Publications by authors named "Roohollah Milimonfared"
Article Synopsis
- Prediction models are increasingly utilized in healthcare to assess risk factors and predict outcomes, contributing to enhanced clinical practices.
- The paper focuses on recent advancements in supervised machine learning (ML) techniques applied to data from post-operative hip and knee replacements.
- It aims to summarize key findings from relevant studies, discussing the methodologies, data sources, limitations, and the overall accuracy of predictive analytics in this field.
Visual scoring of damage at taper junctions is the sole method to quantify corrosion in large-scale retrieval studies of failed hip replacement implants. This study introduces an intelligent image analysis-based method that objectively rates corrosion at stem taper of retrieved hip implants according to the well-known Goldberg scoring method. A Support Vector Machine classifier was used that takes in vectors of global and local textural features and assigns scores to the corresponding images.
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