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Aim: To compare digital breast tomosynthesis (DBT) and ultrasound in women recalled for assessment after a positive screening mammogram and assess the potential for each of these tools to reduce unnecessary biopsies. Methods: This data linkage study included 538 women recalled for assessment from January 2017 to December 2019. The association between the recalled mammographic abnormalities and breast density was analysed using the chi-square independence test. Relative risks and the number of recalled cases requiring DBT and ultrasound assessment to prevent one unnecessary biopsy were compared using the McNemar test. Results: Breast density significantly influenced recall decisions (p < 0.001). Ultrasound showed greater potential to decrease unnecessary biopsies than DBT: in entirely fatty (21% vs. 5%; p = 0.04); scattered fibroglandular (23% vs. 10%; p = 0.003); heterogeneously dense (34% vs. 7%; p < 0.001) and extremely dense (39% vs. 9%; p < 0.001) breasts. The number of benign cases needing assessment to prevent one unnecessary biopsy was significantly lower with ultrasound than DBT in heterogeneously dense (1.8 vs. 7; p < 0.001) and extremely dense (1.9 vs. 5.1; p = 0.03) breasts. Conclusion: Women with dense breasts are more likely to be recalled for assessment and have a false-positive biopsy. Women with dense breasts benefit more from ultrasound assessment than from DBT.

Educating students to become evidence-based practitioners remains a significant challenge in health professional education. Clinical placements form a significant element of curriculum and the impact of students’ placement experiences on their ability and desire to practice in an evidence-based manner remains largely unexplored. Medical imaging students complete a variety of clinical placements and observe and participate in a range of clinical settings. This study utilised focus groups to explore students’ experiences of evidence-based practice (EBP) during clinical placements, the impact of these experiences on their education and their future tendencies within the profession. Reflexive thematic analysis was utilised to analyse data and three main overarching themes were identified. The first theme, “Education” explored participants’ understanding of the concept of EBP, the importance of sound pedagogy to enable students’ ability to practice in an evidence-based manner and explored the relationship between participants’ placement experiences and their future EBP tendencies. The second theme investigated, “Culture and responsibility” and was divided into three subthemes, professional, organisational, and individual. Focusing on where the responsibility for implementing EBP should lie and the impact of culture on individuals’ ability to implement evidence-based practice. The third theme, “Hopes, fears, and barriers” explored the participants’ sense of responsibility around implementing EBP and the barriers that they had experienced in doing so. It illuminated their fears around becoming apathetic as clinicians. Improving the teaching of EBP skills within health professional education has the potential to enhance the translation of knowledge into practice as graduates become clinicians within the profession.

Background: To compare the diagnostic efficacy of digital breast tomosynthesis (DBT) and ultrasound across breast densities in women recalled for assessment. Methods: A total of 482 women recalled for assessment from January 2017 to December 2019 were selected for the study. Women met the inclusion criteria if they had undergone DBT, ultrasound and had confirmed biopsy results. We calculated sensitivity, specificity, PPV, and AUC for DBT and ultrasound. Results: In dense breasts, DBT showed significantly higher sensitivity than ultrasound (98.2% vs. 80%; p < 0.001), but lower specificity (15.4% vs. 55%; p < 0.001), PPV (61.3% vs. 71%; p = 0.04) and AUC (0.568 vs. 0.671; p = 0.001). In non-dense breasts, DBT showed significantly higher sensitivity than ultrasound (99.2% vs. 84%; p < 0.001), but no differences in specificity (22% vs. 33%; p = 0.14), PPV (69.2% vs. 68.8%; p = 0.93) or AUC (0.606 vs. 0.583; p = 0.57). Around 73% (74% dense and 71% non-dense) and 77% (81% dense and 72% non-dense) of lesions assigned a RANZCR 3 by DBT and ultrasound, respectively, were benign. Conclusion: DBT has higher sensitivity, but lower specificity and PPV than ultrasound in women with dense breasts recalled for assessment. Most lesions rated RANZCR 3 on DBT and ultrasound are benign and may benefit from short interval follow-up rather than biopsy.

Background: Although paediatrics' are more radio-sensitivity than adults, the number of requested paediatric CT examinations is increasing globally. Significant efforts have been focused on achieving the lowest possible radiation dose for paediatric patients, particularly adjusting for size differences. This study aims to establish Diagnostic Reference Levels (DRLs) for paediatric patients based on size-specific dose estimates (SSDE). Method: In this retrospective national study, CT scans for brain, chest, abdominopelvic, and chest, abdomen, and pelvis (CAP) were collected from four hospitals in Jordan undergoing paediatric CT scans. A total of 1818 cases were randomly selected in four age categories (

This study aimed to systematically review the literature to synthesise and summarise the evidence surrounding the efficacy of artificial intelligence (AI) in classifying paediatric pneumonia on chest radiographs (CXRs). Following the initial search of studies that matched the pre-set criteria, their data were extracted using a data extraction tool, and the included studies were assessed via critical appraisal tools and risk of bias. Results were accumulated, and outcome measures analysed included sensitivity, specificity, accuracy, and area under the curve (AUC). Five studies met the inclusion criteria. The highest sensitivity was by an ensemble AI algorithm (96.3%). DenseNet201 obtained the highest level of specificity and accuracy (94%, 95%). The most outstanding AUC value was achieved by the VGG16 algorithm (96.2%). Some of the AI models achieved close to 100% diagnostic accuracy. To assess the efficacy of AI in a clinical setting, these AI models should be compared to that of radiologists. The included and evaluated AI algorithms showed promising results. These algorithms can potentially ease and speed up diagnosis once the studies are replicated and their performances are assessed in clinical settings, potentially saving millions of lives.

Background The impact of radiologists’ characteristics has become a major focus of recent research. However, the markers of diagnostic efficacy and confidence in dense and non-dense breasts are poorly understood. Purpose This study aims to assess the relationship between radiologists’ characteristics and diagnostic performance across dense and non-dense breasts. Materials and methods Radiologists specialising in breast imaging ( n = 128) who had 0.5–40 (13±10.6) years of experience reading mammograms were recruited. Participants independently interpreted a test set containing 60 digital mammograms (40 normal and 20 abnormal) with similarly distributed breast densities. Diagnostic performance measures were analysed via Jamovi software (version 1.6.22). Results In dense breasts, breast-imaging fellowship completion significantly improved specificity ( p = 0.004), location sensitivity ( p = 0.01) and the area under the curve (AUC) of the receiver operating characteristic ( p = 0.03). Only participation in BreastScreen reading significantly improved all performance metrics: specificity ( p = 0.04), sensitivity ( p = 0.005), location sensitivity ( p < 0.001) and AUC ( p < 0.001). Reading > 100 mammograms weekly significantly improved sensitivity ( p = 0.03), location sensitivity ( p = 0.001), and AUC ( p = 0.03).In non-dense breasts, breast fellowship completion significantly improved sensitivity ( p = 0.02), location sensitivity ( p = 0.04) and AUC ( p = 0.002). Participation in BreastScreen reading and reading > 100 mammograms weekly significantly improved only sensitivity ( p = 0.002 and p = 0.003, respectively) and location sensitivity ( p < 0.001 and p < 0.001, respectively). Conclusion Participating in screening programs, breast fellowships and reading > 100 mammograms weekly are important indicators of the diagnostic performance of radiologists across dense and non-dense breasts. In dense breasts, optimal performance resulted from participation in a breast screening program.