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Abstract Background [18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task ‘TNMu’ classifier to classify the presence/absence of tumour, node, metastasis (‘TNM’) findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task (‘u’) to account for studies with ambiguous TNM status. Methods 2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches. Results We find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved. Conclusions Our ‘TNMu’ classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.

BackgroundPrevious studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development. The use of these technologies and infrastructural questions have been insufficiently addressed. Systems generally consist of sensors or software installed in smartphones or homes. However, no studies have attempted to use smart textiles, which are fabrics with integrated electronics. ObjectiveThis study aims to understand the design requirements for a smart textile loneliness monitoring system from the perspectives of older people and stakeholders. MethodsWe conducted co-design workshops with 5 users and 6 stakeholders to determine the design requirements for smart textile loneliness monitoring systems. We derived a preliminary product concept of the smart wearable and furniture system. Digital and physical models and a use case were evaluated in a focus group study with older people and stakeholders (n=7). ResultsThe results provided insights for designing systems that use smart textiles to monitor loneliness in older people and widen their use. The findings informed the general system, wearables and furniture, materials, sensor positioning, washing, sensor synchronization devices, charging, intervention, and installation and maintenance requirements. This study provided the first insight from a human-centered perspective into smart textile loneliness monitoring systems for older people. ConclusionsWe recommend more research on the intervention that links to the monitored loneliness in a way that addresses different needs to ensure its usefulness and value to people. Future systems must also reflect on questions of identification of system users and the available infrastructure and life circumstances of people. We further found requirements that included user cooperation, compatibility with other worn medical devices, and long-term durability.

Purpose Loneliness is a negative emotional state which is common in later life. The accumulative effects of loneliness have a significant impact on the physical and mental health of older adults. We aim to qualitatively explore the experiences of loneliness in later life and identify relevant behaviours and indicators which will inform novel methods of loneliness detection and intervention. Methods We conducted 60 semi-structured interviews with people aged 65 and over between September 2022 and August 2023. Data were analysed using a reflective thematic approach with early theme development on NVIVO software. Results Three themes were identified from the experiences of loneliness in older adults. 1) Unique responses to loneliness, including crying, increased eating or drinking and sleep difficulties, 2) Age-related losses, such as networks, roles, and abilities to engage in activities reducing over time and 3) Individual differences in overcoming loneliness, where strategies such as keeping busy and adopting a positive mindset were impacted by motivation and mood of older adults. Conclusion Distinct signs and relevant factors to loneliness in later life have been identified which can be detected by future sensing technologies. Findings of this in-depth qualitative study highlight that loneliness is a subjective experience requiring a holistic and person-centred approach to detection and intervention.

Abstract Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.

Background The brain reserve hypothesis posits that larger maximal lifetime brain growth (MLBG) may confer protection against physical disability in multiple sclerosis (MS). Larger MLBG as a proxy for brain reserve, has been associated with reduced progression of physical disability in patients with early MS; however, it is unknown whether this association remains once in the secondary progressive phase of MS (SPMS). Our aim was to assess whether larger MLBG is associated with decreased physical disability progression in SPMS.Methods We conducted a post hoc analysis of participants in the MS-Secondary Progressive Multi-Arm Randomisation Trial (NCT01910259), a multicentre randomised placebo-controlled trial of the neuroprotective potential of three agents in SPMS. Physical disability was measured by Expanded Disability Status Scale (EDSS), 9-hole peg test (9HPT) and 25-foot timed walk test (T25FW) at baseline, 48 and 96 weeks. MLBG was estimated by baseline intracranial volume (ICV). Multivariable time-varying Cox regression models were used to investigate the association between MLBG and physical disability progression.Results 383 participants (mean age 54.5 years, 298 female) were followed up over 96 weeks. Median baseline EDSS was 6.0 (range 4.0–6.5). Adjusted for covariates, larger MLBG was associated with a reduced risk of EDSS progression (HR 0.84,95% CI:0.72 to 0.99;p=0.04). MLBG was not independently associated with time to progression as measured by 9HPT or T25FW.Conclusion Larger MLBG is independently associated with physical disability progression over 96 weeks as measured by EDSS in SPMS. This suggests that MLBG as a proxy for brain reserve may continue to confer protection against disability when in the secondary progression phase of MS.Trail registration number NCT01910259.

