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Published on: July 7, 2023
Observational evaluation of AI-assisted measurements and reporting for enhanced workflow efficiency in leg and foot
Marie-Pauline Talabard1, Marion Durteste2, Amine Hamid3
1Department of Musculoskeletal Imaging, Cochin Hospital, AP-HP, Paris, France.
Rationale And Objectives:
Radiographic musculoskeletal (MSK) measurements are essential for diagnosis and surgical planning, but they remain time-consuming and prone to variability. Artificial intelligence (AI) can address these limitations by automating both measurements and reporting. This study assessed the impact of AI-assisted measurements and reporting on workflow efficiency for leg and foot radiographs using BoneMetrics and AutoReport (Gleamer, Paris, France).
Materials And Methods:
Leg and foot radiographs were collected retrospectively. The ground truth was established by a senior MSK radiologist through manual annotation of key measurements. The same measurements were performed by a junior radiologist first manually, and subsequently with AI assistance. Reports were then generated by the radiologist via voice dictation and subsequently using AI-generated reports. These sessions formed the basis for simulated workflows: manual measurements with dictation, AI-assisted measurements with dictation, AI-assisted measurements with automated reporting.Measurement accuracy was compared between the AI solution and the junior radiologist.
Results:
A total of 98 leg radiographs and 101 foot radiographs were analyzed by the junior radiologist. Measurement time was significantly reduced from 166 to 40 s (p < 0.001) and reporting time from 80 s to 33 s (p < 0.001) with AI assistance. When combining both steps into simulated workflows, total interpretation time decreased from 246 s in the fully manual workflow to 73 s in the fully automated workflow, representing a 70 % gain in efficiency. The AI solution demonstrated high measurement accuracy with performance comparable to that of the junior radiologist.
Conclusion:
This study demonstrated that AI assistance improved workflow efficiency in leg and foot radiography without compromising measurement accuracy. Integrating automated measurements and reporting in the end-to-end workflow holds promise for streamlining clinical practice.
