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INTRODUCTION: Artificial Intelligence (AI) is increasingly implemented in medical imaging practice, however, its impact on radiographers practice is not well studied. The aim of this study was to explore the perceived impact of AI on radiographers' activities and profession in Switzerland. METHODS: 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. RESULTS: A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 64% of them did not feel confident with AI-terminology. Participants viewed AI as an opportunity (57%), while 19% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centered care, research, and patient safety. The significant threats identified were reduction on work positions (23%), decrease of the radiographers' expertise level due to automation bias (16%). Participants (68%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (88%). 93% of the participants mentioned that AI education should be included at undergraduate education program. CONCLUSION: 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.

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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), Prevalence-adjusted 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.

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Artroplastia de Reemplazo de Cadera , Imagen por Resonancia Magnética , Cuidados Preoperatorios , Humanos , Proyectos Piloto , Imagen por Resonancia Magnética/métodos , Masculino , Femenino , Cuidados Preoperatorios/métodos , Adulto , Reproducibilidad de los Resultados , Persona de Mediana Edad , Imagenología Tridimensional/métodos , Estudios de Factibilidad

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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.