Point of care diagnostic systems

• US 20060008923A1
• Filed: 09/02/2005
• Published: 01/12/2006
• Est. Priority Date: 02/03/1998
• Status: Abandoned Application

• Current Assignee: Cytyc Corporation (Hologic Incorporated)
• Original Assignee: Cytyc Corporation (Hologic Incorporated)
• Inventors: Hussa, Robert, Anderson, Emory V., Nemec, Edward, Lapointe, Jerome P., Martinez, Ricardo R., Desieno, Duane
• Application Number:
  US11/218,807
• Publication Number:
  US 20060008923A1
• Time in Patent Office: Days
• US Class Current: 436/518
• CPC Class Codes: G01N 21/474 Details of optical heads therefor, e.g. using optical fibres
• CPC Class Codes: G01N 21/8483 Investigating reagent band test-element handling not specific to a test method G01N33/4875; analytical elements specific to chemical analysis of biological material G01N33/52; autometer with reagent band G01N35/04
• CPC Class Codes: G01N 2333/78 Connective tissue peptides, e.g. collagen, elastin, laminin, fibronectin, vitronectin, cold insoluble globulin [CIG]
• CPC Class Codes: G01N 2800/368 Pregnancy complicated by disease or abnormalities of pregnancy, e.g. preeclampsia, preterm labour
• CPC Class Codes: G01N 33/54388 based on lateral flow
• CPC Class Codes: G01N 33/558 using diffusion or migration of antigen or antibody immunochromatographic test strips G01N33/54387
• CPC Class Codes: G01N 33/6887 from muscle, cartilage or connective tissue
• CPC Class Codes: G01N 33/689 related to pregnancy or the gonads
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y10S 436/811 Test for named disease, body condition or organ function
• CPC Class Codes: Y10S 436/814 Pregnancy

Abstract

Systems and methods for medical diagnosis or risk assessment for a patient are provided. These systems and methods are designed to be employed at the point of care, such as in emergency rooms and operating rooms, or in any situation in which a rapid and accurate result is desired. The systems and methods process patient data, particularly data from point of care diagnostic tests or assays, including immunoassays, electrocardiograms, X-rays and other such tests, and provide an indication of a medical condition or risk or absence thereof. The systems include an instrument for reading or evaluating the test data and software for converting the data into diagnostic or risk assessment information.

Claims

1. A method of classifying an image, comprising:

reducing the image to a set of derived parameters that can be used to reconstruct the image within a specified degree of tolerance;

inputting the derived parameters into a classification means; and

determining the classification of the image based on the output of the classification means.

2. The method of claim 1, wherein the classification means is a neural network.

3. The method of claim 1, wherein the means of reducing the image to a set of derived parameters comprises:

defining a mathematical function containing a plurality of parameters representative of the image; and

optimizing the parameters of the mathematical function using a numerical technique that minimizes the error between the image and a reconstruction of the image using the mathematical function.

4. The method of claim 1, wherein the means of reducing the image to a set of derived parameters comprises:

inputting the image to a trained neural network, wherein the inputs to the network represent the image, the network comprises a hidden layer where the number of hidden elements is smaller than the number of inputs to the network, and the outputs of the network represent the reconstruction of the image; and

setting the derived parameters to the output values of the trained neural network.

5. The method of claim 1, wherein the means of reducing the image to a set of derived parameters comprises:

defining a neural network wherein the inputs to the network are the coordinates of a point in the image, a hidden layer contains a plurality of elements, and the output of the network represents the reconstruction of the associated point in the image;

training the neural network so that the error between the network output and the image are minimized for all points in the image; and

setting the derived parameters to the weights of the hidden layer of the trained neural network.

6. A method for determining results from a test strip, comprising:

(a) measuring light reflected from the surface of the test strip to obtain a reflectance signal, wherein the reflectance signal is indicative of the presence of the analyte; and

(b) processing the data obtained from the reflectance signal using data processing software employing data reduction and curve fitting algorithms and/or a trained neural network to convert the reflectance signal into a result indicative of the presence or absence or a threshold concentration of analyte in a sample by a process of claim 1.

7. The method of claim 6, wherein the classification means is a neural network.

8. The method of claim 6, wherein the means of reducing the image to a set of derived parameters comprises:

defining a mathematical function containing a plurality of parameters representative of the image; and

optimizing the parameters of the mathematical function using a numerical technique that minimizes the error between the image and a reconstruction of the image using the mathematical function.

9. The method of claim 6, wherein the means of reducing the image to a set of derived parameters comprises:

inputting the image to a trained neural network, wherein the inputs to the network represent the image, the network comprises a hidden layer where the number of hidden elements is smaller than the number of inputs to the network, and the outputs of the network represent the reconstruction of the image; and

setting the derived parameters to the output values of the trained neural network.

10. The method of claim 6, wherein the means of reducing the image to a set of derived parameters comprises:

defining a neural network wherein the inputs to the network are the coordinates of a point in the image, a hidden layer contains a plurality of elements, and the output of the network represents the reconstruction of the associated point in the image;

training the neural network so that the error between the network output and the image are minimized for all points in the image; and

setting the derived parameters to the weights of the hidden layer of the trained neural network.

11. The method of claim 6, wherein the processing of data process, comprises:

(i) reducing the raw reflectance data;

(ii) plotting the reduced data to generate a second image of the data;

(iii) expressing the second image as a polynomial mathematical function to generate parameters that define this image; and

(iv) comparing the parameters to parameters generated from a reference sample, whereby a positive, negative or quantitative result is obtained.

12. The method of claim 6, wherein the data is processed by a process, comprising:

(i) optionally correcting the reflectance readings to correct for light leakage;

(ii) reducing the raw reflectance data using a ratiometric formula;

(iii) generating a second image of the test data by plotting the reduced data;

(iv) expressing the second image as a polynomial mathematical function, and generating parameters that define the image;

(v) comparing the scanned image and second image by solving the linear regression through the curves;

(vi) validating the parameters obtained from the curve-fitting and the peak heights obtained to obtain a validated result; and

(vii) classifying the validated result as positive or negative by comparing peak heights of a clinical sample to reference samples.

13. The method of claim 12, wherein the data processing process, further comprises:

(viii) inputting the validated result into a decision-support system to generate a medical diagnosis or risk assessment.

14. The method of claim 6, wherein the data processing process, further comprises:

(viii) inputting the result into a decision-support system to generate a medical diagnosis or risk assessment.

15. A method for determining results from a test strip, comprising:

(a) scanning the test strip in a reflectance reader to obtain a scanned image, wherein the signal is indicative of the presence of the analyte; and

(b) processing the data obtained from the reflectance signals using data processing software employing data reduction and curve fitting algorithms and/or a trained neural network to convert the reflectance signal obtained from reading the test strip into data indicative of the test results'"'"' presence or absence or a threshold concentration of analyte in a sample by a process of claim 1.