Nuclear medicine technologists practice impacted by AI denoising applications in PET/CT images

• Published in: Radiography (London, England. 1995) England. 1995), 2024-07, Vol.30 (4), p.1232-1239
• Main Authors: Champendal, M., Ribeiro, R.S.T., Müller, H., Prior, J.O., Sá dos Reis, C.
• Format: Article
• Language: English
• Subjects: Adoption; AI readiness; Algorithms; Artificial Intelligence; Clinical practice; Female; Focus Groups; Humans; Image denoising; Implementation; Male; Nuclear Medicine; PET/CT; Positron Emission Tomography Computed Tomography - methods; Workload
• ISSN: 1078-8174; 1532-2831; 1532-2831
• DOI: 10.1016/j.radi.2024.06.010

Artificial intelligence (AI) in positron emission tomography/computed tomography (PET/CT) can be used to improve image quality when it is useful to reduce the injected activity or the acquisition time. Particular attention must be paid to ensure that users adopt this technological innovation when outcomes can be improved by its use. The aim of this study was to identify the aspects that need to be analysed and discussed to implement an AI denoising PET/CT algorithm in clinical practice, based on the representations of Nuclear Medicine Technologists (NMT) from Western-Switzerland, highlighting the barriers and facilitators associated. Two focus groups were organised in June and September 2023, involving ten voluntary participants recruited from all types of medical imaging departments, forming a diverse sample of NMT. The interview guide followed the first stage of the revised model of Ottawa of Research Use. A content analysis was performed following the three-stage approach described by Wanlin. Ethics cleared the study. Clinical practice, workload, knowledge and resources were de 4 themes identified as necessary to be thought before implementing an AI denoising PET/CT algorithm by ten NMT participants (aged 31–60), not familiar with this AI tool. The main barriers to implement this algorithm included workflow challenges, resistance from professionals and lack of education; while the main facilitators were explanations and the availability of support to ask questions such as a “local champion”. To implement a denoising algorithm in PET/CT, several aspects of clinical practice need to be thought to reduce the barriers to its implementation such as the procedures, the workload and the available resources. Participants emphasised also the importance of clear explanations, education, and support for successful implementation. To facilitate the implementation of AI tools in clinical practice, it is important to identify the barriers and propose strategies that can mitigate it.