Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

BACKGROUND AND AIMS: Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. METHODS: A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. RESULTS: There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. CONCLUSIONS: Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.

Quantum fidelity measures for mixed states.

Applications of quantum technology often require fidelities to quantify performance. These provide a fundamental yardstick for the comparison of two quantum states. While this is straightforward in the case of pure states, it is much more subtle for the more general case of mixed quantum states often found in practice. A large number of different proposals exist. In this review, we summarize the required properties of a quantum fidelity measure, and compare them, to determine which properties each of the different measures has. We show that there are large classes of measures that satisfy all the required properties of a fidelity measure, just as there are many norms of Hilbert space operators, and many measures of entropy. We compare these fidelities, with detailed proofs of their properties. We also summarize briefly the applications of these measures in teleportation, quantum memories and quantum computers, quantum communications, and quantum phase-space simulations.

PRODIS: a proteomics data management system with support to experiment tracking.

BACKGROUND: A research area that has greatly benefited from the development of new and improved analysis technologies is Proteomics and large amounts of data have been generated by proteomic analysis as a consequence. Previously, the storage, management and analysis of these data have been done manually. This is, however, incompatible with the volume of data generated by modern proteomic analysis. Several attempts have been made to automate the tasks of data analysis and management. In this work we propose PRODIS (Proteomics Database Integrated System), a system for proteomic experimental data management. The proposed system enables an efficient management of the proteomic experimentation workflow, simplifies controlling experiments and associated data and establishes links between similar experiments through the experiment tracking function. RESULTS: PRODIS is fully web based which simplifies data upload and gives the system the flexibility necessary for use in complex projects. Data from Liquid Chromatography, 2D-PAGE and Mass Spectrometry experiments can be stored in the system. Moreover, it is simple to use, researchers can insert experimental data directly as experiments are performed, without the need to configure the system or change their experiment routine. PRODIS has a number of important features, including a password protected system in which each screen for data upload and retrieval is validated; users have different levels of clearance, which allow the execution of tasks according to the user clearance level. The system allows the upload, parsing of files, storage and display of experiment results and images in the main formats used in proteomics laboratories: for chromatographies the chromatograms and lists of peaks resulting from separation are stored; For 2D-PAGE images of gels and the files resulting from the analysis are stored, containing information on positions of spots as well as its values of intensity, volume, etc; For Mass Spectrometry, PRODIS presents a function for completion of the mapping plate that allows the user to correlate the positions in plates to the samples separated by 2D-PAGE. Furthermore PRODIS allows the tracking of experiments from the first stage until the final step of identification, enabling an efficient management of the complete experimental process. CONCLUSIONS: The construction of data management systems for Proteomics data importing and storing is a relevant subject. PRODIS is a system complementary to other proteomics tools that combines a powerful storage engine (the relational database) and a friendly access interface, aiming to assist Proteomics research directly at data handling and storage.

Correction of eIF2-dependent defects in brain protein synthesis, synaptic plasticity, and memory in mouse models of Alzheimer's disease.

Neuronal protein synthesis is essential for long-term memory consolidation, and its dysregulation is implicated in various neurodegenerative disorders, including Alzheimer's disease (AD). Cellular stress triggers the activation of protein kinases that converge on the phosphorylation of eukaryotic translation initiation factor 2α (eIF2α), which attenuates mRNA translation. This translational inhibition is one aspect of the integrated stress response (ISR). We found that postmortem brain tissue from AD patients showed increased phosphorylation of eIF2α and reduced abundance of eIF2B, another key component of the translation initiation complex. Systemic administration of the small-molecule compound ISRIB (which blocks the ISR downstream of phosphorylated eIF2α) rescued protein synthesis in the hippocampus, measures of synaptic plasticity, and performance on memory-associated behavior tests in wild-type mice cotreated with salubrinal (which inhibits translation by inducing eIF2α phosphorylation) and in both ß-amyloid-treated and transgenic AD model mice. Thus, attenuating the ISR downstream of phosphorylated eIF2α may restore hippocampal protein synthesis and delay cognitive decline in AD patients.

Panorama da produção científica sobre Cuidados Intermediários e Hospitais Comunitários: uma revisão integrativa / Overview of scientific production on Intermediate Care and Community Hospitals: an integrative review

