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Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

Background: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole‐slide images (WSIs) is a very time‐consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep‐learning (DL)‐based method for automatic curation of large pathology datasets with several slides per patient. Methods: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry‐stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro‐averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. Results: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994–0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979–0.985] and 0.875 [95% CI: 0.864–0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. Conclusion: Our findings show that using the low‐resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations. [ABSTRACT FROM AUTHOR]

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI), machine learning (ML) and deep learning (DL) are having a major impact on banking (FinTech), health (HealthTech), law (RegTech) and other sectors such as charitable fundraising (CharityTech). The pace of technological innovation and the ability of AI systems to think like human beings (simulate human intelligence), perform tasks independently, develop intelligence based on its own experiences and process layers of information to learn ever-complex representations of data (ML/DL) means that improvements in the rates at which this technology can undertake complex, technical and time-consuming tasks, identify people, objects, voices, patterns, etc., screen for 'problems' earlier, and provide solutions, provide astounding benefit in economic, political and social terms. The purpose of this paper is to explore advents in AI, ML and DL in the context of the regulatory compliance challenge faced by financial institutions in the United Kingdom (UK). Design/methodology/approach: The subject is explored through the analysis of data and domestic and international published literature. The first part of the paper summarises the context of current regulatory issues, the advents in deep learning, how financial institutions are currently using AI, and how AI could provide further technological solutions to regulatory compliance as of February 2023. Findings: It is suggested that UK financial institutions can further utilise AI, ML and DL as part of an armoury of solutions that ease the regulatory burden and achieve high levels of compliance success. Originality/value: To the best of the author's knowledge, this is the first study to specifically explore how AI, ML and DL can continue to assist UK financial institutions in meeting the regulatory compliance challenge and the opportunities provided for financial institutions by the metaverse. [ABSTRACT FROM AUTHOR]

Background: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings. Method: PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics. Results: The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies. Conclusion: The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology. Waiting lists for elective surgeries are rising in the UK. We conducted a scoping review with the aim to understand if artificial intelligence has the potential to improve the utilization of operating theatre resources through improved scheduling. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study is to demonstrate that with the use of artificial intelligence (AI) in computed tomography (CT), radiation doses of CT kidney-ureter-bladder (KUB) and CT urogram (CTU) can be reduced to less than that of X-ray KUB and CT KUB, respectively, while maintaining the good image quality. Materials and Methods: We reviewed all CT KUBs (n = 121) performed in September 2019 and all CTUs (n = 74) performed in December 2019 at our institution. The dose length product (DLP) of all CT KUBs and each individual phase of CTU were recorded. DLP of each scan done with new scanner (Canon Aquilion One Genesis with AiCE [CAOG]) which uses AI and deep learning reconstruction (DLR) were compared against traditional non-AI scanner (GE OPTIMA 660 [GEO-660]). We also compared DLPs of both scanners against the United Kingdom, National Diagnostic Reference Levels (NDRL) for CT. Results: One hundred and twenty-one patient's CT KUBs and 74 patient's CTUs were reviewed. For CT KUB group, the mean DLP of 81/121 scans done using AI/DLR scanner (CAOG) was 77.8 mGy cm (1.16 mSv), while the mean DLP of 40/121 CT KUB done with GEO-660 was 317.1 mGy cm (4.75 mSv). For CTU group, the mean DLP for 46/74 scans done using AI/DLR scanner (CAOG) was 401.9 mGy cm (6 mSv), compared to mean DLP of 1352.6 mGy cm (20.2 mSv) from GEO-660. Conclusion: We propose that CT scanners using AI/DLR method have the potential of reducing radiation doses of CT KUB and CTU to such an extent that it heralds the extinction of plain film XR KUB for follow-up of urinary tract stones. To the best of our knowledge, this is the first study comparing CT KUB and CTU doses from new scanners utilizing AI/DLR technology with traditional scanners using hybrid iterative reconstruction technology. Moreover, we have shown that this technology can markedly reduce the cumulative radiation burden in all urological patients undergoing CT examinations, whether this is CT KUB or CTU. [ABSTRACT FROM AUTHOR]

In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R2 above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts. [ABSTRACT FROM AUTHOR]

Background: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. Methods: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. Results: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. Conclusions: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD. [ABSTRACT FROM AUTHOR]

Objective: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans.Methods: We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time.Results: A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence.Conclusions: We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

Disinformation attacks that make use of social media platforms, e.g., the attacks orchestrated by the Russian "Internet Research Agency" during the 2016 U.S. Presidential election campaign and the 2016 Brexit referendum in the UK, have led to increasing demands from governmental agencies for AI tools that are capable of identifying such attacks in their earliest stages, rather than responding to them in retrospect. This research undertaken on behalf of the Canadian Armed Forces and Department of National Defence. Our ultimate objective is the development of an integrated set of machine-learning algorithms which will mobilize artificial intelligence to identify hostile disinformation activities in "near-real-time." Employing The Dark Crawler, the Posit Toolkit, TensorFlow (Deep Neural Networks), plus the Random Forest classifier and short-text classification programs known as LibShortText and LibLinear, we have analysed a wide sample of social media posts that exemplify the "fake news" that was disseminated by Russia's Internet Research Agency, comparing them to "real news" posts in order to develop an automated means of classification. [ABSTRACT FROM AUTHOR]

