Convergence: The future of health

Sharp, Phil; Hockfield, Susan

doi:10.1126/science.aam8563

Cited by 46 publications

(31 citation statements)

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“…https://www.darpa.mil/program/explainable-artificial-intelligence52 The approach we have taken at our MMIV center https://mmiv.no, located inside the Department of Radiology53 In-line with the ideas of the convergence of disciplines and the "future of health", as described in[361] …”

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confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,587

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,587

883

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“…In 2015, the NCI continued into phase II of the Physical Sciences–Oncology initiative by building a collaborative network of ten PS–OCs and eight Physical Sciences–Oncology Projects (PS–OPs). In addition to programs at the NCI, the National Institutes of Health (NIH), National Science Foundation (NSF), the US Department of Defense, and the US Department of Energy are now involved in some aspects of research at the integration of physical sciences, engineering and the life sciences — broadly referred to as convergence research 208 . However, the level of support for convergence research is small, with only 3% of all NIH funding going to principal investigators in physical sciences, engineering and mathematics 208 and less going to those focused on integrating their expertise into cancer research.…”

Section: Future Directionsmentioning

confidence: 99%

Engineering and physical sciences in oncology: challenges and opportunities

2017

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The principles of engineering and physics have been applied to oncology for nearly 50 years. Engineers and physical scientists have made contributions to all aspects of cancer biology, from quantitative understanding of tumour growth and progression to improved detection and treatment of cancer. Many early efforts focused on experimental and computational modelling of drug distribution, cell cycle kinetics and tumour growth dynamics. In the past decade, we have witnessed exponential growth at the interface of engineering, physics and oncology that has been fuelled by advances in fields including materials science, microfabrication, nanomedicine, microfluidics, imaging, and catalysed by new programmes at the National Institutes of Health (NIH), including the National Institute of Biomedical Imaging and Bioengineering (NIBIB), Physical Sciences in Oncology, and the National Cancer Institute (NCI) Alliance for Nanotechnology. Here, we review the advances made at the interface of engineering and physical sciences and oncology in four important areas: the physical microenvironment of the tumour and technological advances in drug delivery; cellular and molecular imaging; and microfluidics and microfabrication. We discussthe research advances, opportunities and challenges for integrating engineering and physical sciences with oncology to develop new methods to study, detect and treat cancer, and we also describe the future outlook for these emerging areas.

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“…All of these findings are now converging through the merging of life, engineering in physical sciences that will fundamentally change the approach to disciplinary strategies in human health and the global scale. 14,15 Public health practice in training will be greatly impacted by the need for multidisciplinary approaches to address the chronic diseases that will be the major sources of morbidity for the rest of the century.…”

Section: Populations and Population Health In The 21st Centurymentioning

confidence: 99%

Environmental health in the biology century: Transitions from population to personalized prevention

Groopman

2019

Exp Biol Med (Maywood)

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Within the last decade, for the first time in human history, deaths from chronic diseases have exceeded mortality from acute causes worldwide. These chronic diseases encompass a spectrum of cancers, cardiovascular diseases, neurological diseases, and the emerging consequences of obesity and over nutrition. Further, there are more people today who are cancer survivors as well as people who are afflicted with multiple chronic diseases. This results in an emerging new group of susceptible populations with complex biology’s that will drive the development of new experimental models. Since environmental exposures have a profound impact from the etiology of disease through progression and response to therapeutic and preventive interventions, a new appreciation of the role of environmental health has emerged. This mini-review will attempt to provide a global perspective on the transitions that have occurred in environmental health over the last 200 years and how these transitions are impacting diverse populations globally. The extraordinary advances in our understanding of the biology of normal development and the molecular progression of disease processes have created unprecedented opportunities for the translation of basic science to therapy and prevention. The need to integrate findings from the biological, physical, engineering, social, and behavioral sciences, sometimes called convergence, points to an imperative to develop new team science approaches to address the health consequences of environmental exposures. Finally, as it is increasingly recognized that disease outbreaks in one part of the world are no longer isolated from global impacts, there is a need to assure that our next generations of trained scientists have grounding in global collaborations. Impact statement There is a rapidly occurring, dynamic change, in the causes of morbidity and mortality in different populations across the globe. More people today are being diagnosed and treated for chronic diseases such as cancer, cardiovascular disease, and diabetes than ever before. Environmental exposures across the lifespan have a profound impact on the outcomes of these chronic diseases. Further, there are more people living today who have survived their therapy from these diagnoses and who are now differentially susceptible to environmental exposures. Collectively, this poses both the challenge and opportunity to the experimental biology and medicine community to build new models that reflect this changing human situation. The extraordinary advances in our understanding of the biology of disease provide extraordinary insights for both therapeutic and prevention strategies. Multidisciplinary teams including biological, physical, engineering and social and behavioral scientists will be needed to address this problem over the next several decades.

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Convergence: The future of health

Cited by 46 publications

References 0 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Engineering and physical sciences in oncology: challenges and opportunities

Environmental health in the biology century: Transitions from population to personalized prevention

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