Emerging use of artificial intelligence in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.     

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.     

Artificial intelligence in medical imaging of the liver

Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.     

Artificial intelligence and inflammatory bowel disease: Where are we going?

Inflammatory bowel diseases, namely ulcerative colitis and Crohn’s disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.     

Applied machine learning in hematopathology

An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.     

Identifying patients with genetic predisposition to acute myeloid leukemia

Germline syndromes in myeloid leukemias are being discovered increasingly in patients, and their identification is essential for proper medical management to yield positive health outcomes for patients and their families. There needs to be a greater appreciation of germline predisposition driving the development of hematologic malignancies within the field of myeloid malignancies. Characterization of the influence of germline mutations on the development of myeloid malignancies is ongoing by utilization of next generation sequencing data and prognostic panels. Here, we propose modifications to the utilization and analysis of genetic results, specifically to have a high index of clinical suspicion for germline predisposition, to use assays that are comprehensive for detection of these variants, and a few caveats to interpreting sequencing data. Presented are the benefits and shortcomings of prognostic panels and clinical examples of the utilization of the prognostic panel used within the Department of Pathology at The University of Chicago. The examples demonstrate that panels performed for prognostication on DNA derived from malignant cells are able to identify patients with germline syndromes, but they can lack coverage for genes that confer inherited susceptibility. Furthermore, the panels are often not designed to find duplication and deletion mutations, which calls for a need to improve assay design and bioinformatic approaches to interpret such variants using these data.     

The Brugada Syndrome: Facts and Controversies

The diagnosis of Brugada syndrome (BS) is based on a combination of clinical (malignant arrhythmias presenting as syncopal or sudden death episodes) and electrocardiographic (pathognomonic ST segment elevation morphology) features. Over the last 15 years, since its introduction as a distinct clinical entity, the BS has been extensively investigated worldwide. In this article an overview of recent developments concerning the genetic background, the diagnostic tools and the therapeutic alternatives will be presented. In the last years, the results of the first medium-term follow-up studies have also been published. Some of these studies present contradictory results, especially concerning the identification of useful sudden death predictors in asymptomatic patients. The review presented here will discuss this prognostic controversy and will offer possible explanations for the different results.     

Artificial intelligence as the next step towards precision pathology

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to improve both the volume and precision of histomorphological evaluation. Recently, machine learning, and particularly deep learning, has enabled rapid advances in computational pathology. The integration of machine learning into routine care will be a milestone for the healthcare sector in the next decade, and histopathology is right at the centre of this revolution. Examples of potential high‐value machine learning applications include both model‐based assessment of routine diagnostic features in pathology, and the ability to extract and identify novel features that provide insights into a disease. Recent groundbreaking results have demonstrated that applications of machine learning methods in pathology significantly improves metastases detection in lymph nodes, Ki67 scoring in breast cancer, Gleason grading in prostate cancer and tumour‐infiltrating lymphocyte (TIL) scoring in melanoma. Furthermore, deep learning models have also been demonstrated to be able to predict status of some molecular markers in lung, prostate, gastric and colorectal cancer based on standard HE slides. Moreover, prognostic (survival outcomes) deep neural network models based on digitized HE slides have been demonstrated in several diseases, including lung cancer, melanoma and glioma. In this review, we aim to present and summarize the latest developments in digital image analysis and in the application of artificial intelligence in diagnostic pathology.     

Automation,machine learning,and artificial intelligence in echocardiography: A brave new world

Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.     

Computational intelligence in early diabetes diagnosis: a review

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.     

基于深度学习的医学图像分割方法

近年来,日渐成熟的人工智能深度学习技术使得众多领域逐渐实现自动化智能化作业。在医疗领域,随着医疗数据电子化和互联网医疗的发展,基于卷积神经网络实现包含定位、分割和分类于一体的辅助诊断系统应用已成为新型医疗模式发展的必然趋势。医学影像分割技术是医疗图像自动分析中的难点和重点,目前仍面临许多亟待解决的问题。该文将从临床医学影像的特点、深度学习主流分割网络和医学图像分割网络在临床中的应用3个方面对医学图像分割领域的研究进展进行系统综述,并进一步分析卷积神经网络在医学影像分割任务中的发展现状、面临的挑战以及未来的发展方向。     

Genetic epidemiology: disease susceptibility and severity

Genetic factors are increasingly recognized to have an important contribution to the occurrence of both inflammatory and noninflammatory rheumatic disease. Although identifying the specific genetic mechanisms involved in the rheumatic diseases continues to present considerable challenges, the prospect of identifying individual gene action has been brought closer by a number of recent developments. These include newer approaches to phenotype definition, refinements in statistical tools for analysis, and the advent of newer technologies, including the use of microarrays. In this article, we review some of these developments together with the recent literature on the contribution of both broad and specific genetic factors to the spectrum of rheumatic disease. We also consider contemporary opinions on the potential impact of genetic discoveries to human health.     