Objectives This study aims to assess the feasibility to perform transoral robotic surgery (TORS) with a new robotic platform, the Versius Surgical System (CMR Surgical, UK) in a preclinical cadaveric setting in accordance to stage 0 of the IDEAL-D framework.Design IDEAL stage 0 preclinical assessment of the Versius Robotic System in TORS in human cadavers.Setting All procedures were performed in a simulated operating theatre environment at a UK surgical training centre.Participants 11 consultant head and neck surgeons from the UK, mainland Europe and the USA took part in TORS procedures on six human cadavers.Interventions 3 key index procedures were assessed that represent the core surgical workload of TORS: lateral oropharyngectomy, tongue base resection and partial supraglottic laryngectomy.Main outcome measures The primary outcome was the successful completion of each surgical procedure. Secondary outcomes included the optimisation of system setup, instrumentation and surgeon-reported outcomes for feasibility of each component procedural step.Results 33 cadaveric procedures were performed and 32 were successfully completed. One supraglottic laryngectomy was not fully completed due to issues dividing the epiglottic cartilage with available instrumentation. Surgeon-reported outcomes met the minimal level of feasibility in all procedures and a consensus that it is feasible to perform TORS with Versius was reached. Available instrumentation was not representative of other robotic platforms used in TORS and further instrument optimisation is recommended before wider dissemination.Conclusions It is feasible to perform TORS with the Versius Surgical System (CMR Surgical) within a pre-clinical cadaveric setting. Clinical evaluation is needed and appropriate with the system. Further instrument development and optimisation is desirable.

Abstract Scalar translocation is a severe form of intra-cochlear trauma during cochlear implant (CI) electrode insertion. This study explored the hypothesis that the dimensions of the cochlear basal turn and orientation of its inferior segment relative to surgically relevant anatomical structures influence the scalar translocation rates of a pre-curved CI electrode. In a cohort of 40 patients implanted with the Advanced Bionics Mid-Scala electrode array, the scalar translocation group (40%) had a significantly smaller mean distance A of the cochlear basal turn (p

Abstract Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

Abstract 3D imaging technology is becoming more prominent every day. However, more validation is needed to understand the actual benefit of 3D versus conventional 2D vision. This work quantitatively investigates whether experts benefit from 3D vision during minimally invasive fetoscopic spina bifida (fSB) repair. A superiority study was designed involving one expert team ( $$>18$$ > 18 procedures prior) who performed six 2D and six 3D fSB repair simulations in a high-fidelity animal training model, using 3-port access. The 6D motion of the instruments was recorded. Among the motion metrics are total path length, smoothness, maximum speed, the modified Spectral Arc Length (SPARC), and Log Dimensionless Jerk (LDLJ). The primary clinical outcome is operation time (power 90%, 5% significance) using Sealed Envelope Ltd. 2012. Secondary clinical outcomes are water tightness of the repair, CO $$_2$$ 2 insufflation volume, and OSATS score. Findings show that total path length and LDLJ are considerably different. Operation time during 3D vision was found to be significantly shorter compared to 2D vision ( $$113\pm 9$$ 113 ± 9 vs. $$149\pm 19$$ 149 ± 19 min; p $$=$$ = 0.026). These results suggest enhanced performance with 3D vision during interrupted suturing in fetoscopic SBA repair. To confirm these results, a larger-scale follow-up study involving multiple experts and novice surgeons is recommended.

Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardized MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy. We acquired and publicly release a curated multi-center routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (n = 124) and T2-weighted (T2w) (n = 363) images were included and the VS manually annotated. Segmentations were produced and verified in an iterative process: (1) initial segmentations by a specialized company; (2) review by one of three trained radiologists; and (3) validation by an expert team. Inter- and intra-observer reliability experiments were performed on a subset of the dataset. A state-of-the-art deep learning framework was used to train segmentation models for VS. Model performance was evaluated on a MC-RC hold-out testing set, another public VS datasets, and a partially public dataset. The generalizability and robustness of the VS deep learning segmentation models increased significantly when trained on the MC-RC dataset. Dice similarity coefficients (DSC) achieved by our model are comparable to those achieved by trained radiologists in the inter-observer experiment. On the MC-RC testing set, median DSCs were 86.2(9.5) for ceT1w, 89.4(7.0) for T2w, and 86.4(8.6) for combined ceT1w+T2w input images. On another public dataset acquired for Gamma Knife stereotactic radiosurgery our model achieved median DSCs of 95.3(2.9), 92.8(3.8), and 95.5(3.3), respectively. In contrast, models trained on the Gamma Knife dataset did not generalize well as illustrated by significant underperformance on the MC-RC routine MRI dataset, highlighting the importance of data variability in the development of robust VS segmentation models. The MC-RC dataset and all trained deep learning models were made available online.

