Diagnosis of Pulmonary Embolism Using Fuzzy Inference System

Diagnosis of Pulmonary Embolism Using Fuzzy Inference System

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 A r t ific ia l I n t e llig e n c e E x p e rt S y s te m s E L IZ A M Y C IN A r t ific ia l N e u r a l N e t w o r k s P A T T E R N R E C O G N IT IO N F u z z y I n fe r e n c e S y s te m C AM ER A C AR S

Groups Facing Higher Probability of Pulmonary Embolism Patients Undergoing various types of surgery - general, urological, neurosurgical, 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 Criterias PIOPED - Prospective Investigation of Pulmonary Embolism Diagnosis [1995]. Biellos Criteria [1979]. Inputs from Expert Radiologists. 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 Input

Fuzzifier Fuzzifier Inference Inference Engine Engine Defuzzifier Output Fuzzy Fuzzy Rule

Rule Base Base 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. 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. Testing/Simulation To ensure accuracy and usability, the software has to pass stringent tests. These tests were applied in two phases. Alpha Testing

Beta 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. 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 wont 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.

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