TELEMEDICINE 2027 Open access

Impact of artificial intelligence on acquisition efficiency, image quality and resolution in knee MRI: a pre-post quasi-experimental study protocol

L. Guardamagna,1,2 D. Savioni,3 L. Spagnolo,4 S. Haoufadi,1 G. Marzagalli,2,5 G. Barra,2,6 M. Provenzano,2,7 D. Di Napoli8 | 1Department of Orthopedics and Traumatology, Clinical Institutes of Pavia and Vigevano, Pavia; 2Laboratory for Rehabilitation and Orthopedic Surgery, Institute of Care “Città di Pavia”, Department of Clinical Surgical, Diagnostic and Pediatric Sciences, University of Pavia; 3Senior Radiology Technician, Radiology Unit, Institute of Care “Città di Pavia”; 4Chief Radiology Technician, Radiology Unit, Institute of Care “Città di Pavia”; 5Education Area, Medical Direction, Clinical Institutes of Pavia and Vigevano, Pavia; 6Quality and Accreditation Area, Medical Direction, Clinical Institutes of Pavia and Vigevano, Pavia; 7Directorate of Nursing and Allied Health Professions, Clinical Institutes of Pavia and Vigevano, Pavia; 8Medical Director, Clinical Institutes of Pavia and Vigevano, Pavia, Italy

Authors

Published
28 January 2026
Section
TELEMEDICINE 2027
DOI
10.4081/gimle.750

Abstract

This single-centre quasi-experimental pre-post study evaluates the impact of artificial intelligence (AI) on knee magnetic resonance imaging (MRI) examinations. The objectives include assessing changes in image acquisition time per exam, evaluating overall image quality, and analysing image resolution. These outcomes will be measured in a randomized sample of 100 patients who underwent knee MRI, comparing 50 standard examinations (from January to June 2024) with 50 AI-powered examinations (from January to June 2026). For each period, examinations will be assigned a unique study ID, and a computer-generated random sequence will be used to select 50 cases per cohort. If a selected case meets exclusion criteria (incomplete exam, major motion artefact), it will be replaced by the next randomly selected ID. Image quality will be analysed in terms of variations in the signal-to-noise ratio (SNR). SNR assessment will be conducted on regions of interest selected from five images per patient in both cohorts, yielding a total of 250 per group. The study is expected to demonstrate reduced image acquisition times, non-inferior image quality, and improved resolution, supporting the role of AI-enhanced workflows in routine musculoskeletal MRI practice.

Keywords

References

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