nitrogen and body fluid values in the parameter sequence corresponding to the frame time point are extracted, which are marked together with the values at the previous and next time points as a parameter abnormal segment. The image coordinate range of all image jump frames and the parameter abnormal value interval are uniformly corresponding, and are identified as a group of multi-modal linkage abnormal segments. The start and end time, image frame number, image coordinate center point and corresponding creatinine parameter change value of each linkage abnormality are output, and the multi-modal collaborative offset marker is obtained.

[0136] As shown in Figure 6 , the acquisition step of the early collaborative abnormality locking result is specifically:

[0137] S501: Based on the multi-modal collaborative offset marker, analyze the positioning information of each time segment, compare the time tags with the time indexes of the lagging segments and image key points in the dynamic trend linkage feature, screen the segment range that occurs in the time dimension, and obtain the synchronous overlapping time period index set;

[0138] Extract the time period corresponding to each group of linkage abnormality markers, record the start and end frame numbers, and read the coordinate index information in the image space of each abnormal segment. Organize the time tags into a list, and one-by-one compare them with the time tags of the lagging segments in the dynamic trend linkage feature. Merge all overlapping time intervals, and determine whether each linkage abnormality time period is included in the lagging segment time range. If partially overlapping, record the segment as an overlapping candidate. Index extract the image key point time sequence, obtain the time stamp when all key point events occur, and compare whether there is a time point intersection with the aforementioned candidate overlapping time period. If the image key point and the collaborative offset segment are completely consistent at a certain time frame, it is recorded as an accurate overlap. If the image key point is located between the start and end frames of the collaborative offset time period, it is recorded as a range overlap. Finally, all time segments with any of the above intersection forms are summarized to generate a complete synchronous overlapping time period index set, which contains the matching results of linkage abnormality time period index, lagging trend time period index and image key point time tag, and the items in the set are arranged in ascending order by time frame number. The synchronous overlapping time period index set is obtained.

[0139] S502: Call the synchronization overlap period index set, calculate the coverage of the overlapping segment in the image key point sequence, judge whether the coverage ratio reaches the linkage recognition reference range, recognize the segment sequence with strong continuity and mark the segment boundary index, and obtain the overlapping segment coverage ratio;

[0140] Perform coverage analysis operation on each synchronization segment, extract the time label of all key points from the image key point sequence, compare the start and end time frames of the current overlap segment, count the number of key points appearing in the segment, divide the number by the length of the overlap segment, calculate the key point coverage ratio, if the coverage ratio exceeds 50%, the overlap segment is marked as a high coverage segment, if it is less than 30%, it is marked as a low coverage segment, further continuity detection is performed on all high coverage segments to judge whether the frame index in the image sequence is continuous, if the start and end frames of adjacent high coverage segments are separated by no more than two frames, they can be considered as continuous and merged into a longer segment, record the new start and end index, and at the same time, count the total number of key points in the merged segment and the proportion of the image key point sequence, if the proportion exceeds 60% of the total number of image key point sequence, it is marked as a key segment, and the start and end frame number, segment length, key point number and coverage ratio of all key segments are marked, and the overlapping segment coverage ratio data table is generated.

[0141] S503: According to the overlapping segment coverage ratio, judge whether there is a centralized deviation in the linkage range between the image trajectory and the physiological index, filter the time nodes showing consistent linkage response in the continuous segment, and integrate the time range and key feature annotation information to obtain the early cooperative abnormality locking result;

[0142] The linkage between the image trajectory and the physiological index is judged, the image gravity trajectory, texture direction change sequence and density change sequence of each high coverage key segment are read, and the direction change sequence of creatinine, urea nitrogen and body fluid parameters corresponding to the same time range is extracted, the dominant direction trend of the image index is matched with the direction trend of each physiological parameter, if the image and any two parameters are consistent in direction, it is recorded as a consistent linkage point, if all three directions are consistent, it is recorded as a strong consistent linkage point, the proportion of consistent linkage points in each key segment is calculated, if the proportion exceeds 70% of the total frame number, it is marked as a high consistent segment, and the distribution of consistent points between continuous time frames in all high consistent segments is screened, if there are consistent points for more than five frames, it is marked as a centralized linkage segment, the start and end frame number, image space gravity coordinates and corresponding parameter value of the centralized linkage segment are written into the integration table, and the center point and feature point position of the image corresponding to the time frame are marked on the key point image, the structured result composed of image time segment range, key point coordinates and parameter value combination of multiple linkage indicators is output, and the early cooperative abnormality locking result is obtained.

[0143] As Figure 7The AKI recognition system combined with the biomarker and the image feature includes:

[0144] The trend linkage recognition module analyzes creatinine, cystatin C and IL-18 sequences based on the high-risk population of acute kidney injury, calculates a change direction, matches a lag mode, screens consistent trend segments, judges physiological trend continuity, and obtains a dynamic trend linkage feature.

[0145] The symmetrical structure extraction module optimizes a renal portal vein path symmetry axis according to the dynamic trend linkage feature, constructs a rotation plane, analyzes differences in gray scale, texture and density, judges structure consistency, and obtains a rotation plane anatomical feature group.

