Diagnosis of Pulmonary Embolism Using Fuzzy Inference System Research Assistant: Vishwanath Acharya Research Director: Dr. Gursel Serpen Medical Expertise: Drs. Parsai, Coombs & Woldenberg of Medical College of Ohio

Why Artificial Intelligence??? It can offer a competent second opinion. It offers the expertise of an expert radiologist in interpreting scans when an expert radiologist is not available. It has the ability to make accurate and quick diagnosis. It has the potential to reduce inter-observer variability.

Artificial Intelligence in Practice

Groups Facing Higher Probability of Pulmonary Embolism Patients Undergoing various types of surgery - general, urological, neuro- surgical, and gynecological. Patients with orthopedic problems and chronic diseases. These groups face a higher probability of Pulmonary Embolism due to the high risk of developing deep venous thrombosis.

Various Diagnostic Criteria’s PIOPED - Prospective Investigation of Pulmonary Embolism Diagnosis [1995]. Biello’s Criteria [1979]. Inputs from Expert Radiologists.

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

Biello-Siegel Criteria High Probability (87 %) –2 Large {> 75 % of a segment} mismatches. –Q defect >> CXR and V. Intermediate Probability ( %) –Abnormality not within “low” or “high” category. Low Probability (10 %) –Small {< 25 % of a segment} Q defect(S). –Matched V/Q defects involving < 1/3 of lung. –Non-Segmental defects. –Q defect << CXR defect. Normal (0 %).

Is Fuzzy Logic really Fuzzy? Why Fuzzy Logic? Despite its name Fuzzy Logic is not nebulous, cloudy or vague. It provides a very precise approach for dealing with uncertainty which is derived from complex human behavior. Fuzzy Logic is so powerful, mainly because it does not require a deep understanding of a system or exact and precise numerical values. It uses abstraction that in human beings is arrived at from experience or intuition. It allows intermediate values and representation of knowledge with subjective concepts to be defined between conventional evaluation. It basically pays attention to the “excluded middle” gray areas. It attempts to apply a more human like way of thinking in programming of computers.

Fuzzy Inference System The three major components of the Fuzzy Inference System are: Fuzzifier - Converts the crisp input into appropriate fuzzy quantity. Inference Engine - Allows the application of the rule base to the input parameters whereby producing the output. Defuzzifier - Converts the output produced by the Inference Engine into user understandable terms. Fuzzifier Inference Engine Defuzzifier Fuzzy Rule Base Fuzzy Rule Base Input Output

Inputs to Fuzzy System (According to PIOPED Criteria) Number of Segmental Perfusions. Number of Non-Segmental Perfusions. Ventilation/Perfusion Mismatch. Chest X-Ray Abnormality. Presence of Pleural Effusion.

Inputs to Fuzzy System (According to PIOPED Criteria) Wt - Weight (pre-calculation of segmental and non-segmental perfusion defects. Vqdef - Ventilation-Perfusion Defect Mismatch. Cxrab - Chest X-ray abnormality. Peff - Presence of Pleural Effusion.

Rule Base of Fuzzy System (Modeling of the PIOPED Criteria)

Outputs from Fuzzy Inference System (According to PIOPED Criteria) Output of the Fuzzy System models the diagnostic capabilities of the Fuzzy System. Hence, the various classes are: Normal. Very Low. Low. Intermediate. High The output of the Fuzzy System are mapped to one of these classes.

Outputs from Fuzzy Inference System (According to PIOPED Criteria) Dia - Diagnosis, is the output of the Fuzzy System and is divided into 5 classes. What you see here is the tweaking that has to be given to all the classes in order to implement the PIOPED criteria to its best fit.

Alpha Testing Output data was obtained and passed to radiologists to check for accuracy. Data developed by radiologists was run through the system and checked to ensure that is produced expected results. Testing/Simulation To ensure accuracy and usability, the software has to pass stringent tests. These tests were applied in two phases. Beta Testing 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 won’t be intimidated by it.

Conclusions Implementation of Artificial Intelligence software in the diagnosis of medical diseases is feasible and can be very easily extended to cover different diseases. It can be of help to medical practitioners. The alternative methods utilized to diagnose for Pulmonary Embolism effectively capture the spirit of the PIOPED criteria. This software has the ability to make accurate and quick diagnosis.