Abstract Whilst most individuals with SARS-CoV-2 infection have relatively mild disease, managed in the community, it was noted early in the pandemic that individuals with cardiovascular risk factors were more likely to experience severe acute disease, requiring hospitalisation. As the pandemic has progressed, increasing concern has also developed over long symptom duration in many individuals after SARS-CoV-2 infection, including among the majority who are managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. Here, we examine post-illness metabolomic profiles, using nuclear magnetic resonance (Nightingale Health Oyj), and gut-microbiome profiles, using shotgun metagenomic sequencing (Illumina Inc), in 2561 community-dwelling participants with SARS-CoV-2. Illness duration ranged from asymptomatic (n = 307) to Post-COVID Syndrome (n = 180), and included participants with prolonged non-COVID-19 illnesses (n = 287). We also assess a pre-established metabolomic biomarker score, previously associated with hospitalisation for both acute pneumonia and severe acute COVID-19 illness, for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, including biomarkers such as fatty acids and cholesterol, was associated with longer duration of illness, both in individuals with and without SARS-CoV-2 infection. Greater values of a pre-existing metabolomic biomarker score also associated with longer duration of illness, regardless of SARS-CoV-2 infection. We found no association between illness duration and gut microbiome profiles in convalescence. This highlights the potential role of cardiometabolic dysfunction in relation to the experience of long duration symptoms after symptoms of acute infection, both COVID-19 as well as other illnesses.

Loneliness and social isolation are prevalent concerns among older adults and can lead to negative health consequences and a reduced lifespan. New technologies are increasingly being developed to help address loneliness and social isolation in older adults, including monitoring systems, social networks, robots, companions, smart televisions, augmented reality (AR) and virtual reality (VR) applications. This systematic review maps human-centered design (HCD) and user-centered design (UCD) approaches, human needs, and contextual factors considered in current technological interventions designed to address the problems of loneliness and social isolation in older adults. We conducted a scoping review and in-depth examination of 98 papers through a qualitative content analysis. We found 12 studies applying either an HCD or UCD approach and observed strengths in continuous user involvement and implementation in field studies but limitations in participant inclusion criteria and methodological reporting. We also observed the consideration of important human needs and contextual factors. However, more research is needed on stakeholder perspectives, the functioning of applications in different housing environments, as well as studies that include diverse socio-economic groups.

Abstract Background The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support. Methods A set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We trained and evaluated out-of-sample an array of extreme gradient boosted trees-based predictive models of incrementally greater numbers of investigational modalities and modelled features. Both linear and non-linear associations with investigational features were quantified. Results Predictive models of mortality showed progressively increasing fidelity with greater numbers of modelled modalities and dimensions. The area under the receiver operating characteristic curve rose from 0.67 (sd = 0.078) for age and sex to 0.874 (sd = 0.046) for the most comprehensive model. Extracranial bone and soft tissue features contributed more than intracranial features towards long-term mortality prediction. The anterior cingulate and angular gyri, and serum albumin, were the greatest intracranial and biochemical model contributors respectively. Conclusions High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.

IntroductionHyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery.MethodsA lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350–1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study.ResultsFocusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery.DiscussionThis work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

BackgroundAutonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment.ObjectiveTo determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions.MethodsPubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259.ResultsFour hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and in-silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms “idealized” for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation.ConclusionDespite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.Systematic review registrationidentifier: CRD42023392259.

Summary: Background: Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Methods: Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. Findings: 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = −0.14 standard deviations, SDs, 95% confidence intervals, CI: −0.21, −0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, β = −0.22 SDs, 95% CI: −0.35, −0.09). Effects were comparable to hospital presentation during illness (N = 281, β = −0.31 SDs, 95% CI: −0.44, −0.18), and 10 years age difference (60–70 years vs. 50–60 years, β = −0.21 SDs, 95% CI: −0.30, −0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Interpretation: Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Funding: Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency.

Summary: Background: Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. Methods: In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18–100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. Findings: We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. Interpretation: Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. Funding: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.

