| 1. |
- Hering, Alessa, et al.
(författare)
-
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
- 2023
-
Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 42:3, s. 697-712
-
Tidskriftsartikel (refereegranskat)abstract
- Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
|
|
| 2. |
- James, Sarah-Naomi, et al.
(författare)
-
A population-based study of head injury, cognitive function and pathological markers.
- 2021
-
Ingår i: Annals of clinical and translational neurology. - : Wiley. - 2328-9503. ; 8:4, s. 842-856
-
Tidskriftsartikel (refereegranskat)abstract
- 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.Participants (n=502, age=69-71) from the 1946 British Birth Cohort underwent cognitive testing (subtests of Preclinical Alzheimer Cognitive Composite), 18 F-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) >15years prior to the scan (ii) anytime up to age 71.Compared to those with no evidence of an LOC HI, only those reporting an LOC HI>15years 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).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).
|
|
| 3. |
- Molteni, Erika, et al.
(författare)
-
SARS-CoV-2 (COVID-19) infection in pregnant women : characterization of symptoms and syndromes predictive of disease and severity through real-time, remote participatory epidemiology
- 2020
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- BACKGROUND: From the beginning of COVID-19 pandemic, pregnant women have been considered at greater risk of severe morbidity and mortality. However, data on hospitalized pregnant women show that the symptom profile and risk factors for severe disease are similar to those among women who are not pregnant, although preterm birth, Cesarean delivery, and stillbirth may be more frequent and vertical transmission is possible. Limited data are available for the cohort of pregnant women that gave rise to these hospitalized cases, hindering our ability to quantify risk of COVID-19 sequelae for pregnant women in the community.OBJECTIVE: To test the hypothesis that pregnant women in community differ in their COVID-19 symptoms profile and disease severity compared to non-pregnant women. This was assessed in two community-based cohorts of women aged 18-44 years in the United Kingdom, Sweden and the United States of America.STUDY DESIGN: This observational study used prospectively collected longitudinal (smartphone application interface) and cross-sectional (web-based survey) data. Participants in the discovery cohort were drawn from 400,750 UK, Sweden and US women (79 pregnant who tested positive) who self-reported symptoms and events longitudinally via their smartphone, and a replication cohort drawn from 1,344,966 USA women (162 pregnant who tested positive) cross-sectional self-reports samples from the social media active user base. The study compared frequencies of symptoms and events, including self-reported SARS-CoV-2 testing and differences between pregnant and non-pregnant women who were hospitalized and those who recovered in the community. Multivariable regression was used to investigate disease severity and comorbidity effects.RESULTS: Pregnant and non-pregnant women positive for SARS-CoV-2 infection drawn from these community cohorts were not different with respect to COVID-19-related severity. Pregnant women were more likely to have received SARS-CoV-2 testing than non-pregnant, despite reporting fewer clinical symptoms. Pre-existing lung disease was most closely associated with the severity of symptoms in pregnant hospitalized women. Heart and kidney diseases and diabetes were additional factors of increased risk. The most frequent symptoms among all non-hospitalized women were anosmia [63% in pregnant, 92% in non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant women who were hospitalized. Gastrointestinal symptoms, including nausea and vomiting, were different among pregnant and non-pregnant women who developed severe outcomes.CONCLUSIONS: Although pregnancy is widely considered a risk factor for SARS-CoV-2 infection and outcomes, and was associated with higher propensity for testing, the profile of symptom characteristics and severity in our community-based cohorts were comparable to those observed among non-pregnant women, except for the gastrointestinal symptoms. Consistent with observations in non-pregnant populations, comorbidities such as lung disease and diabetes were associated with an increased risk of more severe SARS-CoV-2 infection during pregnancy. Pregnant women with pre-existing conditions require careful monitoring for the evolution of their symptoms during SARS-CoV-2 infection.
|
|
| 4. |
- Molteni, Erika, et al.
(författare)
-
Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts
- 2021
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 5. |
- Murray, Benjamin, et al.
(författare)
-
Accessible data curation and analytics for international-scale citizen science datasets
- 2021
-
Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 8:1
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 6. |
- Sudre, Carole H., et al.
(författare)
-
Attributes and predictors of long COVID
- 2021
-
Ingår i: Nature Medicine. - : Springer Nature. - 1078-8956 .- 1546-170X. ; 27:4, s. 626-631
-
Tidskriftsartikel (refereegranskat)abstract
- Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We ana- lyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) partici- pants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavi- rus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.
|
|
| 7. |
- Varsavsky, Thomas, et al.
(författare)
-
Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application : a prospective, observational study
- 2021
-
Ingår i: The Lancet Public Health. - : Elsevier. - 2468-2667. ; 6:1, s. 21-29
-
Tidskriftsartikel (refereegranskat)abstract
- Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. Funding: Zoe Global, 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, Alzheimer's Society, Chronic Disease Research Foundation.
|
|
