Triage-driven diagnosis for early detection of oesophageal cancer

In this thesis I present my work to advance the early detection of oesophageal adenocarcinoma by investigating translational aspects of a minimally invasive oesophageal cell sampling technology for the detection of Barrett oesophagus. Most oesophageal adenocarcinoma patients present with advanced disease, requiring treatment with chemotherapy with or without radiotherapy, followed by surgery to remove the oesophagus, and even then the overall five-year survival is less than 20%. However, if the cancer can be diagnosed at an early, superficial stage then treatment can be performed endoscopically and over 80% of patients survive beyond 5 years. The disease has a clear pre-cancer stage called Barrett oesophagus, making early detection feasible. A novel test called Cytosponge for diagnosing Barrett by cell collection coupled with an immunohistochemical test (Trefoil factor 3 / TFF3) has been developed. I have investigated two distinct topics, which are key to implement the Cytosponge-TFF3 test in primary and secondary care. First, I analysed and interpreted data of a pragmatic, prospective, multicentre, randomised controlled trial (BEST3) in order to evaluate the use of Cytosponge in primary care. The study aim was to investigate whether offering this test to patients on medication for gastro-oesophageal reflux disease (GERD) would increase the detection of Barrett oesophagus compared with usual care. We were able to show that in patients with GERD the offer of Cytosponge-TFF3 testing results in improved detection (in excess of 10-fold) of Barrett oesophagus. Second, I devised and implemented a machine learning framework applied to Cytosponge samples with the objective to reduce the pathologists' screening time. I trained and independently validated the framework on data from two clinical trials, analysing a combined total of 4,662 pathology slides from 2,331 patients. The approach exploits screening patterns of expert gastrointestinal pathologists and established decision pathways to define eight triage classes of varying priority for manual expert review. By substitution of manual review with automated review in low-priority classes, I can reduce pathologist workload by 57% while matching the diagnostic performance of expert pathologists

Object Detection in medical imaging

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art. To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis. The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches. The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetíveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte. Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes específicas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas. Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possível resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking. As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições típicas nesta área

Small bowel motility quantitation using MRI and its relationship to gastrointestinal symptoms

The small bowel is difficult to analyse due to its deep anatomical location and the large variation seen in individuals, in regard to both anatomy and function including motility. Dynamic MRI allows small bowel motility to be captured and visually assessed by radiologists, but there is often large inter-observer variation and a lack of complicated motility patterns being investigated. This thesis aims to explore the link between abnormal motility and gastrointestinal (GI) symptoms in Crohn’s disease (CD) and irritable bowel syndrome (IBS) using MRI. Firstly, a scan duration of 15 seconds and a temporal resolution of 1 image per second were shown to be sufficient for robust small bowel MRI motility measurements. Next, a validation study confirmed an association between aberrant motility and CD patient symptoms, particularly diarrhoeal stools (rho = -0.29). The strongest association was in patients with higher symptom severity (rho = -0.633). Building on this work, more complex motility metrics were developed and compared to subjective radiological scoring. Spatial and temporal variation were found to be associated with CD patient symptoms and were also particularly difficult to visually assess. The motility metrics were applied in clinical IBS data to explore differences in IBS subgroups. Significantly reduced temporal variation of motility (P < 0.001) and area of motile bowel (P < 0.001) was found in IBS-C (constipation-predominant) compared to IBS-M (mixed constipation and diarrhoea). Finally, texture analysis (TA) terminal ileum (TI) to colon ratios were found to be higher for TA contrast (P = 0.005) and lower for TA energy (P = 0.03) in IBS-C compared to healthy controls (HCs). Ascending colon diameter was shown to be significantly larger in IBS-C than HCs (P = 0.005)

Seeing the Big Picture: System Architecture Trends in Endoscopy and LED-Based hyperspectral Subsystem Intergration

Early-stage colorectal lesions remain difficult to detect. Early development of neoplasia tends to be small (less than 10 mm) and flat and difficult to distinguish from surrounding mucosa. Additionally, optical diagnosis of neoplasia as benign or malignant is problematic. Low rates of detection of these lesions allow for continued growth in the colorectum and increased risk of cancer formation. Therefore, it is crucial to detect neoplasia and other non-neoplastic lesions to determine risk and guide future treatment. Technology for detection needs to enhance contrast of subtle tissue differences in the colorectum and track multiple biomarkers simultaneously. This work implements one such technology with the potential to achieve the desired multi-contrast outcome for endoscopic screenings: hyperspectral imaging. Traditional endoscopic imaging uses a white light source and a RGB detector to visualize the colorectum using reflected light. Hyperspectral imaging (HSI) acquires an image over a range of individual wavelength bands to create an image hypercube with a wavelength dimension much deeper and more sensitive than that of an RGB image. A hypercube can consist of reflectance or fluorescence (or both) spectra depending on the filtering optics involved. Prior studies using HSI in endoscopy have normally involved ex vivo tissues or xiv optics that created a trade-off between spatial resolution, spectral discrimination and temporal sampling. This dissertation describes the systems design of an alternative HSI endoscopic imaging technology that can provide high spatial resolution, high spectral distinction and video-rate acquisition in vivo. The hyperspectral endoscopic system consists of a novel spectral illumination source for image acquisition dependent on the fluorescence excitation (instead of emission). Therefore, this work represents a novel contribution to the field of endoscopy in combining excitation-scanning hyperspectral imaging and endoscopy. This dissertation describes: 1) systems architecture of the endoscopic system in review of previous iterations and theoretical next-generation options, 2) feasibility testing of a LED-based hyperspectral endoscope system and 3) another LED-based spectral illuminator on a microscope platform to test multi-spectral contrast imaging. The results of the architecture point towards an endoscopic system with more complex imaging and increased computational capabilities. The hyperspectral endoscope platform proved feasibility of a LED-based spectral light source with a multi-furcated solid light guide. Another LED-based design was tested successfully on a microscope platform with a dual mirror array similar to telescope designs. Both feasibility tests emphasized optimization of coupling optics and combining multiple diffuse light sources to a common output. These results should lead to enhanced imagery for endoscopic tissue discrimination and future optical diagnosis for routine colonoscopy

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Ergonomics of the Operative Field in Paediatric Minimal Access Surgery

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New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process