Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. Methods: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Background: Electronic Health Records hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Existing approaches focus mostly on structured data and a subset of single-domain outcomes. We explore how temporal modelling of patients from free text and structured data, using deep generative transformers can be used to forecast a wide range of future disorders, substances, procedures or findings. Methods: We present Foresight, a novel transformer-based pipeline that uses named entity recognition and linking tools to convert document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events such as disorders, substances, procedures and findings. We processed the entire free-text portion from three different hospital datasets totalling 811336 patients covering both physical and mental health. Findings: On tests in two UK hospitals (King's College Hospital, South London and Maudsley) and the US MIMIC-III dataset precision@10 0.68, 0.76 and 0.88 was achieved for forecasting the next disorder in a patient timeline, while precision@10 of 0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept. Foresight was also validated on 34 synthetic patient timelines by five clinicians and achieved relevancy of 97% for the top forecasted candidate disorder. As a generative model, it can forecast follow-on biomedical concepts for as many steps as required. Interpretation: Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials and clinical research to study the progression of disorders, simulate interventions and counterfactuals, and educational purposes.

When diagnosed at its earliest stage more than 9 in 10 people with bowel cancer will survive their disease for over five years compared with 1 in 10 when it's diagnosed late. The study is hoping to recruit over 2000 participants before September 2022. Colorectal cancer affects 1 in 15 men and 1 in 18 women in the UK with 16,600 deaths every year; it is the UK's second most deadly cancer. Bowel cancer starts when a polyp (or'adenoma') progresses to cancer, but it can be prevented if detected early enough. Colonoscopy is the'gold standard' assessment for bowel cancer and Adenoma Detection Rate (ADR) (which measures how many polyps the doctor removes) has a notable impact on bowel cancer outcomes.

January 31, 2020 – Zenzium, Ltd., announced today its participation in a groundbreaking trial in Greater Manchester which is to test cutting edge wearable technology in combination with Artificial Intelligence (AI) for patients who have received cancer treatment. Called, EMBRaCE, (Enhanced Monitoring for Better Recovery and Cancer Experience), the trial is a collaboration between Manchester University NHS Foundation Trust, The Christie NHS Foundation Trust, The University of Manchester, Aptus Clinical and Zenzium, Ltd. The trial has opened initially for blood cancer, lung, and colorectal cancer patients and will run across Greater Manchester. Using commercially available health sensors and devices in combination with AI could reveal digital fingerprints associated with vital signs and other clinical data that could allow doctors to assess the progress of their patients and potentially improve patient outcomes. The technologies under investigation include: • a smart ring, worn on any finger made by Oura Health • the Withings ScanWatch, a hybrid smartwatch • the Isansys system, which is worn on the chest • AI capabilities developed and provided by Zenzium The technologies can assess a range of vital signs, including electrocardiogram (ECG), heart rate, temperature, physical activity levels and sleep.

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect coronavirus using routine clinical data (blood tests, and vital signs). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this work, we examine two machine learning approaches, intended to predict a patient's COVID-19 status using routinely collected and readily available clinical data.

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect coronavirus using routine clinical data (blood tests, and vital signs). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this work, we examine two machine learning approaches, intended to predict a patient's COVID-19 status using routinely collected and readily available clinical data. We employ adversarial training to explore robust deep learning architectures that protect attributes related to demographic information about the patients. The two models we examine in this work are intended to preserve sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets from the Oxford University Hospitals, Bedfordshire Hospitals NHS Foundation Trust, University Hospitals Birmingham NHS Foundation Trust, and Portsmouth Hospitals University NHS Trust we train and test two neural networks that predict PCR test results using information from basic laboratory blood tests, and vital signs performed on a patients' arrival to hospital. We assess the level of privacy each one of the models can provide and show the efficacy and robustness of our proposed architectures against a comparable baseline. One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.

Digital health provider Babylon has launched its AI-powered triage tool in Rwanda to further digitise the country's healthcare system. The tool is now being used by Babylon (known locally as Babyl) call centre nurses in Rwanda to help them work more efficiently and make improved, faster decisions for their patients. It will help nurses ask patients the right questions, collect relevant information about a patient's symptoms and provide them with insights to help choose the correct triage path. If a follow-up appointment is required, the patient information collected on the triage call is passed on to the doctor, saving both the clinician and the patient time. Shivon Byamukama, CEO of Babyl Rwanda, said: "Rwandans have embraced digital healthcare that allows them to access clinicians from wherever they are. With the introduction of the AI triage tool in our call centre, we are effectively placing doctors' brains in the hands of our nurses in the digital triage."

The latest Digital Health News Industry round up includes details on a name change, a robotic surgical systems installation and the launch of digital pre-op assessments for one trust. The Midlands Partnership NHS Foundation Trust has selected Agilisys to develop a business intelligence strategy to enhance the trust's data and analytics capabilities. As a result, the trust will have a single business intelligence tool that can be used across the whole of Midlands Partnership NHS Foundation Trust (MPFT). Previously this information was used by just the Business Intelligence Team but by making it available trust-wide, regardless of skill-level, it will support massive growth in the trust's data warehouse. In turn, this will support MPFT's ambitions for HIMSS 7. The two organisations are now working together on delivering a roadmap that informs the trust's decisions on migrating its data to the cloud.

Title: Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence One sentence summary: An efficient and effective privacy-preserving AI framework is proposed for CT-based COVID-19 diagnosis, based on 9,573 CT scans of 3,336 patients, from 23 hospitals in China and the UK. Abstract Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health. MAIN TEXT Introduction As the gold standard for identifying COVID-19 carriers, reverse transcription-polymerase chain reaction (RT-PCR) is the primary diagnostic modality to detect viral nucleotide in specimens from cases with suspected infection. It has been reported that coronavirus carriers present certain radiological features in chest CTs, including ground-glass opacity, interlobular septal thickening, and consolidation, which can be exploited to identify COVID-19 cases.

Artificial intelligence (AI) has undoubtedly been a growing presence in the healthcare industry, shaving years and billions of pounds off drug development programmes, accurately predicting A&E influxes, and even detecting early signs of disease in patients years before it was thought possible. The field of neuroscience has been no exception to this wave of technological innovation, with exciting developments cropping up in recent months and years that could potentially revolutionise diagnoses, treatments, and outcomes for patients on a global scale. The term AI covers a field of computer science that is focused upon the simulation of human intelligence and computational processes. However, there are several subfields of AI technology currently being explored in neuroscience, including machine learning (ML) and deep learning (DL). AI covers all programming systems that can perform tasks which usually require human intelligence.