Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging

• DOI: 10.3174/ajnr.a7589
• Publication Date: 2022-07-28T16:26:06Z
• Authors: T. Yamamoto; C. Lacheret; H. Fukutomi; R.A. Kamraoui; L. Denat; B. Zhang; V. Prevost; L. Zhang; A. Ruet; B. Triaire; V. Dousset; P. Coupé; T. Tourdias
• Keywords: Health Professionals

<h3>Introduction</h3> Novel teleophthalmology technologies have the potential to reduce unnecessary and inaccurate referrals between community optometry practices hospital eye services as a result improve patients' access appropriate timely care. However, little is known about acceptability facilitators barriers implementations of these in real life. <h3>Methods analysis</h3> A theoretically informed, qualitative study will explore healthcare professionals' perspectives on Artificial Intelligence Decision Support System models combination situated observations services, semistructured interviews with patients professionals self-audiorecordings be conducted. Participants purposively selected from 4 5 6–8 affiliated practices. The aim recruit 30–36 30 All audiorecorded, participants' permission, transcribed verbatim. Data interviews, analysed thematically informed by normalisation process theory an inductive approach. <h3>Ethics dissemination</h3> Ethical approval has been received London-Bromley research ethics committee. Findings reported through academic journals conferences ophthalmology, health research, management studies human-computer interaction.