RESUMO Mudanças epidemiológicas decorrentes do envelhecimento populacional motivaram transformações nos sistemas de saúde mundiais. Desde 1990, emergem discussões sobre Cuidados Intermediários (CI), visando diminuir hospitalizações, melhorar a coordenação do cuidado e estimular políticas públicas de cuidados próximos ao território. Este trabalho objetivou apresentar e analisar a literatura científica sobre CI, enfatizando serviços de Hospitais Comunitários. Realizou-se uma revisão integrativa, combinando os descritores "Intermediate Care", "Community Hospitals", "Intermediate Care Facilities", "Long-Term Care", "Hospitals Chronic Disease" e "Rehabilitation Services", nos idiomas português, inglês e espanhol, nas bases Portal Regional da Biblioteca Virtual em Saúde, PubMed e Biblioteca Digital Brasileira de Teses e Dissertações. Após processo de busca e elegibilidade, foram incluídos 30 documentos para síntese qualitativa, provenientes de: Reino Unido, Noruega, Espanha, Dinamarca, Austrália e Itália. Resultados apontam para a eficácia, a efetividade e a eficiência dos CI, com boas avaliações por parte de profissionais e usuários. Os CI, como estratégia de fortalecimento da Atenção Primária à Saúde, redução de internações, favorecimento da independência funcional dos pacientes e seu retorno à comunidade, apresentam-se como uma inovação em saúde e aposta promissora. Entretanto, persistem lacunas na literatura que ensejam mais estudos sobre o tema, com vistas a subsidiar a tomada de decisão.

ABSTRACT Epidemiological changes resulting from populational aging have led to changes in global health systems. Since 1990, discussions on Intermediate Care (IC) have emerged, with a view to reducing hospitalizations, improving care coordination, and stimulating public care policies close to the territory. This work aims to present and analyze the scientific literature on CI, emphasizing services from Community Hospitals. An integrative review was carried out, combining the descriptors "Intermediate Care", "Community Hospitals", "Intermediate Care Facilities", "Long-Term Care", "Hospitals Chronic Disease", and "Rehabilitation Services", in Portuguese, English and Spanish, in the Regional Portal of Virtual Health Library, PubMed and Digital Library of Thesis and Dissertations bases. After the search and eligibility process, 30 documents were included for qualitative synthesis, coming from: The United Kingdom, Norway, Spain, Denmark, Australia and Italy. Results point to the efficacy, effectiveness and efficiency of ICs, with good evaluations by professionals and users. ICs, as a strategy to strengthen Primary Health Care, reduce hospital admissions, favor patients' functional independence and return to the community, are presented as a health innovation and promising bet. However, there are gaps in the literature that lead to more studies on the topic, with a view to supporting decision making.

Fortalecer os Cuidados Paliativos durante a pandemia de Covid-19 / Strengthening Palliative Care during the Covid-19 pandemics / Fortalecimiento de los Cuidados Paliativos durante la pandemia de la Covid-19

A pandemia de Covid-19 (doença do coronavírus) vem impondo grandes desafios. Além dos impactos econômicos e sociais, o crescente número de casos e óbitos, a sobrecarga dos serviços de saúde e a situação de vulnerabilidade a qual estão expostos os trabalhadores da saúde têm gerado uma enorme onda de sofrimento. Nesse contexto, os serviços de saúde, a população atendida e os trabalhadores da saúde podem se beneficiar de uma abordagem de cuidado baseada nos Cuidados Paliativos. Os Cuidados Paliativos visam à promoção, à prevenção e ao alívio do sofrimento; à promoção de dignidade, à melhor qualidade de vida e à adaptação a doenças progressivas. Assim, apresentamos reflexões sobre os desafios impostos pela pandemia e a importância dos Cuidados Paliativos neste momento, compreendendo a necessidade de sua adoção como abordagem transversal, incluídos em todos os serviços da Rede de Atenção à Saúde, bem como inseridos na formação profissional em saúde. (AU)

Covid-19 (Coronavirus Disease) has brought great challenges. Apart from the economic and social impacts, the growing number of cases and deaths, the overburden in health services, and the vulnerability situation to which health workers are exposed have been causing great suffering. In this context, health services, their target population, and health workers can benefit from a Palliative Care approach. The objective of Palliative Care is to foster, prevent, and alleviate suffering; promote dignity; improve quality of life; and adapt to progressive disorders. Therefore, this article reflects on the challenges imposed by the pandemics and on the importance of Palliative Care at this difficult time, understanding the need for its adoption as a transversal approach to be included in all Healthcare Network Services and in health professional education. (AU)

La pandemia de la Covid-19 (enfermedad del Coronavirus) ha impuesto grandes desafíos. Además de los impactos económicos y sociales, el creciente número de casos y fallecimientos, la sobrecarga de los servicios de salud y la situación de vulnerabilidad a la que están expuestos los trabajadores de la salud han generado una enorme ola de sufrimiento. En ese contexto, los servicios de salud, la población atendida y los trabajadores de la salud pueden beneficiarse de un abordaje de cuidado basada en los Cuidados Paliativos. Los Cuidados Paliativos tienen como objetivo la promoción, prevención y alivio del sufrimiento, promoción de la dignidad, mejor calidad de vida y adaptación a enfermedades progresivas. De esa forma, presentamos reflexiones sobre los desafíos impuestos por la pandemia y la importancia de los Cuidados Paliativos en este momento, incluyendo la necesidad de su adopción como abordaje transversal, incluido en todos los servicios de la Red de Atención de la Salud, así como inseridos en la formación profesional en salud. (AU)

Stratification of patients with liver fibrosis using dual-energy CT.

