866 The Use of Deep Learning for The Detection, Characterisation and Prediction of Metastatic Disease From CT: A Systematic Review

Introduction Computed tomography (CT) is widely used in the clinical setting for the diagnosis, staging and management of cancer. The presence of metastatic disease in cancer has significant implications on most effective treatment options as well as prognosis. With advances in computing technology, deep learning - a form of machine learning - where layers of programmed algorithms are able interpret and recognise patterns may have a potential role in CT image analysis. This review aims to provide an overview on the use of deep learning in CT image analysis in the diagnostic evaluation of metastatic disease. Method A systematic search on databases Medline, Embase and Central was performed. Retrieved studies were screened as per the inclusion and exclusion criteria. A total of 29 studies were included for which a narrative synthesis was provided Results With regards to metastatic disease, the studies could be grouped together into three areas of research. Firstly, the use of deep learning on the detection of metastatic disease from CT imaging. Secondly, its use on the characterisation of lesions on CT into metastatic disease. Finally, the use of deep learning to predict the presence or development of metastatic disease based on the primary tumour. Conclusions Deep learning in CT image analysis could have a potential role in evaluating metastatic disease, however, prospective clinical trials investigating its clinical value is required.

CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.

A Fully Automatic AI-based CT Image Analysis System for Accurate Detection, Diagnosis, and Quantitative Severity Evaluation of Pulmonary Tuberculosis

Background: Accurate and rapid diagnosis of pulmonary tuberculosis (TB) plays a crucial role in timely prevention and appropriate medical treatment to the disease. This study aims to develop and evaluate an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB.Methods: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning based cascading framework was connected to create a processing pipeline. To train and validate the model, 1921 lesions were manually labeled, classified by six categories of critical imaging features, and visually scored for the lesion involvement as the ground truth. “TB score” was calculated by the network-activation map to assess the disease burden quantitively. Independent test datasets from two additional hospitals and NIH TB Portal were used to validate externally the performance of the AI model.Results: CT scans from 526 participants (mean age, 48.5 years±16.5; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of 0.68 on the validation cohort. In the independent datasets, the overall classification accuracy for six pulmonary critical imaging findings indicative of TB were 81.08%-91.05%. A moderate to strong correlation was demonstrated between the AI model quantified “TB score” and the radiologist-estimated CT score.Conclusion: This end-to-end AI system based on chest CT can achieve human-level diagnostic performance, and holds great potential for early management and medical resource optimization of patients with pulmonary TB in clinical practice.

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

CT Image Analysis Using Grayscale Statistics to Categorise Severity of Lung Abnormalities of COVID-19 Patients

Grayscale image attributes from 456 images extracted from CT scan slices of 53 patients (49 with COVID-19 and 4 without) are used to establish a visual scale of severity of lung abnormalities (five classes: 0 to 4). The complex trends of these easy-to-derive image attributes can be used graphically to discern the visual scale of lung abnormalities in broad terms. With the aid of machine learning algorithms, the visual classes can be distinguished with close to 95% accuracy using combinations of selected grayscale attributes. Confusion matrices reveal that the best-performing machine learning models are able to distinguish more accurately between certain classes than visual inspection of CT images by experts. The adaboost, decision tree and random forest models confused on average less than 25 of the 456 CT-scan image extracts evaluated between the visual classes of lung abnormalities.