Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Clinical Concepts and Practical Management Techniques in Dentistry

Oral healthcare deals with complete oral health, including prevention, treatment, and cure. This book provides readers with the latest updates on dental clinical concepts and practical management techniques. It is divided into four sections that contain in-depth chapters with concepts and techniques from the fields of oral medicine, periodontology, radiology, endodontics, restorative dentistry, dental trauma, and probability and sampling. It is a compendium of work by internationally recognized oral clinicians and public healthcare leaders in dentistry. It presents updates on some of the most pertinent issues within the practice of dentistry, such as regenerative endodontic procedures, the unique role of radiographs, recognition of child abuse, and dental statistics

Quality of life in patients receiving platinum based chemotherapy for advanced non-small cell lung cancer.

Quality of life in patients receiving platinum based chemotherapy for advanced non-small cell lung cancer. Lung cancer is the cause of 34,000 deaths in the UK each year, with a five year survival rate of only 7.5%. The current treatment for advanced Non Small Cell Lung Cancer is combination chemotherapy but this confers only a small survival advantage. Quality of Life is often proposed as a secondary outcome to most chemotherapy studies as chemotherapy remains palliative. Quality of life is measured using a series of tools, such as the EORTC QLQ 30 that although established and tested for validity are functionally based or focus on physical symptoms. The aim of this study is to explore the meaning of quality of life in this group of patients. The study utilises use comparative methods (interview n=50, QLQ EORTC 30 data, clinical observation/field notes, medical notes, nursing notes and mapping) to examine the meaning of quality of life in this patient group. This is essentially a collaboration of medical and nursing practice with the aim of understanding what quality of life means to these patients, improving the experience of patients undergoing treatment and offering appropriate psycho-social support. Content analysis has generated a core theme of patient experience as having an impact on quality of life (negotiation of the treatment calendar, value of treatment broker and interactions with professionals) the overlapping themes are Lens of diagnosis (viewed as atrocity stories), The worth of treatment (despite physical side effects and poor life expectancy, chemotherapy is a focus of hope and allows for adjustment to poor prognosis) and Suffering (psychological and social, for example exclusion from social activities and loss of independence). This study has impacted on the service to cancer patients at a central London NHS Foundation Trust

Capsule Network-based Radiomics: From Diagnosis to Treatment

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals have resulted in a surge of significant interest in ``radiomics". Radiomics is an emerging and relatively new research field, which refers to semi-quantitative and/or quantitative features extracted from medical images with the goal of developing predictive and/or prognostic models. Radiomics is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional radiomics workflow is, typically, based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Clinical application of hand-crafted radiomics is, however, limited by the fact that features are pre-defined and extracted without taking the desired outcome into account. The aforementioned drawback has motivated trends towards development of deep learning-based radiomics (also referred to as discovery radiomics). Discovery radiomics has the advantage of learning the desired features on its own in an end-to-end fashion. Discovery radiomics has several applications in disease prediction/ diagnosis. Through this Ph.D. thesis, we develop deep learning-based architectures to address the following critical challenges identified within the radiomics domain. First, we cover the tumor type classification problem, which is of high importance for treatment selection. We address this problem, by designing a Capsule network-based architecture that has several advantages over existing solutions such as eliminating the need for access to a huge amount of training data, and its capability to learn input transformations on its own. We apply different modifications to the Capsule network architecture to make it more suitable for radiomics. At one hand, we equip the proposed architecture with access to the tumor boundary box, and on the other hand, a multi-scale Capsule network architecture is designed. Furthermore, capitalizing on the advantages of ensemble learning paradigms, we design a boosting and also a mixture of experts capsule network. A Bayesian capsule network is also developed to capture the uncertainty of the tumor classification. Beside knowing the tumor type (through classification), predicting the patient's response to treatment plays an important role in treatment design. Predicting patient's response, including survival and tumor recurrence, is another goal of this thesis, which we address by designing a deep learning-based model that takes not only the medical images, but also different clinical factors (such as age and gender) as inputs. Finally, COVID-19 diagnosis, another challenging and crucial problem within the radiomics domain, is dealt with using both X-ray and Computed Tomography (CT) images (in particular low-dose ones), where two in-house datasets are collected for the latter and different capsule network-based models are developed for COVID-19 diagnosis

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

ABSTRACT BOOK 50th World Conference on Lung Health of the International Union Against Tuberculosis and Lung Disease (The Union)

The International Journal of Tuberculosis and Lung Disease is an official journal of The Union. The Journal’s main aim is the continuing education of physicians and other health personnel, and the dissemination of the most up-to-date infor mation in the field of tuberculosis and lung health. It publishes original articles and commissioned reviews not only on the clinical and biological and epidemiological aspects, but also—and more importantly—on community aspects: fundamental research and the elaboration, implementation and assessment of field projects and action programmes for tuberculosis control and the promo tion of lung health. The Journal welcomes articles submitted on all aspects of lung health, including public health-related issues such as training programmes, cost-benefit analysis, legislation, epidemiology, intervention studies and health systems research