1. Pulmonary Embolism Sanjay Kumar Kulchania (LECTURER)
M.M.INSTITUTE OF MEDICAL & NURSING
2. Neural Networks In Medical Diagnosis A neural network
system: does not suffer from fatigue or psychological factors that
can affect the reliability of the diagnosis procedure. once
trained, can offer the expertise of an expert radiologist in
interpreting the scans when an expert radiologist is not
accessible. has the promise for a more accurate diagnosis than is
possible with human interpretation.
3. Pulmonary Embolism (PE) Blood clots break off from their
source and become emboli. Emboli travel through the heart into the
pulmonary arteries. They occlude the arteries to various anatomic
regions of the lung. 300,000 to 600,000 hospitalizations and 50,000
People die each year from PE [NIH Consensus Statement cited August
1999]
4. Various Diagnostic Criterias Modified PIOPED - Prospective
Investigation of Pulmonary Embolism Diagnosis [1995]. Biellos
Criteria [1979]. Inputs from Expert Radiologists. The modified
PIOPED criteria was followed in this project
5. Modified PIOPED Criteria High Probability > = 2 Large
segmental perfusion defects (SPD). 1 Large SPD and >= 2 Moderate
SPD. > = 4 Moderate SPD. Intermediate Probability 1 Moderate to
< 2 Large SPD. Corresponding V/Q defect and CXR opacity in lower
lung. Single moderately matched V/Q defect. Corresponding V/Q
defect and small Pleural Effusion. Low Probability Multiple
Matching V/Q defects. Corresponding V/Q defects and CXR parenchymal
opacity in upper or middle lung zone. Corresponding V/Q defects and
large Pleural Effusion. > 3 Small SPD. Very Low Probability <
= 3 Small SPD. Normal No perfusion defects and perfusion outlines
the shape of the lung seen on CXR *CXR = Chest Radiograph **V/Q =
Ventilation-Perfusion
6. Architecture of the Neural Diagnosis System Architecture of
the Neural Diagnosis System Output Inputs to ANN Image Processing
System Artificial Neural Network Committee Machine V/Q Scans and
Chest X-Ray Graphical User Interface (GUI)
7. The ANN Committee Machine Dynamic committee machine 13 MLPs
to classify (divided into 5 groups for various probabilitites) 14
RBFNNs as Gating Networks (Part of Integrator) Confidence
Integrator (14 RBFNNs) Output Inputs 1 perceptron 1 Perceptron High
Probability Intermediate Probability 2 perceptrons MLP-1 2 hidden
nodes MLP-2 3 hidden nodes Low Probability 7 perceptrons MLP 2
hidden node Very Low Probability Normal
8. Inputs to the ANN Committee Machine 1) Size of the largest
perfusion defect with respect to the size of the lung. 2) Number of
small (< 25% of a segment) segmental perfusion defects with a
normal CXR. 3) Number of matched V/Q defects with normal CXR 4)
Number of non-segmental perfusion defects 5) Number of perfusion
defects surrounded by normally perfused lung 6) Number of
corresponding V/Q defects with CXR parenchymal opacity in upper or
middle lung zone. 7) Number of corresponding V/Q defects with large
pleural effusion. 8) Number of perfusion defects with substantially
larger CXR abnormality. 9) Number of moderate matched V/Q defects
with normal CXR. 10) Number of corresponding V/Q defects with CXR
parenchymal opacity in lower lung zone. 11) Number of corresponding
V/Q defects with small pleural effusion. 12) Number of large
(>75% of a segment) perfusion defect with normal CXR. 13) Number
of moderate (25% - 75% of a segment) perfusion defects without CXR
abnormality.
9. Outputs Classification - Normal Very Low Probability Low
Probability Intermediate Probability High Probability Confidence
Range 0 to 1
10. The Integrator Produces confidences in the MLP outputs
Confidences depends on distance of input point from decision
boundary of the particular MLP (Gaussian Function used) Confidence
= |r -1| where, r= RBFNN output Distance from Decision Boundary (x)
RBFNN Output (y) 1 0 RBFNN Output v/s Distance from Decision
Boundaries
11. Image Enhancement Intensity adjustment done to raise the
average pixel intensity in the image to a value between 65% and 70%
Nonlinear mapping using an S curve used to improve the contrast of
the image Mapped Intensity = I(x,y) * a * m 0 255 * a 127 * a 0 1 0
255 * m 200 * a Mapping function (m)Image intensity range (a