Real-time clinical diagnostic systems for fluorescent spectrum analysis of tissue cells and methods thereof

• US 20050272027A1
• Filed: 04/15/2005
• Published: 12/08/2005
• Est. Priority Date: 06/07/2004
• Status: Abandoned Application

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: MediaTek, Inc.
• Inventors: Wu, Chih-Chung, Chang, Jen-Hao, Tong, Yuh-Ping, Hung, Kuo-Feng, Fong, Sou-Lin, Cheng, Yi-Ping, Su, Mei-Fang, Lee, Su-Jan
• Application Number:
  US11/107,258
• Publication Number:
  US 20050272027A1
• Time in Patent Office: Days
• US Class Current: 435/4
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01N 2021/6423 Spectral mapping, video display
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence

Abstract

Real-time clinical diagnostic expert systems for fluorescent spectrum analysis of tissue cells and methods thereof. An exemplary system includes a set of optical fibers, wherein the first optical fiber introduces an incident light to an subject epidermal tissue, and the second optical fiber receives an auto-fluorescent signal, a set of monochromators, wherein the first monochromator produces the incident light, and the second monochromator produces the auto-fluorescent signal from the second optical fiber, a light detector for detecting the auto-fluorescent signal from the second monochromator, a signal processing unit for plotting a spectrum of the auto-fluorescent signal, and a spectrum analyzing unit comprising a database for analyzing the spectrum with the database to obtain a probability of disease for the subject epidermal tissue.

Claims

1. A real-time clinical diagnosis expert system for fluorescent spectrum analysis of tissue cells, comprising:

a set of optical fibers comprising a first optical fiber for introducing an incident light to a subject epidermal tissue, and a second optical fiber for receiving an auto-fluorescent signal produced by the subject epidermal tissue;

a set of monochromators comprising a first monochromator for producing the incident light and a second monochromator for receiving the auto-fluorescent signal received by the second optical fiber;

a light detector for detecting the auto-fluorescent signal received by the second monochromator;

a signal processing unit for plotting a spectrum of the auto-fluorescent signal, and

a spectrum analyzing unit comprising a database for analyzing the spectrum with the database to obtain a disease probability for the subject epidermal tissue.

2. The system as claimed in claim 1, wherein the signal processing unit plots the auto-fluorescent signal with a weight table (W), and the weight table (W) is obtained from a serial process comprising:

combining a plurality of diseases (D) and a plurality of signals to obtain an assumption (D_k),

transferring the assumption to a spectral probability (S), and

inferring the spectral probability (S) a weight table (W);

wherein

the plurality of diseases (D) are as formula (1);

D={D₁, D₂, D₃, . . . , D_k} 

 

(1)

wherein D₁, D₂, D₃, . . . , D_k indicate the type of diseases, k is a natural number;

the assumption of the plurality of signal in each disease is as formula (2);

D_k={b_ij} 

 

(2)

wherein b indicates Boolean values, representing a Boolean value of signal i corresponding to sample j of disease D_k, and i and j are natural numbers;

the signal probability (S) is as formula (3);

S_nk=P(P_n|D_k) 

 

(3)

wherein S_nk represents a statistic probability P(|) of signal n of disease k, and n, k are natural numbers;

the weight table (W) is as formula (4);

W={S_nk} 

 

(4)

wherein n, k are natural numbers.

3. The system as claimed in claim 2, wherein the auto-fluorescent signal is as formula (5):

D_x={b_i} 

 

(5)

wherein D_x represents a disease of the subject epidermal tissue, b_i represents a Boolean value of signal I of the subject epidermal tissue, and i is a natural number.

4. The system as claimed in claim 3, wherein the spectrum analyzing unit analyzes the spectrum of the auto-fluorescent signal and the database by formula (6):

$\begin{array}{cc}{T}_k=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\left(S_{i,k}❘<br><br>b_i=\mathrm{true}\right)& \left(6\right)\end{array}$

wherein T_k represents a sum of the probability for D_x corresponding to D_k for inferring D_x to a defined disease, and the higher T_k is, the higher possibility of the defined disease.

5. The system as claimed in claim 4, further comprising an auto-modification of the weight table, the auto-modification of the weight table automatically appends a result of D_k to S_nk.

6. The system as claimed in claim 1, wherein the incident light is green light.

7. The system as claimed in claim 1, wherein the spectrum property comprises a fluorescent intensity of a defined wavelength, an area of a defined wavelength range, or a slope of a defined wave peak.

8. The system as claimed in claim 1, wherein the disease comprises basal cell epithelioma, squamous cell carcinoma, malignant melanoma, psoriasis, or nevus.

9. A clinical diagnosis method for fluorescent spectrum analysis of tissue cell, comprising:

introducing an incident light produced by a first monochromator to a subject epidermal tissue through a first optical fiber;

receiving an auto-fluorescent signal produced by the subject epidermal tissue through a second optical fiber to a second monochromator;

detecting the auto-fluorescent signal from the second monchromator by a light detector;

plotting a spectrum of the auto-fluorescent signal by a signal processing unit; and

analyzing the spectrum of the auto-fluorescent signal with a database in a spectrum analyzing unit to obtain a disease probability for the subject epidermal tissue.

10. The method as claimed in claim 9, wherein the signal processing unit plots the auto-fluorescent signal with a weight table (W), and the weight table (W) is obtained from a serial process comprising:

combining a plurality of diseases (D) and a plurality of signals to obtain an assumption (Dk),

transferring the assumption to a spectral probability (S), and

inferring the spectral probability (S) a weight table (W);

wherein

the plurality of diseases (D) are as formula (1);

D={D₁, D₂, D₃, . . . , D_k} 

 

(1)

where D₁, D₂, D₃, . . . , D_k indicate the type of diseases, k is a natural number;

the assumption of the plurality of signal in each disease is as formula (2);

D_k={b_ij} 

 

(2)

wherein b indicates Boolean values, representing a Boolean value of signal i corresponding to sample j of disease D_k, and i and j are natural numbers;

the signal probability (S) is as formula (3);

S_nk=P(P_n|D_k) 

 

(3)

wherein S_nk represents a statistic probability P(|) of signal n of disease k, and n, k are natural numbers;

the weight table (W) is as formula (4);

W={S_nk} 

 

(4)

wherein n, k are natural numbers.

11. The method as claimed in claim 10, wherein the auto-fluorescent signal is as formula (5):

D_x={b_i} 

 

(5)

wherein D_x represents a disease of the subject epidermal tissue, b_i represents a Boolean value of signal i of the subject epidermal tissue, and i is a natural number.

12. The method as claimed in claim 11, wherein the spectrum analyzing unit analyzes the spectrum of the auto-fluorescent signal and the database by formula (6):

$\begin{array}{cc}{T}_k=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\left(S_{i,k}❘<br><br>b_i=\mathrm{true}\right)& \left(6\right)\end{array}$

wherein T_k represents a sum of the probability for D_x corresponding to D_k for inferring D_x to a defined disease, and the higher T_k indicates a higher possibility of the defined disease.

13. The method as claimed in claim 12, further comprising a step of auto-modification of the weight'"'"'s table, the auto-modification of the weight table automatically appends a result of D_k to S_nk.

14. The method as claimed in claim 9, wherein the incident light is green light.

15. The method as claimed in claim 9, wherein the spectrum property comprises a fluorescent intensity of a defined wavelength, an area of a defined wavelength range, or a slope of a defined wave peak.

16. The method as claimed in claim 9, wherein the disease comprises basal cell epithelioma, squamous cell carcinoma, malignant melanoma, psoriasis, or nevus.