Abstract The Delta (B.1.617.2) variant was the predominant UK circulating SARS-CoV-2 strain between May and December 2021. How Delta infection compares with previous variants is unknown. This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly the predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. 3581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta versus Alpha infection (including fever, sore throat, and headache) and some vice versa (dyspnoea). Symptom burden in the first week was higher with Delta versus Alpha infection; however, the odds of any given symptom lasting ≥ 7 days was either lower or unchanged. Illness duration ≥ 28 days was lower with Delta versus Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.49) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly reduced the risk of Delta infection (by 69-84%). We conclude that COVID-19 from Delta or Alpha infections is similar. The Delta variant is more transmissible than Alpha; however, current vaccines showed good efficacy against disease. This research framework can be useful for future comparisons with new emerging variants.

Abstract COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms.

Abstract INTRODUCTION The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian‐mixture‐modelling–derived cutpoints for Aβ PET positivity were converted. RESULTS The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM‐based Centiloids. Linear adjustment produced a WM‐based cutpoint of 18.1. DISCUSSION Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results. Centiloid values can be influenced by differences in acquisition. We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort. Whole cerebellum referenced values could be reliably transformed to Centiloids. White matter referenced values may be less generalizable between datasets.

The authors show differences in self-reported vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and in the United Kingdom during the initial phase of the COVID-19 vaccine rollout.

Single port access surgery has brought many advantages and new challenges to the field of minimally invasive surgery. In principle, many of these challenges can be addressed through robotics. Continuum robots in particular have seen a surge in interest in recent years for their miniaturisation potential. However, many applications with these robots have been developed for contactless or low-force tasks. This paper tackles several barriers to high-force tasks with continuum robots such as suturing for Open Spina Bifida repair. A novel continuum robot end-effector is proposed. The end-effector is composed of a distal bending segment and hybrid gripper. First, the novel hybrid gripper specifically designed for continuum robots is introduced. The hybrid gripper can function both as a general surgical gripper and as a needle driver while overall keeping the required input force limited. Second, an optimisation of the continuum structure of the distal bending segment is conducted. The objective of this optimisation is to sustain high tip forces – required by the said high-force contact tasks – while keeping deformations acceptable. Third, a quasi-static model that predicts the coupled effect of bending and gripping is derived. This model predicts the deformation of the continuum structure that is induced by the gripping action itself. It could hence be used to compensate for such deformation and establish a stable grip with the otherwise flexible instruments. Finally, experiments are conducted to validate the new designs and the model. The hybrid gripper has a diameter of 3 . 4 mm, a length of 15 . 5 mm and can hold securely a needle with a 4 N input force, outputting a 12 N gripping force. The optimised bending segment is validated for Open Spina Bifida suture: it can bend freely up to 90° and it can support a 1 N tip load up to a 35° bending angle. The bending and gripper model error varies between 1% and 14.55%, yielding useful results to predict the shape of the bending segment and potentially compensate for the disturbance created by actuating the gripper.

Abstract The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.

Abstract The development of outcome measures that can track the recovery of reinnervated muscle would benefit the clinical investigation of new therapies which hope to enhance peripheral nerve repair. The primary objective of this study was to assess the validity of volumetric Magnetic Resonance Imaging (MRI) as an outcome measure of muscle reinnervation by testing its reproducibility, responsiveness and relationship with clinical indices of muscular function. Over a 3-year period 25 patients who underwent nerve transfer to reinnervate elbow flexor muscles were assessed using intramuscular electromyography (EMG) and MRI (median post-operative assessment time of 258 days, ranging from 86 days pre-operatively to 1698 days post- operatively). Muscle power (Medical Research Council (MRC) grade) and Stanmore Percentage of Normal Elbow Assessment (SPONEA) assessment was also recorded for all patients. Sub-analysis of peak volitional force (PVF), muscular fatigue and co-contraction was performed in those patients with MRC > 3. The responsiveness of each parameter was compared using Pearson or Spearman correlation. A Hierarchical Gaussian Process (HGP) was implemented to determine the ability of volumetric MRI measurements to predict the recovery of muscular function. Reinnervated muscle volume per unit Body Mass Index (BMI) demonstrated good responsiveness (R2 = 0.73, p