Significance of machine learning (ML), deep learning (DL) techniques and the availability of Electronic Health Records (EHR) has motivated the need of automated diagnosis system. Furthermore, this development has transformed the health care systems. Recently, several ML and DL models has been proposed for various diseases and has shown the significant outcomes as well. Unfortunately, Urinary tract infections (UTI) is among the minor diseases that is not investigated a lot interms of diagnosing using computation intelligence techniques. However, these models lack the reliability due to the black box nature of the highly complex logic model. Therefore, we attempt to develop an interpretable deep learning (DL) model for the diagnosis of UTI using the dataset of emergency department (ED) patients from UK. Several sets of experiments were conducted using complete dataset, reduced attribute set identified using recursive feature elimination (RFE) and using the attributes identified by the baseline study. The proposed DL model has improved the baseline study accuracy from 0.875 to 0.9275 for 184 feature and 0.859 to 0.943 for the reduced feature. Furthermore, the model has outperformed interms of sensitivity and specificity as well. Due to the data imbalance positive predicted value (PPV), negative predicted value (NPV) and Youden Index was also used for evaluating the performance of the model. The proposed DL model has achieved the highest outcome using 18 attributes selected with RFE technique. The proposed model will produce reliability in the diagnosis made by the model and provide confidence to the doctors to adopt the system in the real life. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods. [ABSTRACT FROM AUTHOR]

To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure–function mapping. Retrospective, cross-sectional database study. A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom. OCT and infrared reflectance (IR) optic disc imaging were paired with the closest VF within 7 days. EfficientNet B2 was used to train 2 single-modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the 2 model predictions. Pointwise mean absolute error (PMAE). A total of 5078 imaging scans to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 (0.438, 0.533) decibels (dB) compared with the IR image of the disc alone and 0.060 (0.047, 0.073) dB with to the OCT alone. The improvement with the policy fusion model was statistically significant (P < 0.0001). Occlusion masking shows that the DL models learned the correct structure–function mapping in a data-driven, feature agnostic fashion. The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure–function relationship in glaucoma. We used a large, real-world dataset to train a multimodal interpretable deep learning method to predict visual field sensitivity values in patients with glaucoma. [ABSTRACT FROM AUTHOR]

This study reviewed the status quo of research on machine learning (ML), including deep learning, in the rail industry. This study conducted a scientometric analysis and critical review of 640 papers selected from 12,675 web-crawled papers. The extensive and complex networks of topics, researchers, and countries were analyzed using the Louvain method, a co-occurrence keyword analysis, a degree centrality analysis, and other network analysis methods. The results indicate that the majority of studies of ML in the rail industry focused on maintenance activities and traffic management, and mainly targeted rolling stock, rails, and passengers. Overhead contact systems, including catenaries, are a high-demand objective for ML-based maintenance. Although analyses of tunnels and stations remain rare, passenger flow prediction, station air quality estimation, shield tunneling performance improvement, and ground settlement are areas of high importance. Geographically, China, the US, and the United Kingdom lead ML studies in the rail industry, and the level of collaboration is higher among European countries than among countries on other continents. Future challenges include ensuring the security and stability of ML, along with considering novel mindsets, the black-box effect, improvements in ML techniques, and resource overload when introducing ML technologies. [ABSTRACT FROM AUTHOR]

Objectives: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. Methods: An AI algorithm was evaluated on four video test datasets containing 173 polyps (35,114 polyp‐positive frames and 634,988 polyp‐negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by eight endoscopists (four independent, four trainees, according to the Joint Advisory Group on gastrointestinal endoscopy [JAG] standards in the UK). Results: In the first two video datasets, the algorithm achieved per‐polyp sensitivities of 100% and 98.9%. Per‐frame sensitivities were 84.1% and 85.2%. In the subtle dataset, the algorithm detected a significantly higher number of polyps (P < 0.0001), compared to JAG‐independent and trainee endoscopists, achieving per‐polyp sensitivities of 79.5%, 37.2% and 11.5%, respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. Conclusions: The AI based algorithm achieved high per‐polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer. [ABSTRACT FROM AUTHOR]

Background ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). Objective we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. Methods we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). Results in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42–1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69–3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31–1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14–1.69]) and 18% (HR = 1.18 [1.10–1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). Conclusions the DL-derived RetiAGE provides a novel, alternative approach to measure ageing. [ABSTRACT FROM AUTHOR]

• Individuals remain poorly predictable from population-level analyses of the brain. • The fidelity limits data scale, compute, and model flexibility impose are unknown. • We quantify these limits in multimodal, multitarget analyses across 23810 people. • We show a radical change in modelling regimes is needed for individual prediction. Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available. [ABSTRACT FROM AUTHOR]

Multiphase flowmeters are important to monitor oil wells, as they allow operators to obtain a real-time estimate of the production. However, due to its high installation cost, uncertainty, and reading errors, the oil and gas industry started to invest in virtual meters, mathematical models that can obtain the oil and gas flow forecast using the instrumentation already available in the well, such as pressure and temperature sensors. The purpose of this article is to develop a virtual multiphase flow predictor using artificial intelligence models such as Long Short-Term Memory (LSTM), Gate Recurrent Units (GRU), Multi-Layer Perceptron (MLP) neural networks and deep learning. As input data, this paper uses a public database available on the Oil and Gas Authority (UK) to train the models and demonstrate the possibility of predicting multiphase flow with a reasonable error margin, proving that the method is efficient in estimating well production rates. • In the oil industry, it is important to measure the production from each well. • A Virtual Meter is a low-cost alternative. It can measure multiphase flow without any additional equipment. • This paper proposes a Virtual Meter using neural networks such as MLP, LSTM, GRU and deep learning combinations. • Prediction performance was evaluated using the Oil and Gas Authority (OGA) public database and it shows promising results. • Experiments using a combination of multiple layers shows this type of prediction takes advantage of deep learning techniques. [ABSTRACT FROM AUTHOR]