DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks

The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks. The gaze of these networks has in recent years turned increasingly towards applications in healthcare. It was perhaps inevitable that COVID-19, a global disease propagating health and economic devastation, should capture the attention and resources of the world's computer scientists in academia and industry. The potential for AI to support the response to the pandemic has been proposed across a wide range of clinical and societal challenges, including disease forecasting, surveillance and antiviral drug discovery. This is likely to continue as the impact of the pandemic unfolds on the world's people, industries and economy but a surprising observation on the current pandemic has been the limited impact AI has had to date in the management of COVID-19. This correspondence focuses on exploring potential reasons behind the lack of successful adoption of AI models developed for COVID-19 diagnosis and prognosis, in front-line healthcare services. We highlight the moving clinical needs that models have had to address at different stages of the epidemic, and explain the importance of translating models to reflect local healthcare environments. We argue that both basic and applied research are essential to accelerate the potential of AI models, and this is particularly so during a rapidly evolving pandemic. This perspective on the response to COVID-19, may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively.Comment: Accepted in Nature Machine Intelligenc

Bias, Repeatability and Reproducibility of Liver T1 Mapping With Variable Flip Angles.

Funder: National Institute for Health Research; Id: http://dx.doi.org/10.13039/501100000272BACKGROUND: Three-dimensional variable flip angle (VFA) methods are commonly used for T1 mapping of the liver, but there is no data on the accuracy, repeatability, and reproducibility of this technique in this organ in a multivendor setting. PURPOSE: To measure bias, repeatability, and reproducibility of VFA T1 mapping in the liver. STUDY TYPE: Prospective observational. POPULATION: Eight healthy volunteers, four women, with no known liver disease. FIELD STRENGTH/SEQUENCE: 1.5-T and 3.0-T; three-dimensional steady-state spoiled gradient echo with VFAs; Look-Locker. ASSESSMENT: Traveling volunteers were scanned twice each (30 minutes to 3 months apart) on six MRI scanners from three vendors (GE Healthcare, Philips Medical Systems, and Siemens Healthineers) at two field strengths. The maximum period between the first and last scans among all volunteers was 9 months. Volunteers were instructed to abstain from alcohol intake for at least 72 hours prior to each scan and avoid high cholesterol foods on the day of the scan. STATISTICAL TESTS: Repeated measures ANOVA, Student t-test, Levene's test of variances, and 95% significance level. The percent error relative to literature liver T1 in healthy volunteers was used to assess bias. The relative error (RE) due to intrascanner and interscanner variation in T1 measurements was used to assess repeatability and reproducibility. RESULTS: The 95% confidence interval (CI) on the mean bias and mean repeatability RE of VFA T1 in the healthy liver was 34 ± 6% and 10 ± 3%, respectively. The 95% CI on the mean reproducibility RE at 1.5 T and 3.0 T was 29 ± 7% and 25 ± 4%, respectively. DATA CONCLUSION: Bias, repeatability, and reproducibility of VFA T1 mapping in the liver in a multivendor setting are similar to those reported for breast, prostate, and brain. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1

Herbivore Preference for Native vs. Exotic Plants: Generalist Herbivores from Multiple Continents Prefer Exotic Plants That Are Evolutionarily Naïve