[0146] The image variation detection module compares gray scale, texture and density changes between image frames according to the rotation plane anatomical feature group, judges direction consistency, analyzes fluctuation amplitude, identifies mutation regions and offset trends, and obtains structure continuity abnormal data.

[0147] The linkage offset calculation module calculates a renal cortex gray scale trajectory according to the structure continuity abnormal data, analyzes a moving direction, compares creatinine, urea nitrogen and body fluid trends, judges linkage deviation, screens corresponding abnormal segments, and obtains a multi-modal collaborative offset marker.

[0148] The abnormal timing locking module compares lag segments and image trajectory key nodes based on the multi-modal collaborative offset marker, screens a time overlap range, calculates a coverage ratio, judges whether the recognition condition is met, and obtains an early collaborative abnormal locking result.

[0149] It should be understood that the term "and / or" herein is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship, which can be understood according to the context before and after.

[0150] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0151] It should be understood that the size of the sequence number of the above processes does not mean the order of execution in various embodiments of the present application, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0152] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described devices, apparatuses and units can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0154] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the above-described device embodiments are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0156] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0157] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0158] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An AKI identification method combining biomarkers and image features, characterized in that, The method includes: S1: Based on high-risk populations for acute kidney injury, we analyzed the sequences of creatinine, cystatin C, and IL-18, calculated the direction of change, matched hysteresis patterns, screened fragments with consistent trends, judged the continuity of physiological trends, and obtained dynamic trend linkage characteristics. S2: Based on the dynamic trend linkage characteristics, optimize the symmetry axis of the renal portal vein path, construct a rotational plane, analyze the differences in grayscale, texture and density, judge the structural consistency, and obtain the anatomical feature group of the rotational plane. S3: Based on the rotating plane anatomical feature group, compare the changes in grayscale, texture and density between image frames, determine the consistency of direction, analyze the fluctuation amplitude, identify abrupt change regions and offset trends, and obtain structural continuity anomaly data; S4: Based on the structural continuity anomaly data, calculate the grayscale trajectory of the renal cortex, analyze the direction of movement, compare creatinine and urea nitrogen, determine the linkage deviation, screen the corresponding abnormal segments, and obtain the multimodal collaborative offset marker. S5: Based on the multimodal collaborative offset marker, compare the lagging segments with key nodes of the image trajectory, filter the time overlap range, calculate the coverage ratio, determine whether the recognition conditions are met, and obtain the early collaborative anomaly locking result.

2. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The dynamic trend linkage features include concentration direction symbols, trend sequence numbers, and lag matching identifiers; the rotational plane anatomical feature group includes symmetry axis grayscale baseline, rotational surface texture distribution, and voxel aggregation degree; the structural continuity anomaly data includes parameter fluctuation boundaries, structural deflection dimensions, and spatial mutation labels; the multimodal collaborative migration markers include image translation paths, physiological fluctuation trajectories, and linkage difference regions; and the early collaborative anomaly locking results include anomaly synchronization segments, cross-warning segments, and early warning status information.

3. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The specific steps for obtaining the dynamic trend linkage feature are as follows: S101: Based on high-risk populations for acute kidney injury, the serum creatinine, cystatin C and IL-18 monitoring sequences obtained by continuous monitoring were analyzed. The direction of change of the test results at adjacent time points was compared in turn, and the trend of relative increase or decrease was marked to obtain the direction transfer marker sequence. S102: Compare the current time segment in the direction transfer mark sequence with the adjacent segments in the historical monitoring sequence, align the direction marks according to the time points, make a consistency judgment, analyze whether the directions in each pair of corresponding relationships maintain the same trend, and count the proportion of time nodes with consistent directions in the total segments to obtain the time consistency ratio. S103: Filter the segments whose duration meets the basic judgment requirements and whose proportion meets the linkage standard among the time consistency ratios, analyze whether the change direction of the segments that meet the conditions is consistent, output the monitoring time range with common trend direction, and obtain the dynamic trend linkage characteristics.

4. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The specific steps for obtaining the rotational plane anatomical feature set are as follows: S201: Based on the dynamic trend linkage characteristics, locate the approximate axis distribution of the renal portal vein region in the grayscale image, analyze the symmetrical intensity distribution of the regions on both sides of the renal hilum structure in the grayscale image, optimize the central direction of the symmetrical axis, adjust the distribution position of the axis crossing point in the grayscale gradient change region, and obtain the symmetrical path distribution information. S202: Call the symmetrical path distribution information to construct a set of rotating planes based on the axis, analyze the lateral variation of the gray distribution of the kidney region in each plane, calculate the dynamic distribution sequence of the gray difference on both sides of the axis under multi-angle rotation, determine whether the gray features maintain a continuous transition state during the rotation process, and obtain the angle gray change trend. S203: Based on the gray-scale change trend of the angle, compare the arrangement direction of the kidney texture in each plane with the spatial position relationship of the voxel aggregation area in the image, determine whether there are common features in the structural distribution in the multi-angle image cross-section, integrate the spatial combination of gray-scale, texture and density, and obtain the rotating plane anatomical feature group.

5. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The specific steps for obtaining the structural continuity anomaly data are as follows: S301: Based on the rotating plane anatomical feature group, analyze the grayscale, texture direction and density change trends of continuous image frames within the kidney region, compare whether the spatial orientation between time nodes is consistent, identify whether there are direction reversal or offset features, and obtain a joint change trajectory sequence. S302: Call the joint directional trajectory sequence, filter image segments with discontinuous directions on grayscale, texture and density paths, determine whether the positional offset in the corresponding image frame is concentrated in the target area, mark the corresponding image coordinate segment and its positional distribution, and obtain the local spatial disturbance area. S303: Based on the local spatial disturbance area, determine whether there is an abnormal trajectory of the structural axis within the target area, analyze whether the offset behavior exceeds the spatial tolerance range, and verify the structural offset distribution range and deviation conditions to obtain structural continuity anomaly data.

6. The AKI identification method combining biomarkers and image features according to claim 5, characterized in that, The analysis of whether the offset behavior exceeds the spatial tolerance range is performed using the following formula: ； Calculate the offset eigenvalue To determine whether there are trajectory anomalies of the structural axis within the target area, This represents the number of structural control points. Representing the Symmetrical grayscale axis points of structural control points in space Representing the The gray-level reference coordinates of each structural control point in space are calculated from the center of the renal portal vein region in the initial frame. The values ​​are obtained by extracting the gray-level weighted center of the renal cortex region after image segmentation. This represents the mean offset of all structural control points. Represents the symmetry axis stability threshold, which is the maximum tolerance of the mean distribution of texture gradient directions extracted from standard renal hilum cross-sectional image samples.

7. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The specific steps for obtaining the multimodal cooperative offset marker are as follows: S401: Based on the structural continuity anomaly data, analyze the gray-level centroid coordinate distribution of the renal cortex region in consecutive image frames, calculate the horizontal and vertical translation paths of the centroid position between each image frame, determine whether the paths form a forward arrangement or a position jump in the image plane, and obtain the displacement trend of the image sequence. S402: Based on the displacement trend of the image sequence, compare the direction of change of creatinine and urea nitrogen values ​​in the same time period, determine whether the continuous trend of the parameter sequence deviates from the direction of the image trajectory, identify the combination terms with inconsistent trends, and obtain the parameter trajectory offset coefficient. S403: Based on the parameter trajectory offset coefficient, filter out time segments with linkage anomalies in the deviation coefficient fluctuation range, determine whether the time segments form a co-occurrence structure between the image space and the detection parameters, and identify the spatial coordinates and data intervals of such time segments to obtain multimodal collaborative offset markers.

8. The AKI identification method combining biomarkers and image features according to claim 1, characterized in that, The specific steps for obtaining the early collaborative anomaly locking results are as follows: S501: Based on the multimodal collaborative offset marker, analyze the positioning information of each time period, compare its time label with the time index of the lagging segment and image key point in the dynamic trend linkage feature, filter the segment range that intersects in the time dimension, and obtain the synchronous overlapping time period index set. S502: Call the synchronous overlapping time period index set, calculate the coverage degree of overlapping segments in the image key point sequence, determine whether the coverage ratio reaches the linkage recognition benchmark range, identify the segment sequence with strong continuity, mark the segment boundary index, and obtain the overlapping segment coverage ratio. S503: Based on the overlapping segment coverage ratio, determine whether there is a concentrated shift in the linkage range between the image trajectory and physiological indicators, filter out time nodes that show consistent linkage response within continuous segments, and integrate time range and key feature annotation information to obtain early collaborative anomaly locking results.

9. An AKI identification system combining biomarkers and image features, said system being used to implement the AKI identification method combining biomarkers and image features as described in any one of claims 1-8, characterized in that, The system includes: The trend linkage identification module is based on high-risk groups of acute kidney injury. It analyzes the sequences of creatinine, cystatin C and IL-18, calculates the direction of change, matches the lag pattern, screens fragments with consistent trends, judges the continuity of physiological trends, and obtains dynamic trend linkage features. The symmetry structure extraction module optimizes the symmetry axis of the renal portal vein path based on the dynamic trend linkage features, constructs a rotation plane, analyzes the differences in grayscale, texture and density, judges the structural consistency, and obtains the rotation plane anatomical feature group. The image variation detection module compares the changes in grayscale, texture and density between image frames based on the rotating plane anatomical feature group, determines the consistency of direction, analyzes the fluctuation amplitude, identifies abrupt change regions and offset trends, and obtains structural continuity anomaly data. The linkage offset calculation module calculates the gray trajectory of the renal cortex based on the structural continuity anomaly data, analyzes the direction of movement, compares creatinine and urea nitrogen, judges the linkage deviation, filters the corresponding abnormal segments, and obtains multimodal collaborative offset markers. The abnormal timing locking module, based on the multimodal collaborative offset marker, compares the lagging segments with key nodes of the image trajectory, filters the time overlap range, calculates the coverage ratio, determines whether the recognition conditions are met, and obtains the early collaborative abnormal locking result.

Images