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Machine Learning-Aided Diagnosis Enhances Human Detection of Perilunate Dislocations
Anna Luan1,2, Lisa von Rabenau1, Arman T Serebrakian2
1Stanford University, CA, USA.
Hand (New York, N.Y.)
|January 16, 2025
Summary
Machine learning tools enhance the detection of perilunate dislocations on wrist radiographs. This AI-assisted approach improves accuracy and specificity, particularly for residents, reducing misdiagnosis rates.
Area of Science:
- Orthopedic Surgery
- Radiology
- Artificial Intelligence
Background:
- Perilunate and lunate injuries are often misdiagnosed.
- Accurate and timely diagnosis is crucial for effective treatment.
Purpose of the Study:
- To evaluate if a machine learning algorithm can improve the human detection of perilunate/lunate dislocations.
- To assess the impact of AI assistance on diagnostic performance across different medical specialties and training levels.
Main Methods:
- 137 participants (emergency medicine, hand surgery, radiology) evaluated 30 wrist radiographs.
- Radiographs were assessed with and without an AI tool that labeled the lunate.
- Diagnostic performance was measured using sensitivity, specificity, accuracy, and F1 score.
Main Results:
- The AI tool improved overall specificity (88% to 94%), accuracy (89% to 93%), and F1 score (0.89 to 0.92).
- Attending physicians and fellows showed improved specificity (93% to 97%) with the AI tool.
- Residents demonstrated significant improvements in accuracy (86% to 91%) and specificity (86% to 93%) when using the AI tool.
Conclusions:
- Machine learning tools enhance radiographic detection of perilunate dislocations, especially for residents.
- AI assistance improves diagnostic specificity for all training levels.
- This technology can help reduce misdiagnosis, particularly when specialist evaluation is delayed.
