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Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa.

The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness.

A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident.

Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended.

The online version contains supplementary material available at 10.1186/s13244-021-01028-z.
The online version contains supplementary material available at 10.1186/s13244-021-01028-z.
Mindfulness-based interventions (MBIs) have been widely implemented to improve self-regulation behaviors, often by targeting emotion-related constructs to facilitate change. Yet the degree to which MBIs engage specific measures of emotion-related constructs has not been systematically examined.

Using advanced meta-analytic techniques, this review examines construct and measurement engagement in trials of adults that used standardized applications of the two most established MBIs Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT), or modified variations of these interventions that met defined criteria.

Seventy-two studies (
=7,378) were included (MBSR
=47, MBCT
= 21, Modified
=4). MBIs led to significant improvement in emotion-related processing overall, compared to inactive controls (
=0.58;
=36), and in all constructs assessed depression (
=0.66;
=26), anxiety (
=0.63;
=19), combined mental health (
=0.75;
=7 ) and stress (
=0.44;
=11). Rself-regulation, to determine which measures are most influenced by MBCT/MBSR. Compared to extant reviews, which typically focused on MBI outcomes, this work examined mechanistic processes based on measurement domains and tools. Bcl-2 antagonist While effect sizes were similar among measurement tools, this review also includes a descriptive evaluation of measures and points of caution, providing guidance to MBI researchers and clinicians for selection of emotion-related measurement tools.
Mindfulness has been linked to better emotion regulation and more adaptive responses to stress across a number of studies, but the mechanisms underlying these links remain to be fully understood. The present study examines links between trait mindfulness (Five Facets of Mindfulness Questionnaire; FFMQ) and participants' responses to common emotional challenges, focusing specifically on the roles of reduced avoidance and more self-distanced engagement as key potential mechanisms driving the adaptive benefits of trait mindfulness.

Adults (
= 305, age range 40-72) from the Second Generation Study of the Harvard Study of Adult Development completed two laboratory-based challenges - public speaking combined with difficult math tasks (the Trier Social Stress Test) and writing about a memory of a difficult moment. State anxiety and sadness were assessed immediately before and after the two stressors. To capture different ways of engaging, measures of self-distancing, avoidance, and persistent worry were collected during the lab session.

As predicted, individuals who scored higher on the FFMQ experienced less anxiety and persistent worry in response to the social stressors. The FFMQ was also linked to less anxiety and sadness when writing about a difficult moment. The links between mindfulness and negative emotions after the writing task were independently mediated by self-distanced engagement and lower avoidance.

Affective benefits of trait mindfulness under stress are associated with both the degree and the nature of emotional engagement. Specifically, reduced avoidance and self-distanced engagement may facilitate reflection on negative experiences that is less affectively aversive.
Affective benefits of trait mindfulness under stress are associated with both the degree and the nature of emotional engagement. Specifically, reduced avoidance and self-distanced engagement may facilitate reflection on negative experiences that is less affectively aversive.Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability and robustness when facing concept-drifting and noisy streaming data, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we first propose an adaptive local online kernel density estimator (ALoKDE) for real-time density estimation on data streams. ALoKDE consists of two tightly integrated strategies (1) a statistical test for concept drift detection and (2) an adaptive weighted local online density estimation when a drift does occur. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. A robust variant of ALoKDE, i.e., R-ALoKDE, is further developed to effectively handle data streams with varied types/levels of noise.
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