Patent Application
20200221971
The assessment of damaged tissue by burn or other cause has been done visually. Surgical intervention and debridement are often the treatment of choice. The visual presentation of tissue obscures the extent and condition of tissue. The underlying condition of damaged tissue whether dead, impaired or healthy is not readily determined visually.
The technical problem is to take noisy dielectric data from probes to assess tissue damage. The solution is cluster data preferably using a dimensionless machine learning algorithm. There is no practical way to map such data to show tissue damage in near real time without machine learning.
The preferred embodiment is to take a density clustering algorithm, such as Density based Spatial Clustering of Application with Noise (DBSCAN), and to train it with data from burn patients. The algorithm clusters the data and determines the boundary of data point clusters. By training the algorithm and adjusting criteria such as distance measure, distance between data points, the minimum data points constituting a cluster and data density. Clusters of healthy tissue and damaged tissue emerges. Data density is also measured by the maximum distance between data points. The algorithm is validated with further validation data. The clusters are then mapped onto two and three dimensions. A map of clustered data can be displayed to guide surgeons and other healthcare professionals in the treatment of damaged tissue.
The state of healthy, damaged and dead tissue can be determined from the dielectric response from far field probes. The technical problem is to take noisy permittivity data and identify the state of the tissue mapping it in 2 and 3 dimensions. The technical solution is to use machine learning algorithms to cluster the data points that are relevant to tissue assessment and to ignore the noise datapoints. There are multiple clustering algorithms. The preferred algorithm is a DBSCAN because it does not require knowledge of the number of clusters and is dimensionless. The algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) is designed to discover the clusters and noise in a database according to definitions. DBSCAN calculates values by defining the neighborhood of a Point P. A naive approach could require for each point in a cluster that there are at least a minimum number of points (MinPts) the neighborhood of that point. Ideally, the parameters of epsilon (nearness) and MinPts would be known for each cluster and at least one point from the respective cluster. Using these parameters, one could retrieve all points that are density-reachable from the given point using the correct parameters. There is no simple way to know this for all clusters of the data set. However, there is a simple and effective heuristic to determine epsilon and MinPts of the least dense cluster in the data set by using a global value for each parameter for all clusters. The density parameters of the least dense cluster are used for these global parameter values specifying the lowest density which is not considered to be noise.
Pseudo Code for an implementation of a clustering algorithm is as follows:
Where the SetOfPoints is the set of points of the received dielectric response data, Eps is epsilon, measure of nearness, and MinPts is the minimum number of points in a cluster.
The most important function used by DBSCAN is ExpandCluster which is presented below:
When looking at the sample sets of points depicted in
The differing dielectric response identifies differing tissue states (types). In this case the cell health of nominal and stressed cells.
The system 100,
The distance measure is the manner that distance between data points are measured. Different measures are more appropriate for different tissue geometries. Preferably, the measure is cartesian along a Euclidean line or polar along a curve.
However, other measures such as hyperbolic curves would be more appropriate for certain geometries.
The distance measure portion applies the measure to the data and the measure and resulting data point distance are retained in memory and passed on to a minimum points layer 120.
The minimum points layer 120 has a minimum points measure that defines the minimum number of points that define a cluster. The minimum points measure is applied to the distance measured data points. The minimum points measure, and data points are retained in memory. This is passed to a maximum distance (nearness) layer 130 for define the boundary points of the resulting clusters.
The system begins with a selected measure, minimum points measure, and maximum distance (nearness) measure. The system varies the minimum points and nearness parameters preferably where the clustering separation is stable. However, the varying of parameters for a selected number of variances, varying only one of the minimum point or nearness parameters or a combination of these would also be within the denoising system.
The system is embodied vary the minimum points parameter and nearness parameters. The minimum points parameter can be held constant while varying the nearness parameter. The nearness parameter can be held constant while varying the minimum points parameter. Both parameters can be varied as well.
Once the clustering is completed, the data points not in the clusters are noise and are removed and outputted 60. The data can be mapped onto n-dimensions preferably two or three dimensions.
Data is processed by clustering the data according to similar data points.
The method is embodied vary the minimum points parameter and nearness parameters. The minimum points parameter can be held constant while varying the nearness parameter. The nearness parameter can be held constant while varying the minimum points parameter. Both parameters can be varied as well. The parameters are varied preferably until the separation between clusters is stable. However, the number of variations of parameters until the separation is maximized or after a selected number of variations.
The data points that are not within a cluster are defined as noise and are removed 250 and the result is out put as denoised cluster data. The data can be mapped onto n-dimensions preferably two or three dimensions.
The de-noised data can be used to guide medical treatment of tissue that is not visibly damaged, or the damage is concealed. The damaged or in some way different tissue can be assessed and removed. The clustered data points that are denoised can be displayed for surgeons and medical professional or could be used for robotic surgery.
Although specific advantages have been enumerated above, various embodiments may include some, none, or all of the enumerated advantages. Other technical advantages may become readily apparent to one of ordinary skill in the art after review of the following figures and description.
It should be understood at the outset that, although exemplary embodiments are illustrated in the figures and described below, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. The present disclosure should in no way be limited to the exemplary implementations and techniques illustrated in the drawings and described below.
Unless otherwise specifically noted, articles depicted in the drawings are not necessarily drawn to scale.
Modifications, additions, or omissions may be made to the systems, apparatuses, and methods described herein without departing from the scope of the disclosure. For example, the components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses disclosed herein may be performed by more, fewer, or other components and the methods described may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.
To aid the Patent Office and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants wish to note that they do not intend any of the appended claims or claim elements to invoke 35 U.S.C. 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim.
