Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. OBJECTIVE: This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. METHODS: We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS: We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. CONCLUSIONS: AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.CRU

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer : Systematic Review

Acknowledgments This research was funded by the National Institute for Health Research (NIHR) Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening, and Early Diagnosis, PR-PRU-1217-21601. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the CanTest Collaborative (funded by Cancer Research UK C8640/A23385), of which FW and WH are directors and JE, HS, and NdW are associate directors. HS is additionally supported by the Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety (CIN13-413) and the Agency for Healthcare Research and Quality (R01HS27363). The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors would like to thank Isla Kuhn, Reader Services Librarian, University of Cambridge Medical Library, for her help in developing the search strategy.Peer reviewedPublisher PD

Recognising Skin Cancer in Primary Care

Abstract: Skin cancer, including melanoma, basal cell carcinoma and cutaneous squamous cell carcinoma, has one of the highest global incidences of any form of cancer. In 2016 more than 16,000 people were diagnosed with melanoma in the UK. Over the last decade the incidence of melanoma has increased by 50% in the UK, and about one in ten melanomas are diagnosed at a late stage. Among the keratinocyte carcinomas (previously known as non-melanoma skin cancers), basal cell carcinoma is the most common cancer amongst Caucasian populations. The main risk factor for all skin cancer is exposure to ultraviolet radiation—more than 80% are considered preventable. Primary care clinicians have a vital role to play in detecting and managing patients with skin lesions suspected to be skin cancer, as timely diagnosis and treatment can improve patient outcomes, particularly for melanoma. However, detecting skin cancer can be challenging, as common non-malignant skin lesions such as seborrhoeic keratoses share features with less common skin cancers. Given that more than 80% of skin cancers are attributed to ultraviolet (UV) exposure, primary care clinicians can also play an important role in skin cancer prevention. This article is one of a series discussing cancer prevention and detection in primary care. Here we focus on the most common types of skin cancer: melanoma, squamous cell carcinoma and basal cell carcinoma. We describe the main risk factors and prevention advice. We summarise key guidance on the symptoms and signs of skin cancers and their management, including their initial assessment and referral. In addition, we review emerging technologies and diagnostic aids which may become available for use in primary care in the near future, to aid the triage of suspicious skin lesions

Recognising Skin Cancer in Primary Care

Abstract: Skin cancer, including melanoma, basal cell carcinoma and cutaneous squamous cell carcinoma, has one of the highest global incidences of any form of cancer. In 2016 more than 16,000 people were diagnosed with melanoma in the UK. Over the last decade the incidence of melanoma has increased by 50% in the UK, and about one in ten melanomas are diagnosed at a late stage. Among the keratinocyte carcinomas (previously known as non-melanoma skin cancers), basal cell carcinoma is the most common cancer amongst Caucasian populations. The main risk factor for all skin cancer is exposure to ultraviolet radiation—more than 80% are considered preventable. Primary care clinicians have a vital role to play in detecting and managing patients with skin lesions suspected to be skin cancer, as timely diagnosis and treatment can improve patient outcomes, particularly for melanoma. However, detecting skin cancer can be challenging, as common non-malignant skin lesions such as seborrhoeic keratoses share features with less common skin cancers. Given that more than 80% of skin cancers are attributed to ultraviolet (UV) exposure, primary care clinicians can also play an important role in skin cancer prevention. This article is one of a series discussing cancer prevention and detection in primary care. Here we focus on the most common types of skin cancer: melanoma, squamous cell carcinoma and basal cell carcinoma. We describe the main risk factors and prevention advice. We summarise key guidance on the symptoms and signs of skin cancers and their management, including their initial assessment and referral. In addition, we review emerging technologies and diagnostic aids which may become available for use in primary care in the near future, to aid the triage of suspicious skin lesions

Evaluating actions to improve air quality at University Hospitals Birmingham NHS Foundation Trust

Air pollution is the single largest environmental risk to human health in the UK, exerting a major healthcare sector burden and exacerbating health and social inequalities. The NHS Long Term Plan commits the healthcare sector to reducing emissions from all sources, however, to date few Acute NHS Trusts have implemented air quality focused sustainability plans. In this case study, we assess potential air quality improvement actions at University Hospitals Birmingham NHS Foundation Trust’s, Queen Elizabeth Hospital in Birmingham, UK as a test case for NHS sustainability actions. We generate an evidenced based, prioritized shortlist of actions to mitigate emissions and protect patients, staff, and local communities from air pollution exposure. The project supports adoption of an evidence-based, contextually relevant, approach to air quality management within healthcare provision. The methodology used could be employed by organizations with similar goals to address environmental concerns. View Full-Tex

