SANJAY KUMAR

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].
• Biello’s 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 <= 1)
Resulting Intensity
Intensity mapping done during enhancement

12. Architecture of the GUI
Opening Screen
Identify Defective Views
Case of Normal Ventilation scan and Chest Xray
Number of segmental defects in each view
Number of non-segmental defects in each view
Number of perfusion defects surrounded by normally perfused lung
Case of abnormal Ventilation scan and/or Chest XRay
Number of segmental defects in each view
Number of non-segmental defects in each view
Number of perfusion defects surrounded by normally perfused lung
Defects with parenchymal Opacity in Lower, Upper or Middle Lung
Pleural Effusion
Perfusion Defects with substantially larger CXR abnormality
Case of Segmental Perfusion Defects
Identify Lung
Identify defect
Identify defective Segment
Result

13. The Opening Screen
Opening screen of the user interface
•Go through the set of
images
•Identify images that show
a defect
•Select “Bigger View”
button for a better view

14. Case of Normal Ventilation
Scans and Chest XRay
Screen shown in case of Perfusion defects only
For each defective view
•Number of Segmental Perfusion
Defects in the view
•Screen for identifying area of
defect and segment with defect
•Number of Non-segmental defects
•Number of Perfusion defects
surrounded by normally perfused lung

15. Case of segmental perfusion
defects
Screen for marking the segmental perfusion defects
•Identify Lung(s)
•Identify defect(s)
•Identify Segment(s)

16. Case of abnormal Ventilation
scan and/or Chest XRay
Screen shown in cases where Ventilation and/or Perfusion
defects are present.
• Number of segmental defects in each
view
• Number of non-segmental defects in
each view
• Number of perfusion defects
surrounded by normally perfused lung
• Defects with parenchymal Opacity in
Lower, Upper or Middle Lung
• Pleural Effusion
• Perfusion defects with substantially
larger CXR abnormality

17. Result Screen
Screen showing the result

18. Stage 1
• The MLPs in the committee
machine were trained and tested
individually.
• Testing was done to identify and
confirm the positions of the
decision boundaries in Input space.
• RBFNNs were trained to find
cluster centers at the decision
bondaries created by the MLPs (A
distance function was used for
this)
Training/Testing/Simulation
Stage 2
• The committee machine was
integrated (the MLP system and
the Integrator were connected) and
testing was done using a different
set of data.
• The Committee machine was
integrated with the User Interface.
Alpha Phase

19. • Currently being implemented. In this phase the radiologist will have
a hands on experience. This will ensure that the software has a high
degree of usability and physicians will not be intimidated by it.
Training/Testing/Simulation
Beta Phase

20. Conclusions
• Implementation of Artificial Neural Network Systems in
the diagnosis of medical diseases is feasible and can be
very easily extended to cover different diseases.
• The methods utilized to diagnose Pulmonary Embolism
effectively capture the spirit of the modified PIOPED
criteria.
• This system has the ability to make accurate and quick
diagnosis.

21. The Future...
• Total Automation (Radiologists not required to identify
defects)
• Improved Diagnostic capabilities beyond the modified
PIOPED criteria by training using angiography results.
• Network output in terms of presence or absence of PE (not
probabilities).
• Use of other Artificial Intelligence paradigms such as
Fuzzy Logic Systems and Expert Systems in combination
with Artificial Neural Network System.