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IRCT20230723058893N4 IRCT 2026-08-31 Mazandaran University of Medical Sciences Development and evaluation of the effectiveness of an AI-based educational–clinical platform as a "second reader" for Kellgren–Lawrence grading in knee radiography: a randomized controlled trial Development and evaluation of the effectiveness of an AI-based educational–clinical platform as a "second reader" for Kellgren–Lawrence grading in knee radiography: a randomized controlled trial 2026-08-23 anticipated 280 Recruiting https://irct.ir/trial/92348 interventional Randomization: Randomized, Blinding: Not blinded, Placebo: Not used, Assignment: Parallel, Purpose: Education/Guidance, Randomization description: Individual participants will be randomized in a 1:1 allocation ratio to either the control group or the intervention group. A variable block randomization method will be used to generate the allocation sequence. Where feasible, randomization will be stratified by educational specialty and baseline skill level to maintain balance between the study groups. Randomization will be performed after completion of the pre-test. Allocation concealment will be maintained until completion of the pre-test, after which the appropriate software access will be assigned according to the participant’s allocated group. N/A Knee Osteoarthritis. Intervention 1: Intervention group: Standard digital knee radiographs evaluated using the deep learning (AI) algorithm to grade knee osteoarthritis according to the Kellgren-Lawrence (KL) classification criteria. Intervention 2: Control group: Standard training without AI output. Yes - There is a plan to make this available What will be shared: Study protocol, statistical analysis plan, and informed consent form: These documents contain the methodology, statistical analysis details, and participant consent procedures for evaluating the AI model's accuracy in knee osteoarthritis grading When: Starting 6 months after the publication of the study results for a period of up to 5 years To whom: Available to academic researchers and clinicians affiliated with universities or research centers upon reasonable request Conditions: For scientific research, systematic reviews, and meta-analyses related to artificial intelligence applications in knee osteoarthritis grading, subject to approval of the study proposal by the primary investigator Where to obtain: Obtainable by submitting a formal request via email to the corresponding author / principal investigator How to obtain: Applicants must submit a formal request and research proposal to the principal investigator via email. The proposal will be reviewed within 2 to 4 weeks, and upon approval, the documents will be provided Comments:
public Elaheh Sadat Hosseini.
No. 3, Ayatollah Salehi Alley, Mir Mashhad Neighborhood, Behind Ibn Ashub Library, Islamic Republic Street, Sari, Mazandaran Province
sari Iran (Islamic Republic of) 4816646796 +98 915 264 8141 sadathosseini767676@gmail.com Mazandaran University of Medical Sciences scientific Elaheh Sadat Hosseini.
No. 3, Ayatollah Salehi Alley, Mir Mashhad Neighborhood, Behind Ibn Ashub Library, Islamic Republic Street, Sari, Mazandaran Province
sari Iran (Islamic Republic of) 4816646796 +98 915 264 8141 sadathosseini767676@gmail.com Mazandaran University of Medical Sciences Iran (Islamic Republic of) Provision of written informed consent Stable access to the internet and a computer for using the platform No significant prior experience with Kellgren–Lawrence grading or AI-based diagnostic support systems, based on a self-reported screening checklist Ability to complete the pre-test and post-test within the specified timeframe no limit no limit Both Having significant prior experience in KL grading Having significant prior experience working with AI-based computer-aided diagnostic systems. M17 Osteoarthritis of knee Diagnosis Diagnosis Intervention group: Standard digital knee radiographs evaluated using the deep learning (AI) algorithm to grade knee osteoarthritis according to the Kellgren-Lawrence (KL) classification criteria Control group: Standard training without AI output. Diagnostic accuracy of deep learning algorithm in grading knee osteoarthritis based on Kellgren-Lawrence (KL) criteria. Timepoint: Baseline (immediately upon evaluation of knee radiographs by the AI model and human readers). Method of measurement: Evaluated by deep learning model and expert radiologists using the Kellgren-Lawrence grading system on standard weight-bearing anteroposterior knee radiographs. Diagnostic sensitivity, specificity, and processing time of the deep learning algorithm compared to expert radiologists in detecting knee osteoarthritis. Timepoint: Baseline (immediately upon evaluation of knee radiographs by the AI model and human readers). Method of measurement: Statistical analysis using Cohen's kappa coefficient to assess inter-observer agreement, and receiver operating characteristic (ROC) curve analysis to evaluate diagnostic performance parameters. Mazandaran University of Medical Sciences Approved 2026-05-16 mam (RA) Teaching and Therapeutic Hospital of Sari - Mazandaran University of Medical Sciences (Res No. 3, Ayatollah Salehi Alley, Mir Mashhad Neighborhood, Behind Ibn Ashub Library, Islamic Republic Street, Sari, Mazandaran Province sari Mazandaran Iran (Islamic Republic of)