An automated intelligent diagnostic system for the interpretation of umbilical artery Doppler velocimetry.

The objective is to develop an automated intelligent diagnostic system for the interpretation of umbilical artery velocity waveforms. An ultrasound instrument with pulsed-wave Doppler is connected to a microcomputer by means of a frame grabber. After data acquisition, umbilical Doppler velocimetry is handled as a pattern recognition (feature extraction and classification) and decision-making problem. Automated image processing (enhancement, smoothing/ thresholding and edge detection) and analysis are used for feature extraction. Six waveform indices obtained by feature extraction are used as input layer to vector quantization which classifies waveforms into six groups. A clinical decision is assigned to each group by the medical expert. Our system is trained by 278 and 380 waveform images of 94 normal and 157 high risk pregnancies, respectively. The system was tested with 193 and 61 images of normal and risky pregnancies; it was demonstrated that sensitivity and specificity of the system are 54.1% and 80.3%, respectively.

An expert diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics.

In this study, we introduce an expert system for intelligent chromosome recognition and classification based on artificial neural networks (ANN) and features obtained by automated image analysis techniques. A microscope equipped with a CCTV camera, integrated with an IBM-PC compatible computer environment including a frame grabber, is used for image data acquisition. Features of the chromosomes are obtained directly from the digital chromosome images. Two new algorithms for automated object detection and object skeletonizing constitute the basis of the feature extraction phase which constructs the components of the input vector to the ANN part of the system. This first version of our intelligent diagnostic system uses a trained unsupervised neural network structure and an original rule-based classification algorithm to find a karyotyped form of randomly distributed chromosomes over a complete metaphase. We investigate the effects of network parameters on the classification performance and discuss the adaptability and flexibility of the neural system in order to reach a structure giving an output including information about both structural and numerical abnormalities. Moreover, the classification performances of neural and rule-based system are compared for each class of chromosome.

A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms.

OBJECTIVE: Development of an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. STUDY DESIGN: Study design comprised several stages including data acquisition, image processing and analysis, training of artificial neural network and testing the predictive value of the system. The clinical material was handled in two groups. The training group consisted of 952 umbilical artery blood flow velocity waveform images of 174 normal pregnancies with normal outcome, while the testing group was composed of 138 images derived from 20 normal pregnancies with normal outcome and 68 images of 16 high risk pregnancies with poor outcome. All subjects were evaluated by Doppler ultrasonography and umbilical artery blood flow velocity waveform images were transferred to the computer environment by means of a special data acquisition system. Automated image processing and analysis were performed to derive indices such as A/B ratio, resistance index, pulse related index, area ratio of wave and angle of coincident slopes. We have used a supervised artificial neural network (back propagation learning algorithm) to develop an intelligent diagnostic system which is called the BOLU system. RESULTS: This version of the system was trained with the umbilical artery blood flow velocity waveform images of normal pregnancies. Thus, the BOLU system decides whether the tested image is normal for a given gestational week or not. The specificity and sensitivity of this system were estimated to be 98.6% and 51.5% respectively. CONCLUSION: We have developed an artificial intelligent diagnostic system for the interpretation of umbilical artery blood flow velocity waveform measurements. Waveform indices were obtained automatically by image processing and analysis. The predictive value of the system was found to be satisfactory.