Abstract Magnetic resonance imaging (MRI) assessment of fetal blood oxygen saturation (SO2) can transform the clinical management of high‐risk pregnancies affected by fetal growth restriction (FGR). Here, a novel MRI method assesses the feasibility of identifying normally grown and FGR fetuses in sheep and is then applied to humans. MRI scans are performed in pregnant ewes at 110 and 140 days (term = 150d) gestation and in pregnant women at 28+3 ± 2+5 weeks to measure feto‐placental SO2. Birth weight is collected and, in sheep, fetal blood SO2 is measured with a blood gas analyzer (BGA). Fetal arterial SO2 measured by BGA predicts fetal birth weight in sheep and distinguishes between fetuses that are normally grown, small for gestational age, and FGR. MRI feto‐placental SO2 in late gestation is related to fetal blood SO2 measured by BGA and body weight. In sheep, MRI feto‐placental SO2 in mid‐gestation is related to fetal SO2 later in gestation. MRI feto‐placental SO2 distinguishes between normally grown and FGR fetuses, as well as distinguishing FGR fetuses with and without normal Doppler in humans. Thus, a multi‐compartment placental MRI model detects low placental SO2 and distinguishes between small hypoxemic fetuses and normally grown fetuses.

Abstract There has been a significant rise in robotic trajectory guidance devices that have been utilised for stereotactic neurosurgical procedures. These devices have significant costs and associated learning curves. Previous studies reporting devices usage have not undertaken prospective parallel-group comparisons before their introduction, so the comparative differences are unknown. We study the difference in stereoelectroencephalography electrode implantation time between a robotic trajectory guidance device (iSYS1) and manual frameless implantation (PAD) in patients with drug-refractory focal epilepsy through a single-blinded randomised control parallel-group investigation of SEEG electrode implantation, concordant with CONSORT statement. Thirty-two patients (18 male) completed the trial. The iSYS1 returned significantly shorter median operative time for intracranial bolt insertion, 6.36 min (95% CI 5.72–7.07) versus 9.06 min (95% CI 8.16–10.06), p = 0.0001. The PAD group had a better median target point accuracy 1.58 mm (95% CI 1.38–1.82) versus 1.16 mm (95% CI 1.01–1.33), p = 0.004. The mean electrode implantation angle error was 2.13° for the iSYS1 group and 1.71° for the PAD groups (p = 0.023). There was no statistically significant difference for any other outcome. Health policy and hospital commissioners should consider these differences in the context of the opportunity cost of introducing robotic devices. Trial registration: ISRCTN17209025 ( https://doi.org/10.1186/ISRCTN17209025 ).

Estimating the effectiveness of COVID-19 control measures requires large prospective data including symptoms and personal risk factors. Here, the authors used data from smartphone-based application and found that individual face mask use was associated with a 64% reduced risk of COVID-19 symptoms.

Abstract Objective To assess associations between head injury (HI) with loss of consciousness (LOC), ageing and markers of later‐life cerebral pathology; and to explore whether those effects may help explain subtle cognitive deficits in dementia‐free individuals. Methods Participants (n = 502, age = 69–71) from the 1946 British Birth Cohort underwent cognitive testing (subtests of Preclinical Alzheimer Cognitive Composite), 18F‐florbetapir Aβ‐PET and MR imaging. Measures include Aβ‐PET status, brain, hippocampal and white matter hyperintensity (WMH) volumes, normal appearing white matter (NAWM) microstructure, Alzheimer’s disease (AD)‐related cortical thickness, and serum neurofilament light chain (NFL). LOC HI metrics include HI occurring: (i) >15 years prior to the scan (ii) anytime up to age 71. Results Compared to those with no evidence of an LOC HI, only those reporting an LOC HI>15 years prior (16%, n = 80) performed worse on cognitive tests at age 69–71, taking into account premorbid cognition, particularly on the digit‐symbol substitution test (DSST). Smaller brain volume (BV) and adverse NAWM microstructural integrity explained 30% and 16% of the relationship between HI and DSST, respectively. We found no evidence that LOC HI was associated with Aβ load, hippocampal volume, WMH volume, AD‐related cortical thickness or NFL (all p > 0.01). Interpretation Having a LOC HI aged 50’s and younger was linked with lower later‐life cognitive function at age ~70 than expected. This may reflect a damaging but small impact of HI; explained in part by smaller BV and different microstructure pathways but not via pathology related to AD (amyloid, hippocampal volume, AD cortical thickness) or ongoing neurodegeneration (serum NFL).

Abstract Open fetal surgery for spina bifida (SB) is safe and effective yet invasive. The growing interest in fetoscopic SB repair (fSB-repair) prompts the need for appropriate training. We aimed to develop and validate a high-fidelity training model for fSB-repair. fSB-repair was simulated in the abdominal cavity and on the stomach of adult rabbits. Laparoscopic fetal surgeons served either as novices (n = 2) or experts (n = 3) based on their experience. Technical performance was evaluated using competency Cumulative Sum (CUSUM) analysis and the group splitting method. Main outcome measure for CUSUM competency was a composite binary outcome for surgical success, i.e. watertight repair, operation time ≤ 180 min and Objective-Structured-Assessment-of-Technical-Skills (OSATS) score ≥ 18/25. Construct validity was first confirmed since competency levels of novices and experts during their six first cases using both methods were significantly different. Criterion validity was also established as 33 consecutive procedures were needed for novices to reach competency using learning curve CUSUM, which is a number comparable to that of clinical fSB-repair. Finally, we surveyed expert fetal surgeons worldwide to assess face and content validity. Respondents (26/49; 53%) confirmed it with ≥ 71% of scores for overall realism ≥ 4/7 and usefulness ≥ 3/5. We propose to use our high-fidelity model to determine and shorten the learning curve of laparoscopic fetal surgeons and retain operative skills.

Abstract We tested whether pregnant and non-pregnant women differ in COVID-19 symptom profile and severity, and we extended previous investigations on hospitalized pregnant women to those who did not require hospitalization. Two female community-based cohorts (18–44 years) provided longitudinal (smartphone application, N = 1,170,315, n = 79 pregnant tested positive) and cross-sectional (web-based survey, N = 1,344,966, n = 134 pregnant tested positive) data, prospectively collected through self-participatory citizen surveillance in UK, Sweden and USA. Pregnant and non-pregnant were compared for frequencies of events, including SARS-CoV-2 testing, symptoms and hospitalization rates. Multivariable regression was used to investigate symptoms severity and comorbidity effects. Pregnant and non-pregnant women positive for SARS-CoV-2 infection were not different in syndromic severity, except for gastrointestinal symptoms. Pregnant were more likely to have received testing, despite reporting fewer symptoms. Pre-existing lung disease was most closely associated with syndromic severity in pregnant hospitalized. Heart and kidney diseases and diabetes increased risk. The most frequent symptoms among non-hospitalized women were anosmia [63% pregnant, 92% non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant who were hospitalized. Consistent with observations in non-pregnant populations, lung disease and diabetes were associated with increased risk of more severe SARS-CoV-2 infection during pregnancy.

Summary: Background: We aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and adolescents (CA) in the UK during periods of Delta and Omicron variant predominance. Methods: In this prospective longitudinal cohort study, we analysed data from 115,775 CA aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CA with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CA, and post-vaccination side-effects. Findings: Between August 5, 2021 and February 14, 2022, 25,971 UK CA aged 12-17 years received one dose of BNT162b2 vaccine. The probability of testing positive for infection diverged soon after vaccination, and was lower in CA with prior SARS-CoV-2 infection. Vaccination reduced proxy-reported infection risk (-80·4% (95% CI -0·82 -0·78) and -53·7% (95% CI -0·62 -0·43) at 14–30 days with Delta and Omicron variants respectively, and -61·5% (95% CI -0·74 -0·44) and -63·7% (95% CI -0·68 -0.59) after 61–90 days). Vaccinated CA who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CA; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CA. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved within few days (3 days in most cases). Interpretation: One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CA aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CA also had generally mild disease. Overall, vaccination was well-tolerated. Funding: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer's Society, and ZOE Limited.

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework’s ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.

Abstract Background There is increasing interest in improving understanding of the timing and nature of early neurodegeneration in Alzheimer’s disease (AD) and developing methods to measure this in vivo. Autosomal dominant familial Alzheimer’s disease (FAD) provides the opportunity for investigation of presymptomatic change. We assessed early microstructural breakdown of cortical grey matter in FAD with diffusion-weighted MRI. Methods Diffusion-weighted and T1-weighed MRI were acquired in 38 FAD mutation carriers (17 symptomatic, 21 presymptomatic) and 39 controls. Mean diffusivity (MD) was calculated for six cortical regions previously identified as being particularly vulnerable to FAD-related neurodegeneration. Linear regression compared MD between symptomatic and presymptomatic carriers and controls, adjusting for age and sex. Spearman coefficients assessed associations between cortical MD and cortical thickness. Spearman coefficients also assessed associations between cortical MD and estimated years to/from onset (EYO). Across mutation carriers, linear regression assessed associations between MD and EYO, adjusting for cortical thickness. Results Compared with controls, cortical MD was higher in symptomatic mutation carriers (mean ± SD CDR = 0.88 ± 0.39) for all six regions (p

Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance

Summary: The value of biomedical research—a $1.7 trillion annual investment—is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation—as indexed by inclusion in patents, guidelines, or policy documents—from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990–2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC] > 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential. The bigger picture: The relationship of scientific activity to real-world impact is hard to describe and even harder to quantify. Analyzing 43.3 million biomedical papers from 1990–2019, we show that deep learning models of publication, title, and abstract content can predict inclusion of a scientific paper in a patent, guideline, or policy document. We show that the best of these models, incorporating the richest information, substantially outperforms traditional metrics of paper success—citations per year—and transfers to the task of predicting Nobel Prize-preceding papers. If judgments of the translational potential of science are to be based on objective metrics, then complex models of paper content should be preferred over citations. Our approach is naturally extensible to richer scientific content and diverse measures of impact. Its wider application could maximize the real-world benefits of scientific activity in the biomedical realm and beyond.

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility.Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 ‘radiologically normal for age’ head MRI examinations from two large UK hospitals for model training and testing (age range = 18–95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy ‘excessive for age’. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001).Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

Many keyhole interventions rely on bi-manual handling of surgical instruments, forcing the main surgeon to rely on a second surgeon to act as a camera assistant. In addition to the burden of excessively involving surgical staff, this may lead to reduced image stability, increased task completion time and sometimes errors due to the monotony of the task. Robotic endoscope holders, controlled by a set of basic instructions, have been proposed as an alternative, but their unnatural handling may increase the cognitive load of the (solo) surgeon, which hinders their clinical acceptance. More seamless integration in the surgical workflow would be achieved if robotic endoscope holders collaborated with the operating surgeon via semantically rich instructions that closely resemble instructions that would otherwise be issued to a human camera assistant, such as “focus on my right-hand instrument.” As a proof of concept, this paper presents a novel system that paves the way towards a synergistic interaction between surgeons and robotic endoscope holders. The proposed platform allows the surgeon to perform a bimanual coordination and navigation task, while a robotic arm autonomously performs the endoscope positioning tasks. Within our system, we propose a novel tooltip localization method based on surgical tool segmentation and a novel visual servoing approach that ensures smooth and appropriate motion of the endoscope camera. We validate our vision pipeline and run a user study of this system. The clinical relevance of the study is ensured through the use of a laparoscopic exercise validated by the European Academy of Gynaecological Surgery which involves bi-manual coordination and navigation. Successful application of our proposed system provides a promising starting point towards broader clinical adoption of robotic endoscope holders.

Multimode fibres (MMFs) are becoming increasingly attractive in optical endoscopy as they promise to enable unparallelled miniaturisation, spatial resolution and cost. However, high-speed imaging with wavefront shaping has been challenging. Here, we report the development of a video-rate dual-modal photoacoustic (PA) and fluorescence microscopy probe with a high-speed digital micromirror device (DMD) towards forward-viewing endomicroscopy. Optimal DMD patterns were obtained using a real-valued intensity transmission matrix algorithm to raster-scan a 1.5 μm-diameter focused beam at the distal fibre tip for imaging. The PA imaging speed and spatial resolution were varied from ∼2 to 57 frames per second and from 1.7 to 3 μm, respectively. Further, high-fidelity PA images of carbon fibres and mouse red blood cells were acquired at unprecedented speed. The capability of dual-modal imaging was demonstrated with phantoms. We anticipate that with further miniaturisation of the ultrasound detector, this probe could be integrated into medical needles to guide minimally invasive procedures.

ObjectiveThe Koos grading scale is a frequently used classification system for vestibular schwannoma (VS) that accounts for extrameatal tumor dimension and compression of the brain stem. We propose an artificial intelligence (AI) pipeline to fully automate the segmentation and Koos classification of VS from MRI to improve clinical workflow and facilitate patient management.MethodsWe propose a method for Koos classification that does not only rely on available images but also on automatically generated segmentations. Artificial neural networks were trained and tested based on manual tumor segmentations and ground truth Koos grades of contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) MR images from subjects with a single sporadic VS, acquired on a single scanner and with a standardized protocol. The first stage of the pipeline comprises a convolutional neural network (CNN) which can segment the VS and 7 adjacent structures. For the second stage, we propose two complementary approaches that are combined in an ensemble. The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons.ResultsEligible patients (n = 308) were pseudo-randomly split into 5 groups to evaluate the model performance with 5-fold cross-validation. The weighted macro-averaged mean absolute error (MA-MAE), weighted macro-averaged F1 score (F1), and accuracy score of the ensemble model were assessed on the testing sets as follows: MA-MAE = 0.11 ± 0.05, F1 = 89.3 ± 3.0%, accuracy = 89.3 ± 2.9%, which was comparable to the average performance of two neurosurgeons: MA-MAE = 0.11 ± 0.08, F1 = 89.1 ± 5.2, accuracy = 88.6 ± 5.8%. Inter-rater reliability was assessed by calculating Fleiss' generalized kappa (k = 0.68) based on all 308 cases, and intra-rater reliabilities of annotator 1 (k = 0.95) and annotator 2 (k = 0.82) were calculated according to the weighted kappa metric with quadratic (Fleiss-Cohen) weights based on 15 randomly selected cases.ConclusionsWe developed the first AI framework to automatically classify VS according to the Koos scale. The excellent results show that the accuracy of the framework is comparable to that of neurosurgeons and may therefore facilitate management of patients with VS. The models, code, and ground truth Koos grades for a subset of publicly available images (n = 188) will be released upon publication.

Racial/ethnic minorities have been disproportionately impacted by COVID-19. The effects of COVID-19 on the long-term mental health of minorities remains unclear. To evaluate differences in odds of screening positive for depression and anxiety among various racial and ethnic groups during the latter phase of the COVID-19 pandemic, we performed a cross-sectional analysis of 691,473 participants nested within the prospective smartphone-based COVID Symptom Study in the United States (U.S.) and United Kingdom (U.K). from February 23, 2021 to June 9, 2021. In the U.S. (n=57,187), compared to White participants, the multivariable odds ratios (ORs) for screening positive for depression were 1·16 (95% CI: 1·02 to 1·31) for Black, 1·23 (1·11 to 1·36) for Hispanic, and 1·15 (1·02 to 1·30) for Asian participants, and 1·34 (1·13 to 1·59) for participants reporting more than one race/other even after accounting for personal factors such as prior history of a mental health disorder, COVID-19 infection status, and surrounding lockdown stringency. Rates of screening positive for anxiety were comparable. In the U.K. (n=643,286), racial/ethnic minorities had similarly elevated rates of positive screening for depression and anxiety. These disparities were not fully explained by changes in leisure time activities. Racial/ethnic minorities bore a disproportionate mental health burden during the COVID-19 pandemic. These differences will need to be considered as health care systems transition from prioritizing infection control to mitigating long-term consequences.

ObjectiveMonitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies.MethodsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018–01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965).ResultsEighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC.ConclusionML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.

Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. We analysed a cohort of untested symptomatic app users (N = 1,237), nested in the Zoe COVID Symptom Study (Zoe, N = 4,394,948); and symptomatic respondents who wanted, but did not have a test (N = 1,956), drawn from a University of Maryland survey administered to Facebook users (The Global COVID-19 Trends and Impact Survey [CTIS], N = 775,746). The proportion tested among individuals with incident test-qualifying symptoms rose from ~20% to ~75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (72.9% vs 84.6% p

Background: Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. Methods: We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. Findings: Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). Interpretation: Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. Funding: UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited.

Photoacoustic endoscopy (PAE) can provide 3D functional, molecular and structural information of tissue deep inside the human body, and thus could be well suited for guiding minimally invasive procedures such as tumour biopsy and fetal surgery. One of the major challenges in the development of miniature PAE probes, in particular, forward-viewing PAE probes, is the integration of a sensitive and broadband ultrasound sensor with the light delivery and scanning system into a small footprint. In this work, we developed a forward-viewing PAE probe enabling optical-resolution microscopy imaging based on a transparent ultrasound sensor coated on the distal end of a multimode optical fibre. The transparent sensor comprised a transparent polyvinylidene fluoride (PVDF) thin film coated with indium tin oxide (ITO) electrodes with a diameter of 2 mm. Excitation laser light was focused and raster-scanned across the facet of the probe tip through the multimode fibre and the PVDF-ITO thin film via wavefront shaping. The sensor had an optical transmission rate of 55–72% in the wavelength range of 400 to 800 nm, a centre frequency of 17.5 MHz and a −10 dB bandwidth of 25 MHz. Singular value decomposition was used to remove a prominent trigger-induced noise, which enabled imaging close to the probe tip with an optically defined lateral resolution of 2 µm. The performance of the imaging probe was demonstrated by obtaining high-fidelity photoacoustic microscopy images of carbon fibres. With further optimisation of the sensitivity, the probe promises to guide minimally invasive procedures by providing in situ, in vivo characterisation of tissue.