Enemy release and biotic resistance are competing, but not mutually exclusive, hypotheses addressing the success or failure of non-native plants entering a new region. Enemy release predicts that exotic plants become invasive by escaping their co-adapted herbivores and by being unrecognized or unpalatable to native herbivores that have not been selected to consume them. In contrast, biotic resistance predicts that native generalist herbivores will suppress exotic plants that will not have been selected to deter these herbivores. We tested these hypotheses using five generalist herbivores from North or South America and nine confamilial pairs of native and exotic aquatic plants. Four of five herbivores showed 2.4–17.3 fold preferences for exotic over native plants. Three species of South American apple snails (Pomacea sp.) preferred North American over South American macrophytes, while a North American crayfish Procambarus spiculifer preferred South American, Asian, and Australian macrophytes over North American relatives. Apple snails have their center of diversity in South America, but a single species (Pomacea paludosa) occurs in North America. This species, with a South American lineage but a North American distribution, did not differentiate between South American and North American plants. Its preferences correlated with preferences of its South American relatives rather than with preferences of the North American crayfish, consistent with evolutionary inertia due to its South American lineage. Tests of plant traits indicated that the crayfish responded primarily to plant structure, the apple snails primarily to plant chemistry, and that plant protein concentration played no detectable role. Generalist herbivores preferred non-native plants, suggesting that intact guilds of native, generalist herbivores may provide biotic resistance to plant invasions. Past invasions may have been facilitated by removal of native herbivores, introduction of non-native herbivores (which commonly prefer native plants), or both

Pretreatment malnutrition and quality of life - association with prolonged length of hospital stay among patients with gynecological cancer: a cohort study

Background Length of hospital stay (LOS) is a surrogate marker for patients' well-being during hospital treatment and is associated with health care costs. Identifying pretreatment factors associated with LOS in surgical patients may enable early intervention in order to reduce postoperative LOS. Methods This cohort study enrolled 157 patients with suspected or proven gynecological cancer at a tertiary cancer centre (2004-2006). Before commencing treatment, the scored Patient Generated - Subjective Global Assessment (PG-SGA) measuring nutritional status and the Functional Assessment of Cancer Therapy-General (FACT-G) scale measuring quality of life (QOL) were completed. Clinical and demographic patient characteristics were prospectively obtained. Patients were grouped into those with prolonged LOS if their hospital stay was greater than the median LOS and those with average or below average LOS. Results Patients' mean age was 58 years (SD 14 years). Preoperatively, 81 (52%) patients presented with suspected benign disease/pelvic mass, 23 (15%) with suspected advanced ovarian cancer, 36 (23%) patients with suspected endometrial and 17 (11%) with cervical cancer, respectively. In univariate models prolonged LOS was associated with low serum albumin or hemoglobin, malnutrition (PG-SGA score and PG-SGA group B or C), low pretreatment FACT-G score, and suspected diagnosis of cancer. In multivariable models, PG-SGA group B or C, FACT-G score and suspected diagnosis of advanced ovarian cancer independently predicted LOS. Conclusions Malnutrition, low quality of life scores and being diagnosed with advanced ovarian cancer are the major determinants of prolonged LOS amongst gynecological cancer patients. Interventions addressing malnutrition and poor QOL may decrease LOS in gynecological cancer patients

An in vivo screen identifies ependymoma oncogenes and tumor-suppressor genes

Cancers are characterized by non-random chromosome copy number alterations that presumably contain oncogenes and tumor-suppressor genes (TSGs). The affected loci are often large, making it difficult to pinpoint which genes are driving the cancer. Here we report a cross-species in vivo screen of 84 candidate oncogenes and 39 candidate TSGs, located within 28 recurrent chromosomal alterations in ependymoma. Through a series of mouse models, we validate eight new ependymoma oncogenes and ten new ependymoma TSGs that converge on a small number of cell functions, including vesicle trafficking, DNA modification and cholesterol biosynthesis, identifying these as potential new therapeutic targets.We are grateful to F.B. Gertler (Massachusetts Institute of Technology) and S. Gupton (University of North Carolina) for the generous gift of the VAMP7-phlorin construct and the staffs of the Hartwell Center for Bioinformatics and Biotechnology, the Small Animal Imaging Center, the Animal Resources Center, the Cell and Tissue Imaging Center, and the Flow Cytometry and Cell Sorting Shared Resource at St. Jude Children's Research Hospital for technical assistance. This work was supported by grants from the US National Institutes of Health (R01CA129541, P01CA96832 and P30CA021765, R.J.G.), by the Collaborative Ependymoma Research Network (CERN) and by the American Lebanese Syrian Associated Charities (ALSAC)