Global warming stands as a paramount public health issue of our time, and it is fundamental to explore approaches to green medical imaging departments/(MID). This study aims to map the existing actions in the literature that promote sustainable development in MID towards the promotion of environmental impact reduction. Following the JBI methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), this literature search was conducted on MEDLINE, Embase and CINAHL to encompass studies published after 2013. Combinations of keywords and relevant terms related to environmental sustainability, recycling, medical waste, and greening radiology were applied for this review. Three independent reviewers screened abstracts, titles, and eligible full-text. Disagreement was solved through consensus. 38 out of 4630 articles met all inclusion criteria, and four additional articles were identified and added through reference search. A third of the studies included were published after 2022, and most were conducted in developed countries (36/41). Articles focused on computed tomography (9/41), magnetic resonance imaging (6/41), interventional radiology (4/41), conventional radiography (4/41), ultrasound (2/41), mixed modalities (10/41), or not applicable to an imaging modality (6/41). Four principal categories were identified to decrease ecological footprint: energy consumption, waste management, justification and environmental pollution. To minimise the environmental impact of MIDs raising awareness and promoting education is fundamental. Examinations must be justified adequately, energy consumption must be reduced, and waste management practices need to be implemented. Further studies are required to prioritise the most effective strategies, supporting decision-making among stakeholders. Several strategies are already possible to implement to reduce the environmental impact of MIDs and improve the healthcare outcomes for patients.

Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women.

The aim of this study was to explore the perceived impact of artificial intelligence (AI) on radiographers’ activities and profession in Switzerland. A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted. A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 63.8% of them did not feel confident with AI terminology. Participants viewed AI as an opportunity (57.2%), while 18.5% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centred care, research, and patient safety. The significant threats identified were reduction on work positions (22.6%), decrease of the radiographers’ expertise level due to automation bias (16.4%). Participants (68.3%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (87.6%). 93% of the participants mentioned that AI education should be included at undergraduate education program. Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

Purpose: The purpose of this study is to identify suitable MRI sequences and evaluate the feasibility and performance of MRI for total hip arthroplasty (THA) preoperative planning. Method: A multicentric pilot study was conducted to evaluate DP TSE and T1 GRE 3D sequences. High-resolution pelvis, hip, knee and ankle images were acquired. Protocols were optimised to enhance image quality (IQ) and reduce acquisition time to fit clinical practice. The final protocol was validated with 19 healthy volunteers with variable BMIs at 1.5 and 3 Tesla. Visual assessment was performed by five radiographers and radiologists using the ViewDEX software. Visual Grading Analysis (VGA), Intraclass Correlation Coefficient (ICC), Prevalenceadjusted and bias-adjusted kappa (PABAK) and Visual Grading Characteristics (VGC) were performed to analyse data. Results: VGA scores indicated that the optimised 3D DP TSE and 3D T1 GRE sequences at 3 T, as well as 3D DP TSE sequence at 1.5 T offer adequate IQ and allow a correct visualisation of the anatomy. Overall ICC analysis was moderate to good reliability at 0.749 (95 % CI 0.69-0.79) and increased from good to excellent at 0.846 (95 % CI 0.72-0.91) for DP at 3 T. PABAK shows fair agreement at 0.25 (95 % CI 0.227-0.273). VGC analysis showed that 3D DP TSE sequences performed statistically better than 3D T1 GRE at 1.5 and 3 T (p-value <= 0.05). Furthermore, 3 T sequences showed a statistically better performance compared to 1.5 T (p-value <= 0.05). Conclusions: According to the results, 3D DP and T1 MRI sequences can be considered for preoperative planning for THA. Further research is required to emphasize the clinical validation of the results.

Magnetic resonance imaging (MRI) has limited capacity to visualise cortical bone due to its low proton density and short decay time. Recently developed ultrashort (UTE) and zero-echo time (ZTE) sequences enable bone imaging without ionising radiation. This study aims to identify the key technical parameters, advantages, and limitations of UTE and ZTE for musculoskeletal (MSK) imaging. JBI methodology was applied, and three databases (MEDLINE, EMBASE and CINAHL) were selected to identify articles published after 2005 (French-English). Keywordsand MeSH terms related to UTE, ZTE, MRI and MSK were used. Two independent reviewers screened titles, abstracts, and full texts. Disagreements were solved through consensus. From 671 articles, 18 met all criteria. UTE and ZTE were applied for spine (6/18), lower limb (4/18), head and neck (4/18), general bone (3/18) and shoulder (1/18) investigations. Eight articles suggest a very short repetition time (TR) (0.425-8 ms), three longer TR (100-1075 ms), six did not mention TR. All articles mentioned very short echo time (TE) (0.00-0.34 ms). Acquisition time ranged from 3 to 12 min. UTE and ZTE are based on radial acquisition, allowing to acquire the cortical bone signal and increasing fracture contrast, comparable to CT-scan. Acquisition time was the main disadvantage. UTE and ZTE sequences are promising for the MSK applications, offering good contrast for cortical bone evaluation. Further studies are necessary to assess the possibilities of AI tools as approaches to improve image quality and reduce acquisition time. UTE and ZTE sequences can be added to MSK MRI exams to improve fracture and cortical bone evaluation, allowing radiation-free imaging. ZTE's lower acoustic noise benefits anxious, paediatric, or dementia patients' MRI experience.