Air,Land, and Sea: Gene-Environment Interaction in Chronic Disease

Each of us reflects a unique convergence of DNA and the environment. Over the past 2 decades, huge biobanks linked to electronic medical records have positioned the clinical and scientific communities to understand the complex genetic architecture underlying many common diseases. Although these efforts are producing increasingly accurate gene-based risk prediction algorithms for use in routine clinical care, the algorithms often fail to include environmental factors. This review explores the concept of heritability (genetic vs nongenetic determinants of disease), with emphasis on the role of environmental factors as risk determinants for common complex diseases influenced by air and water quality. Efforts to define patient exposure to specific toxicants in practice-based data sets will deepen our understanding of diseases with low heritability, and improved land management practices will reduce the burden of disease.     

Current prognostic and predictive factors in follicular lymphoma

Follicular lymphoma (FL) is generally considered an indolent disorder. With modern day treatments, long remissions are often achieved both in the front-line and relapsed setting. However, a subset of patients has a more aggressive course and a worse outcome. Their identification is the main purpose of modern day prognostic tools. In this review, we attempt to summarize the evidence concerning prognostic and predictive factors in FL, including (1) pre-treatment factors, from baseline clinical characteristics and imaging tests to histological grade, the microenvironment and genomic abnormalities; (2) post-treatment factors, i.e., depth of response, measured both by imaging tests and minimal residual disease; (3) factors at relapse and duration of response; and (4) prognostic factors in histological transformation. We conclude that, despite the existence of numerous tools, the availability of some of them is still limited; they generally suffer from notable downsides, and most have unproven predictive value, thus having scarce bearing on the choice of regimen at present. However, with the technological and scientific developments of the last few years, the potential for these prognostic factors is promising, particularly in combination, which will probably, in time, help guide therapeutic decisions.     

Pathology – Grading and staging of GEP-NETs

Gastroenteropancreatic neuroendocrine tumours (GEP-NETs) constitute a heterogeneous group of neoplasms. In the last few decades, due to a substantial rise in incidence and prevalence, GEP-NETs have been included among the most common tumours of the gastrointestinal tract. Diagnosis could be challenging and a significant number of patients present with metastatic or unresectable disease. The development of appropriate tools for standardised prognostic stratification and the introduction of effective target therapies have opened new horizons for planning tailored surgical or medical management and follow-up programs for these complex neoplasms. An overview on the GEP-NETs' diagnostic and prognostic criteria proposed by the recently published WHO classification and ENETS and UICC TNM staging systems is presented, focussing on their impact on the clinical and therapeutical approaches.     

Controversy in clinical endocrinology: problems with reclassification of insulin-like growth factor I production and action disorders

CONTEXT: Recent developments in the IGF field have raised questions on whether this is the right time to redefine IGF deficiency. OBJECTIVE: In this controversy, arguments are made against the need for redefining IGF deficiency at this moment, suggesting instead to wait for further clinical developments. CASE: Although a number of rare case reports of IGF deficiency with precise molecular etiologies have been described, the vast majority of the cases remain clinically defined and without a genetic diagnosis. INTERVENTIONS: Because IGF products are now available for clinical use in IGF-deficient patients, we are still using GH stimulation and static IGF levels as our only clinical diagnostic and classification tools. POSITIONS: We need to develop additional clinical tools, side by side with molecular tools, for the diagnosis and subclassification of IGF deficiency. Chief among these are the IGF-generation test for identification of GH-insensitive patients and genetic panels of polymorphic changes in relevant genes. CONCLUSIONS: Until further progress is made in the clinical classification of IGF deficiency, we should not change the current classification, and, when we do, it should be the responsibility of the relevant societies in the field to conduct a consensus statement on the topic first.     

Deep learning in hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.     

Artificial intelligence in gastrointestinal endoscopy:The future is almost here

Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.     

Communication and information technology in medical education

The past few years have seen rapid advances in communication and information technology (C&IT), and the pervasion of the worldwide web into everyday life has important implications for education. Most medical schools provide extensive computer networks for their students, and these are increasingly becoming a central component of the learning and teaching environment. Such advances bring new opportunities and challenges to medical education, and are having an impact on the way that we teach and on the way that students learn, and on the very design and delivery of the curriculum. The plethora of information available on the web is overwhelming, and both students and staff need to be taught how to manage it effectively. Medical schools must develop clear strategies to address the issues raised by these technologies. We describe how medical schools are rising to this challenge, look at some of the ways in which communication and information technology can be used to enhance the learning and teaching environment, and discuss the potential impact of future developments on medical education.