Observational Study Design in Veterinary Pathology, Part 1: Study Design

Observational studies are the basis for much of our knowledge of veterinary pathology and are highly relevant to the daily practice of pathology. However, recommendations for conducting pathology-based observational studies are not readily available. In part 1 of this series, we offer advice on planning and conducting an observational study with examples from the veterinary pathology literature. Investigators should recognize the importance of creativity, insight, and innovation in devising studies that solve problems and fill important gaps in knowledge. Studies should focus on specific and testable hypotheses, questions, or objectives. The methodology is developed to support these goals. We consider the merits and limitations of different types of analytic and descriptive studies, as well as of prospective vs retrospective enrollment. Investigators should define clear inclusion and exclusion criteria and select adequate numbers of study subjects, including careful selection of the most appropriate controls. Studies of causality must consider the temporal relationships between variables and the advantages of measuring incident cases rather than prevalent cases. Investigators must consider unique aspects of studies based on archived laboratory case material and take particular care to consider and mitigate the potential for selection bias and information bias. We close by discussing approaches to adding value and impact to observational studies. Part 2 of the series focuses on methodology and validation of methods

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

A review of research into the development of radiologic expertise: Implications for computer-based training

Rationale and Objectives. Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.Materials and Methods. This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.Results and Conclusion. Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions