Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection

Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line computed tomography (CT) or magnetic resonance (MR) neuroimaging. A bivariate random effects model was used for meta-analysis where appropriate. This study was registered on PROSPERO as CRD42021269563. Results: Out of 42,870 records screened, and 5734 potentially eligible full texts, only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies and 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial hemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% confidence interval [CI] 0.85–0.94) and 0.90 (95% CI 0.83–0.95), respectively. Other AI studies using CT and MRI detected target conditions other than hemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation

Warm Water and Cool Nests Are Best. How Global Warming Might Influence Hatchling Green Turtle Swimming Performance

For sea turtles nesting on beaches surrounded by coral reefs, the most important element of hatchling recruitment is escaping predation by fish as they swim across the fringing reef, and as a consequence hatchlings that minimize their exposure to fish predation by minimizing the time spent crossing the fringing reef have a greater chance of surviving the reef crossing. One way to decrease the time required to cross the fringing reef is to maximize swimming speed. We found that both water temperature and nest temperature influence swimming performance of hatchling green turtles, but in opposite directions. Warm water increases swimming ability, with hatchling turtles swimming in warm water having a faster stroke rate, while an increase in nest temperature decreases swimming ability with hatchlings from warm nests producing less thrust per stroke

Imaging in patients with glioblastoma: A national cohort study

Background Glioblastoma is the most common malignant brain tumor in adults and has a poor prognosis. This cohort of patients is diverse and imaging is vital to formulate treatment plans. Despite this, there is relatively little data on patterns of use of imaging and imaging workload in routine practice. Methods We examined imaging patterns for all patients aged 15–99 years resident in England who were diagnosed with a glioblastoma between 1st January 2013 and 31st December 2014. Patients without imaging and death-certificate-only registrations were excluded. Results The analytical cohort contained 4,307 patients. There was no significant variation in pre- or postdiagnostic imaging practice by sex or deprivation quintile. Postdiagnostic imaging practice was varied. In the group of patients who were treated most aggressively (surgical debulking and chemoradiation) and were MRI compatible, only 51% had a postoperative MRI within 72 hours of surgery. In patients undergoing surgery who subsequently received radiotherapy, only 61% had a postsurgery and preradiotherapy MRI. Conclusions Prediagnostic imaging practice is uniform. Postdiagnostic imaging practice was variable. With increasing evidence and clearer recommendations regarding debulking surgery and planning radiotherapy imaging, the reason for this is unclear and will form the basis of further work

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images

Overcoming challenges of translating deep- learning models for glioblastoma: the ZGBM consortium

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 T 1-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

Chronic psychosocial and financial burden accelerates 5-year telomere shortening: findings from the Coronary Artery Risk Development in Young Adults Study.

Leukocyte telomere length, a marker of immune system function, is sensitive to exposures such as psychosocial stressors and health-maintaining behaviors. Past research has determined that stress experienced in adulthood is associated with shorter telomere length, but is limited to mostly cross-sectional reports. We test whether repeated reports of chronic psychosocial and financial burden is associated with telomere length change over a 5-year period (years 15 and 20) from 969 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a longitudinal, population-based cohort, ages 18-30 at time of recruitment in 1985. We further examine whether multisystem resiliency, comprised of social connections, health-maintaining behaviors, and psychological resources, mitigates the effects of repeated burden on telomere attrition over 5 years. Our results indicate that adults with high chronic burden do not show decreased telomere length over the 5-year period. However, these effects do vary by level of resiliency, as regression results revealed a significant interaction between chronic burden and multisystem resiliency. For individuals with high repeated chronic burden and low multisystem resiliency (1 SD below the mean), there was a significant 5-year shortening in telomere length, whereas no significant relationships between chronic burden and attrition were evident for those at moderate and higher levels of resiliency. These effects apply similarly across the three components of resiliency. Results imply that interventions should focus on establishing strong social connections, psychological resources, and health-maintaining behaviors when attempting to ameliorate stress-related decline in telomere length among at-risk individuals

Cluster randomised trial of a tailored intervention to improve the management of overweight and obesity in primary care in England

Background: Tailoring is a frequent component of approaches for implementing clinical practice guidelines, although evidence on how to maximise the effectiveness of tailoring is limited. In England, overweight and obesity are common, and national guidelines have been produced by the National Institute for Health and Care Excellence. However, the guidelines are not routinely followed in primary care. Methods: A tailored implementation intervention was developed following an analysis of the determinants of practice influencing the implementation of the guidelines on obesity and the selection of strategies to address the determinants. General practices in the East Midlands of England were invited to take part in a cluster randomised controlled trial of the intervention. The primary outcome measure was the proportion of overweight or obese patients offered a weight loss intervention. Secondary outcomes were the proportions of patients with (1) a BMI or waist circumference recorded, (2) record of lifestyle assessment, (3) referred to weight loss services, and (4) any change in weight during the study period. We also assessed the mean weight change over the study period. Follow-up was for 9 months after the intervention. A process evaluation was undertaken, involving interviews of samples of participating health professionals. Results: There were 16 general practices in the control group, and 12 in the intervention group. At follow-up, 15. 08 % in the control group and 13.19 % in the intervention group had been offered a weight loss intervention, odds ratio (OR) 1.16, 95 % confidence interval (CI) (0.72, 1.89). BMI/waist circumference measurement 42.71 % control, 39.56 % intervention, OR 1.15 (CI 0.89, 1.48), referral to weight loss services 5.10 % control, 3.67 % intervention, OR 1.45 (CI 0.81, 2.63), weight management in the practice 9.59 % control, 8.73 % intervention, OR 1.09 (CI 0.55, 2.15), lifestyle assessment 23.05 % control, 23.86 % intervention, OR 0.98 (CI 0.76, 1.26), weight loss of at least 1 kg 42.22 % control, 41.65 % intervention, OR 0.98 (CI 0.87, 1.09). Health professionals reported the interventions as increasing their confidence in managing obesity and providing them with practical resources. Conclusions: The tailored intervention did not improve the implementation of the guidelines on obesity, despite systematic approaches to the identification of the determinants of practice. The methods of tailoring require further development to ensure that interventions target those determinants that most influence implementation

Professional development and research are being neglected: a commentary on the 2019 RCR radiologists’ supporting professional activities (SPA) survey

When the National Health Service (NHS) acquired a statutory duty of care for quality in 1998, clinical governance became a mandatory and intrinsic part of modern medicine. Defined as “a framework through which NHS organisations are accountable for continuously improving the quality of their services and safe-guarding high standards of care by creating an environment in which excellence in clinical care will flourish”,1 the vehicle for NHS consultants to enact clinical governance was supporting professional activity (SPA). All activities that underpin direct clinical care (DCC) are encouraged during SPA time, including professional development, research, audit, teaching, clinical management, appraisal, and job planning.2,3 Adequate time for SPAs alongside DCC is therefore crucial for NHS consultants to\ud maintain excellence in clinical care.3 The recently published Royal College of Radiologists (RCR) Survey on Radiologists’ SPA4 has demonstrated three recurring themes, which are widely recognised to be growing concerns for our specialty