Diagnosis and treatment of foot disease in patients with diabetes is a common clinical-radiologic challenge, particularly the differentiation of neuropathic arthropathy from osteomyelitis. Conventional clinical tests and imaging techniques have limited accuracy for evaluation of the diabetic foot. The introduction of morphologic magnetic resonance (MR) imaging in these patients has provided a qualitative leap in diagnosis. The characteristics of soft-tissue and bone marrow edema and their patterns of distribution throughout the foot allow discrimination between both entities. However, in certain scenarios, the application of MR imaging to this problem is limited because of overlapping features between the two and the coexistence of infection and neuropathic changes. Recent technical advances in MR imaging sequences have increased the capability to add functional quantitative information to structural information. Diffusion-weighted imaging is useful to determine the presence and extension of osteomyelitis. Dynamic contrast-enhanced MR imaging may help to detect differences between the vascularization patterns of neuropathic arthropathy and osteomyelitis. MR angiography (with or without contrast material) is used in clinical practice to identify candidate distal vessels for revascularization. MR neurography, and especially diffusion-tensor imaging, provides quantitative information about neural damage. These new sequences may help in assessment of the different pathophysiologic conditions that occur in the diabetic foot. The physical basis of these techniques, their limitations, and their potential applications for diabetic foot assessment are detailed in this article. The introduction of advanced MR imaging multiparametric protocols, with the aim of enhancing the overall diagnostic accuracy of MR imaging, may help in treatment decision making and lead to improved patient outcomes. RSNA, 2017.

ObjectivesWe assessed the efficiency of BCN Checkpoint in detecting new cases of HIV infection and efficiently linking newly diagnosed individuals to care. MethodsThis study analysed during 2007-2012 the number of tests performed and the number of persons tested in BCN Checkpoint, the HIV prevalence, global and in first visits, the capacity of HIV detection compared to the reported cases in MSM in Catalonia, and the linkage to care rate. ResultsDuring the six years a total of 17.319 tests were performed and 618 HIV-positive cases were detected. Median prevalence of clients who visited the centre for the first time was 5.4% (4.1-5.8). BCN Checkpoint detected 36. 3% (35.0-40.4) of all reported cases in MSM during [2009][2010][2011]. Linkage to care was achieved directly in 90.5% of the cases and only 2.4% of cases were lost to follow-up. ConclusionsA community-based centre, addressed to a key population at risk, can be less effort consuming (time and funding) and show high efficiency in HIV detection and linkage to care. . These factors discourage persons from sexual minorities from seeking and receiving essential HIV prevention, testing, care and treatment services, condemning them to remain at disproportionately high risk of HIV acquisition [6]. Greater access to testing and availability of prevention and care services for persons infected with HIV can reduce new infections and lead to reductions in HIV-associated morbidity and mortality [7]. To overcome some of these barriers to the early diagnosis and linkage to care of infected persons, the patient-based organization Projecte dels NOMS-Hispanosida created in 2006 BCN Checkpoint, a community-based centre (CBC) for MSM in the gay area of Barcelona. This centre offers HIV testing free of prejudice, peer counselling and support, and linkage to medical care for people diagnosed with HIV infection. The centre is staffed by a part-time physician, a nurse, 12 counsellors, a receptionist and two administrative assistants. All members of the team are gay, some are HIV positive and six counsellors are part-time volunteers. Peer support is fundamental in helping HIV-infected persons to deal with the emotional impact of receiving such a diagnosis, as well as in helping them to seek medical care and adhere to treatment. This CBC is dedicated to MSM because Barcelona has a significant MSM community with a high prevalence of HIV infection (17%) [8]. Awareness of serostatus also results in a reduction in the risk of transmission of HIV to sex partners, as a substantial proportion of PLWHIV reduce sexual behaviours likely to transmit HIV after discovering that they have HIV infection [9]. Thus, HIV testing represents secondary prevention for people who know their HIV status (reduction of prevalence and severity of the disease) and primary prevention for the community (reduction of HIV incidence). KeywordsProjecte dels NOMS-Hispanosida, in addition to setting up BCN Checkpoint, started promoting regular testing for MSM and implemented for the first time in Spain the rapid HIV t...

Objectives To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. Methods Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning–based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. Results Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. Conclusions Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. Key Points • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)–based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07937-3.

Cardiovascular magnetic resonance (CMR) imaging is a versatile tool that has established itself as the reference method for functional assessment and tissue characterisation. CMR helps to diagnose, monitor disease course and sub-phenotype disease states. Several emerging CMR methods have the potential to offer a personalised medicine approach to treatment. CMR tissue characterisation is used to assess myocardial oedema, inflammation or thrombus in various disease conditions. CMR derived scar maps have the potential to inform ablation therapy—both in atrial and ventricular arrhythmias. Quantitative CMR is pushing boundaries with motion corrections in tissue characterisation and first-pass perfusion. Advanced tissue characterisation by imaging the myocardial fibre orientation using diffusion tensor imaging (DTI), has also demonstrated novel insights in patients with cardiomyopathies. Enhanced flow assessment using four-dimensional flow (4D flow) CMR, where time is the fourth dimension, allows quantification of transvalvular flow to a high degree of accuracy for all four-valves within the same cardiac cycle. This review discusses these emerging methods and others in detail and gives the reader a foresight of how CMR will evolve into a powerful clinical tool in offering a precision medicine approach to treatment, diagnosis, and detection of disease.