This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.
Keyphrases
- machine learning
- deep learning
- total knee arthroplasty
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- high resolution
- knee osteoarthritis
- artificial intelligence
- big data
- case report
- prognostic factors
- electronic health record
- current status
- risk assessment
- patient reported outcomes
- minimally invasive
- convolutional neural network
- mass spectrometry
- climate change