Sunyaev-Zel'dovich clusters in millennium gas simulations

Large surveys using the Sunyaev–Zel’dovich (SZ) effect to find clusters of galaxies are now starting to yield large numbers of systems out to high redshift, many of which are new dis- coveries. In order to provide theoretical interpretation for the release of the full SZ cluster samples over the next few years, we have exploited the large-volume Millennium gas cosmo- logical N-body hydrodynamics simulations to study the SZ cluster population at low and high redshift, for three models with varying gas physics. We confirm previous results using smaller samplesthattheintrinsic(spherical)Y500–M500relationhasverylittlescatter(σlog10Y ≃0.04), is insensitive to cluster gas physics and evolves to redshift 1 in accordance with self-similar expectations. Our preheating and feedback models predict scaling relations that are in excel- lent agreement with the recent analysis from combined Planck and XMM–Newton data by the Planck Collaboration. This agreement is largely preserved when r500 and M500 are derived using thehydrostaticmassproxy,YX,500,albeitwithsignificantlyreducedscatter(σlog10Y ≃0.02),a result that is due to the tight correlation between Y500 and YX,500. Interestingly, this assumption also hides any bias in the relation due to dynamical activity. We also assess the importance of projection effects from large-scale structure along the line of sight, by extracting cluster Y500 values from 50 simulated 5 × 5-deg2 sky maps. Once the (model-dependent) mean signal is subtracted from the maps we find that the integrated SZ signal is unbiased with respect to the underlying clusters, although the scatter in the (cylindrical) Y500–M500 relation increases in the preheating case, where a significant amount of energy was injected into the intergalactic medium at high redshift. Finally, we study the hot gas pressure profiles to investigate the origin of the SZ signal and find that the largest contribution comes from radii close to r500 in all cases. The profiles themselves are well described by generalized Navarro, Frenk & White profiles but there is significant cluster-to-cluster scatter. In conclusion, our results support the notion that Y500 is a robust mass proxy for use in cosmological analyses with clusters

Summary of Coupling and Tune Feedback Results during RHIC Run 6, and Possible Implications for LHC Commissioning

Early efforts to implement tune feedback during the acceleration ramp in RHIC were hampered by large betatron coupling, as well as the requirement for large dynamic range. Both problems have been addressed, the first by implementation of continuous measurement of coupling, and the second by the development of an improved analog front end. With these improvements, simultaneous coupling and tune feedback were successfully implemented for acceleration ramp development during RHIC Run 6. During the course of this work it became clear that direct excitation of the betatron resonances by high harmonics of the 60Hz power frequency was an obstacle to making the system fully operational. We report here on these results from RHIC Run 6, and implications for LHC commissioning

Operational Scenario of First High Luminosity LHC Run

A new scenario for the first operational run of the HL-LHC era (Run 4) has been recently developed to accommodate a period of performance ramp-up to achieve an annual integrated luminosity close to the nominal HL-LHC design. The operational scenario in terms of beam parameters and machine settings, as well as the different phases, are described here along with the impact of potential delays on key hardware components

"Who Milks the Cows at Maesgwyn?" The Animality of UK Rural Landscapes in Affective Registers

Landscapes are complex outplays of intersecting flows of agency in which humans and non-humans combine in a series of registers, and in cycles of comings and goings to make meshworks of life in place. The presence of animals in some landscapes can be particularly culturally, politically, ecologically, and economically significant but are often overlooked or only partially acknowledged. Here I focus on UK rural landscapes which are rich in animal presences both historically and today. I show how animal presences, and human engagements with them, form key elements of individual and collective practices and imaginings of identity. These presences come in many interrelating, messy, and contesting forms, such as companion animals, wildlife, agricultural livestock, and animals bound up with conservation and field sports. In the shifting meshworks of social, cultural, economic, political and ecological forces at work in rural landscapes, the composition of these animal presences, and the natures of these encounters, will be ever-changing but also retain familiar themes and iconographies. I argue that the animality of rurality is far more strongly represented in popular culture (television, film, literature) than it has been in academic readings of the rural. I also suggest that much of the exchange that makes up animality-rurality meshworks is articulated in affective/emotional registers. Landscape and rural studies need to develop awareness of these registers, and means by which they can be more sensitively investigated. This will be an important step in developing our understandings of all landscapes and the practices of relational, affective, everyday life, both of humans and non-humans, within them. © 2013 Copyright Landscape Research Group Ltd