Automatic detection of pulmonary embolism using computational intelligence.

Student Number : 0418382M - MSc(Eng) dissertation - School of Electrical Engineering and Information Technology - Faculty of Engineering and the Built EnvironmentPulmonary embolism (PE) is a potentially fatal, yet potentially treatable condition. The problem of diagnosing PE with any degree of confidence arises from the nonspecific nature of the symptoms. In difficult cases, multiple tests will need to be performed on a patient before an accurate diagnosis can be made. These tests include Ventilation-Perfusion (V/Q) scanning, Spiral CT, leg ultrasound and d- Dimer testing. The aim of this research is to test the performance of using neural networks, namely Bayesian Neural Networks, to make a diagnosis based on available information. The information contains of a set of 12 V/Q scans which have been processed, and from which features have been extracted to provide inputs to the neural network. This system will act as a second opinion, and is not intended to replace an experienced clinician. The V/Q scans are analysed using image processing techniques in order to segment the lung from the background image and to determine if any abnormalities are present in the lung. The system must be able to discriminate between a genuine case of PE and of other diseases showing similar symptoms such as tuberculosis and parenchymal lung disease. Relevant features to be used in classification were then extracted from the images. The goal of this system is to make use of Bayesian neural networks. Using Bayesian networks, confidence levels can be calculated for each prediction the network makes. This makes them more informative than traditional multi layer perceptron (MLP) networks. The V/Q scans themselves are greyscale images of [256x256] size. In order to reduce redundancy and increase computational speed, Principal Component Analysis (PCA) is used to obtain the most significant information in each of the scans. Usually the Gold Standard for such a system would be pulmonary angiography, but in this case the Bayesian MLP (BMLP) is trained based on diagnosis by an experienced nuclear medicine physician. The system will be used to look at new cases for which the accuracy of the system can be established. Results showed good training performance, while validation performance was reasonable. Intermediate cases proved to be the most difficult to diagnose correctly

Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following

Cardiovascular computed tomography in cardiovascular disease: An overview of its applications from diagnosis to prediction

Cardiovascular computed tomography angiography (CTA) is a widely used imaging modality in the diagnosis of cardiovascular disease. Advancements in CT imaging technology have further advanced its applications from high diagnostic value to minimising radiation exposure to patients. In addition to the standard application of assessing vascular lumen changes, CTA-derived applications including 3D printed personalised models, 3D visualisations such as virtual endoscopy, virtual reality, augmented reality and mixed reality, as well as CT-derived hemodynamic flow analysis and fractional flow reserve (FFRCT) greatly enhance the diagnostic performance of CTA in cardiovascular disease. The widespread application of artificial intelligence in medicine also significantly contributes to the clinical value of CTA in cardiovascular disease. Clinical value of CTA has extended from the initial diagnosis to identification of vulnerable lesions, and prediction of disease extent, hence improving patient care and management. In this review article, as an active researcher in cardiovascular imaging for more than 20 years, I will provide an overview of cardiovascular CTA in cardiovascular disease. It is expected that this review will provide readers with an update of CTA applications, from the initial lumen assessment to recent developments utilising latest novel imaging and visualisation technologies. It will serve as a useful resource for researchers and clinicians to judiciously use the cardiovascular CT in clinical practice

Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier's probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP$_{50}$, sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP$_{50}$. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.Comment: Published in Expert Systems With Application