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- Dance, Sarah L., et al.
(författare)
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Improvements in Forecasting Intense Rainfall : Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
- 2019
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Ingår i: Atmosphere. - : MDPI. - 2073-4433. ; 10:3
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Forskningsöversikt (refereegranskat)abstract
- The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project's achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.
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| 3. |
- Ingram, Julie, et al.
(författare)
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What are the priority research questions for digital agriculture?
- 2022
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Ingår i: Land use policy. - : Elsevier. - 0264-8377 .- 1873-5754. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- There is a need to identify key existing and emerging issues relevant to digitalisation in agricultural production that would benefit from a stronger evidence base and help steer policy formulation. To address this, a prioritisation exercise was undertaken to identify priority research questions concerning digital agriculture in the UK, but with a view to also informing international contexts. The prioritisation exercise uses an established and effective participatory methodology for capturing and ordering a wide range of views. The method involves identifying a large number of participants and eliciting an initial long list of research questions which is reduced and refined in subsequent voting stages to select the top priorities by theme. Participants were selected using purposive sampling and snowballing to represent a number of sectors, organisations, companies and disciplines across the UK. They were each invited to submit up to 10 questions according to certain criteria, and this resulted in 195 questions from a range of 40 participants (largely from England with some representation from Scotland and Wales). Preliminary analysis and clustering of these questions through iterative analysis identified seven themes as follows: data governance; data management; enabling use of data and technologies; understanding benefits and uptake of data and technologies; optimising data and technologies for performance; impacts of digital agriculture; and new collaborative arrangements. Subsequent stages of voting, using an online ranking exercise and a participant workshop for in-depth discussion, refined the questions to a total of 27 priority research questions categorised into 15 gold, 7 silver and 5 bronze, across the 7 themes. The questions significantly enrich and extend previous clustering and agenda setting using literature sources, and provide a range of new perspectives. The analysis highlights the interconnectedness of themes and questions, and proposes two nexus for future research: the different dimensions of value, and the social and institutional arrangements to support digitalisation in agriculture. These emphasise the importance of interdisciplinarity and transdisciplinarity, and the need to tackle the binary nature of current analytical frames. These new insights are equally relevant to contexts outside the UK. This paper highlights the need for research actions to inform policy, not only instrumentally by strengthening the evidence base, but also conceptually, to prompt new thinking. To our knowledge this methodology has not been previously applied to this topic.
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| 5. |
- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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| 6. |
- Ström, Peter, et al.
(författare)
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Pathologist-Level Grading of Prostate Biospies with Artificial intelligence
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test set comprising 1,631 biopsies from 245 men. We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics (ROC) and tumor extent predictions by correlating predicted millimeter cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI and the expert urological pathologists using Cohen's kappa. Results: The performance of the AI to detect and grade cancer in prostate needle biopsy samples was comparable to that of international experts in prostate pathology. The AI achieved an area under the ROC curve of 0.997 for distinguishing between benign and malignant biopsy cores, and 0.999 for distinguishing between men with or without prostate cancer. The correlation between millimeter cancer predicted by the AI and assigned by the reporting pathologist was 0.96. For assigning Gleason grades, the AI achieved an average pairwise kappa of 0.62. This was within the range of the corresponding values for the expert pathologists (0.60 to 0.73).
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