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Decompression sickness (DCS) remains a major operational concern for diving operations, submarine escape and high-altitude jumps. Aside from DCS symptoms, venous gas emboli (VGE) detected with ultrasound post-dive are often used as a marker of decompression stress in humans, with a specificity of 100% even though the sensitivity is poor [1]. Being non-invasive, portable and non-ionizing, ultrasound is particularly suited to regular and repeated monitoring. It could help elucidate inter- and intra-subject variability in VGE and DCS susceptibility, but analyzing these recordings remains a cumbersome task [2]., Competing Interests: The authors of this paper declare no conflicts of interest exist with this submission., (Copyright© Undersea and Hyperbaric Medical Society.)

Background: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making., Main Body: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations., Conclusion: The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health., Competing Interests: The authors have declared that no competing interests exist.

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

The recent opening of massive health databases, as well as the development of methods and tools adapted to their data processing, questions the French model of "morbidity registry". In France in 2019, nearly 61 health registries were operating. As defined by law, these registries identify exhaustively all patients with a given disease in a given territory. Established several decades ago, these registries are part of the French surveillance system that is used for research and evaluation purposes. Since the advent of recent technological progress, large-scale databases are made available to researchers and it is possible with these databases to answer questions initially assigned to the registries. What is the place of such registries in this new context: are they obsolete or still useful? Should they be opposed to the new tools or are they complementary to them, and if so, what is their place in the new French public health ecosystem? The objective of this work was to assess the roles and missions of existing registries and to reflect on their positioning in this new environment. The French model of registry is sometimes questioned because of the complexity of its circuits, requiring a significant amount of human resources. However, the data that constitute them, validated by cross-checking information from several sources, are of very high quality, and make it possible to validate the data in the new databases (National Health Data System (NSDS) or Hospital Data Warehouses). Registries and new databases are in fact complementary, and far from jeopardizing this model, the recent opening of these databases represents an opportunity for registries to modernize their operations and respond to new missions., (Copyright © 2019 Elsevier Masson SAS. All rights reserved.)

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare., Competing Interests: No potential conflict of interest relevant to this article was reported., (Copyright © 2019 Korean Endocrine Society.)

Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies - which the authors suggest be grouped under the term 'digitosome' - constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes - its prevention, management, technology and research - and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Brain injuries are complicated medical problems and their management requires data from disparate sources to extract actionable information. In neurocritical care, interoperability is lacking despite the perceived benefits. Several efforts have been underway, but none have been widely adopted, underscoring the difficulty of achieving this goal. We have identified the current pain points of data collection and integration based on the experience with two large multi-site clinical studies: Transforming Research And Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) in the United States and Collaborative European Neuro Trauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) in Europe. The variability of measurements across sites remains a barrier to uniform data collection. We found a need for annotation standards and for a standardized archive format for high-resolution data. Overall, the hidden cost for successful data collection was initially underestimated.Although the use of bedside data integration solutions, such as the Moberg's Component Neuromonitoring System (Moberg Research, Inc., Ambler, PA, USA) or ICM+ software (Cambridge Enterprise, Cambridge, UK), facilitated the homogenous collection of synchronized data, there remain issues that need to be addressed by the neurocritical care community. To this end, we have organized a Working Group on Neurocritical Care Informatics, whose next step is to create an overarching informatics framework that takes advantage of the collected information to answer scientific questions and to accelerate the translation of trial results to actions benefitting military medicine.

Historically, single-case studies of brain-damaged individuals have contributed substantially to our understanding of cognitive processes. However, the role of single-case cognitive neuropsychology has diminished with the proliferation of techniques that measure neural activity in humans. Instead, large-scale informatics approaches in which data are gathered from hundreds of neuroimaging studies have become popular. It has been claimed that utilizing these informatics approaches can address problems found in single imaging studies. We first discuss reasons for why cognitive neuropsychology is thought to be in decline. Next, we note how these informatics approaches, while having benefits, are not particularly suited for understanding functional architectures. We propose that the single-case cognitive neuropsychological approach, which is focused on developing models of cognitive processing, addresses several of the weaknesses inherent in informatics approaches. Furthermore, we discuss how using neural data from brain-damaged individuals provides data that can inform both cognitive and neural models of cognitive processing.