Assessing the severity of liver fibrosis has direct clinical implications for patient diagnosis and treatment. Liver biopsy, typically considered the gold standard, has limited clinical utility due to its invasiveness. Therefore, several imaging-based techniques for staging liver fibrosis have emerged, such as magnetic resonance elastography (MRE) and ultrasound elastography (USE), but they face challenges that include limited availability, high cost, poor patient compliance, low repeatability, and inaccuracy. Computed tomography (CT) can address many of these limitations, but is still hampered by inaccuracy in the presence of confounding factors, such as liver fat. Dual-energy CT (DECT), with its ability to discriminate between different tissue types, may offer a viable alternative to these methods. By combining the "multi-material decomposition" (MMD) algorithm with a biologically driven hypothesis we developed a method for assessing liver fibrosis from DECT images. On a twelve-patient cohort the method produced quantitative maps showing the spatial distribution of liver fibrosis, as well as a fibrosis score for each patient with statistically significant correlation with the severity of fibrosis across a wide range of disease severities. A preliminary comparison of the proposed algorithm against MRE showed good agreement between the two methods. Finally, the application of the algorithm to longitudinal DECT scans of the cohort produced highly repeatable results. We conclude that our algorithm can successfully stratify patients with liver fibrosis and can serve to supplement and augment current clinical practice and the role of DECT imaging in staging liver fibrosis.

Model-based detection of lung nodules in computed tomography exams. Thoracic computer-aided diagnosis.

RATIONALE AND OBJECTIVES: In this study, we developed a prototype model-based computer aided detection (CAD) system designed to automatically detect both solid and subsolid pulmonary nodules in computed tomography (CT) images. By using this CAD algorithm, along with the radiologist's initial interpretation, we aim to improve the sensitivity of radiologic readings of CT lung exams. MATERIALS AND METHODS: We have developed a model-based CAD algorithm through the use of precise mathematic models that capture scanner physics and anatomic information. Our model-based CAD algorithm uses multiple segmentation algorithms to extract noteworthy structures in the lungs and a Bayesian statistical model selection framework to determine the probability of various anatomical events throughout the lung. We tested this algorithm on 50 low-dose CT lung cancer screening cases in which ground truth was produced through readings by three expert chest radiologists. RESULTS: Using this model-based CAD algorithm on 50 low-dose CT cases, we measured potential sensitivity improvements of 7% and 5% in two radiologists with respect to all noncalcified nodules, solid and subsolid, greater than 5 mm in diameter. The third radiologist did not miss any nodules in the ground truth set. The CAD algorithm produced 8.3 false positives per case. CONCLUSION: Our prototype CAD system demonstrates promising results as a tool to improve the quality of radiologic readings by increasing radiologist sensitivity. A significant advantage of this model-based approach is that it can be easily extended to support additional anatomic models as clinical understanding and scanning practices improve.

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images.

The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.

Tracking of carotid arteries in ultrasound images.

We introduce an automated method for the 3D tracking of carotids acquired as a sequence of 2D ultrasound images. The method includes an image stabilization step that compensates for the cardiac and respiratory motion of the carotid, and tracks the carotid wall via a shape and appearance model trained from representative images. Envisaging an application in automatic detection of plaques, the algorithm was tested on ultrasound volumes from 4,000 patients and its accuracy was evaluated by measuring the distance between the location of more than 4,000 carotid plaques and the location of the carotid wall as estimated by the proposed algorithm. Results show that the centroids of over 95% of the carotid plaques in the dataset were located within 3 mm of the estimated carotid wall, indicating the accuracy of the tracking algorithm.

Contrast-independent liver-fat quantification from spectral CT exams.

The diagnosis and treatment of fatty liver disease requires accurate quantification of the amount of fat in the liver. Image-based methods for quantification of liver fat are of increasing interest due to the high sampling error and invasiveness associated with liver biopsy, which despite these difficulties remains the gold standard. Current computed tomography (CT) methods for liver-fat quantification are only semi-quantitative and infer the concentration of liver fat heuristically. Furthermore, these techniques are only applicable to images acquired without the use of contrast agent, even though contrast-enhanced CT imaging is more prevalent in clinical practice. In this paper, we introduce a method that allows for direct quantification of liver fat for both contrast-free and contrast- enhanced CT images. Phantom and patient data are used for validation, and we conclude that our algorithm allows for highly accurate and repeatable quantification of liver fat for spectral CT.

Deformable atlas for multi-structure segmentation.

We develop a novel deformable atlas method for multistructure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a probabilistic framework that combines prior anatomical knowledge with image-based cues that are specific to the subject's anatomy, and solves it using expectation-maximization method. It improves the segmentation over conventional label fusion methods especially around the structure boundaries, and is robust to large anatomical variation. The proposed method was applied to segment multiple structures in both normal and diseased brains and was shown to significantly improve results especially in diseased brains.

A probabilistic model for haustral curvatures with applications to colon CAD.

Among the many features used for classification in computer-aided detection (CAD) systems targeting colonic polyps, those based on differences between the shapes of polyps and folds are most common. We introduce here an explicit parametric model for the haustra or colon wall. The proposed model captures the overall shape of the haustra and we use it to derive the probability distribution of features relevant to polyp detection. The usefulness of the model is demonstrated through its application to a colon CAD algorithm.

Lung nodule detection via Bayesian voxel labeling.

This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior probabilities and the incorporation of relevant medical knowledge. We provide, under explicit modeling assumptions, closed-form expressions for all the probability distributions involved. The technique is applied to real data, and we present a discussion of its performance.

Part-based local shape models for colon polyp detection.

This paper presents a model-based technique for lesion detection in colon CT scans that uses analytical shape models to map the local shape curvature at individual voxels to anatomical labels. Local intensity profiles and curvature information have been previously used for discriminating between simple geometric shapes such as spherical and cylindrical structures. This paper introduces novel analytical shape models for colon-specific anatomy, viz. folds and polyps, built by combining parts with simpler geometric shapes. The models better approximate the actual shapes of relevant anatomical structures while allowing the application of model-based analysis on the simpler model parts. All parameters are derived from the analytical models, resulting in a simple voxel labeling scheme for classifying individual voxels in a CT volume. The algorithm's performance is evaluated against expert-determined ground truth on a database of 42 scans and performance is quantified by free-response receiver-operator curves.

Model-based analysis of local shape for lesion detection in CT scans.

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.

Quantification of emphysema severity by histogram analysis of CT scans.

Emphysema is characterized by the destruction and over distension of lung tissue, which manifest on high resolution computer tomography (CT) images as regions of low attenuation. Typically, it is diagnosed by clinical symptoms, physical examination, pulmonary function tests, and X-ray and CT imaging. In this paper we discuss a quantitative imaging approach to analyze emphysema which employs low-level segmentations of CT images that partition the data into perceptually relevant regions. We constructed multi-dimensional histograms of feature values computed over the image segmentation. For each region in the segmentation, we derive a rich set of feature measurements. While we can use any combination of physical and geometric features, we found that limiting the scope to two features - the mean attenuation across a region and the region area - is effective. The subject histogram is compared to a set of canonical histograms representative of various stages of emphysema using the Earth Mover's Distance metric. Disease severity is assigned based on which canonical histogram is most similar to the subject histogram. Experimental results with 81 cases of emphysema at different stages of disease progression show good agreement against the reading of an expert radiologist.

Cuidados intermediários e redes de atenção à saúde / Intermediate care and health care networks

A sustentabilidade do SUS é proporcional à sua capacidade de mudar, inovar, reinventar modos singulares de organização, funcionamento, especialmente na micropolítica das práticas no cotidiano. O que as experiências retratam nesta obra é justamente a prática e reflexão sobre este cotidiano, o trabalho de cada dia que realmente sustenta o funcionamento deste gigante chamado Sistema Único de Saúde. Este livro traz a experiência no Brasil dos cuidados intermediários, como dispositivo de cuidado que se situa entre a atenção básica e hospitalar. Mais do que um lugar, é um fluxo de conexões entre possibilidades de cuidado, no qual o usuário pode tomar para si os recursos necessários à sua proteção, conforto, produção de uma vida. A obra foi produzida a muitas mãos, pessoas da Itália e do Brasil, irmanadas em um projeto de construção de sistemas de saúde universais, e serviços acolhedores. Aqui se encontram experiências, formulações teóricas, discussões, polêmicas e muita vontade. Especialmente de imprimir força instituinte ao SUS, e aos serviços da Região Emilia-Romagna na Itália, que participaram ativamente deste esforço, como parceria, companheirismo e solidariedade. O livro escrito durante o ano de 2019, tem seu lançamento no ápice da pandemia de Covid-19 no Brasil, maio/junho de 2020. O que se vê aqui em relação à produção do cuidado, ativação de redes de saúde, e muita generosidade entre trabalhadores e usuários, serve para este período de enfrentamento do novo Coronavírus, assim como para o período posterior a este surto. Um novo mundo deverá emergir de tanto sofrimento imposto pela pandemia, e nele, a atividade comunitária, associada à harmonia entre as pessoas e o ambiente, poderá apontar para possibilidade de um novo mundo. Nele, supomos relações e práticas em saúde com base na ideia de redes solidários de produção do cuidado.