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Machine learning outperforms clinical experts in classification of hip fractures
E A Murphy1, B Ehrhardt1, C L Gregson2,3
1Institute for Mathematical Innovation, University of Bath, Bath, UK.
Scientific Reports
|February 9, 2022
Summary
A new machine learning (ML) method accurately identifies and classifies hip fractures, outperforming human experts. This advancement in medical imaging analysis offers improved diagnostic accuracy for elderly patients, potentially reducing healthcare costs.
Area of Science:
- Orthopedics
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Hip fractures are a significant cause of morbidity, mortality, and high healthcare costs in the elderly population.
- The increasing global elderly population is projected to lead to a rise in hip fracture incidence.
- Accurate fracture classification is crucial as it dictates surgical treatment, influencing patient outcomes and costs.
Purpose of the Study:
- To develop a machine learning (ML) method for identifying and classifying hip fractures from radiographs.
- To compare the diagnostic performance of the ML method against experienced human clinicians.
Main Methods:
- Utilized a dataset of 3659 hip radiographs, with each case independently classified by at least two expert clinicians.
- Developed and trained a machine learning algorithm for hip fracture identification and classification.
Main Results:
- The machine learning method achieved an overall accuracy of 92% in classifying hip fractures.
- The ML approach demonstrated a 19% improvement in accuracy compared to experienced human observers.
Conclusions:
- Machine learning offers a highly accurate tool for hip fracture classification, surpassing human expert performance.
- This AI-driven approach has the potential to enhance diagnostic accuracy, optimize treatment strategies, and manage healthcare costs associated with hip fractures.
