Machine learning has the potential to revolutionize the way knee osteoarthritis (OA) is predicted and treated in patients. Traditionally, statistical methods have been used for patient-specific prediction models, but artificial intelligence approaches such as machine learning can handle complex data sets more efficiently. However, despite the vast amount of data available in OA databases, the field has been slow to adopt advanced analytical techniques. One reason could be a shortage of experts in machine learning for the medical field. Another could be a previous lack of technologies that enable quick and reliable electronic data transfer.
In creating a prediction model, input variables and outcome variables are used to predict outcomes for an individual. Age, sex, and BMI are commonly included as input variables, but other risk factors such as physical activity, knee injury, and ethnicity have also been considered in various studies. Moreover, to enhance the predictive accuracy of current models, several other variables and risk factors should be added, such as quadriceps strength, genetics, and nutritional factors.
Current OA prediction models generally include ten different outcomes, with radiographic knee OA being one of the most commonly used variables. However, future models may recognize the importance of outcome variables visible on MRI, such as bone curvature and meniscal lesions.
To achieve precise early diagnosis and prognosis, advanced machine learning and deep learning approaches are necessary to analyze big and complex data in a timely and accurate manner. However, with large and complex data sets, it is crucial to decrease the number of variables to increase the interpretability of the prediction model. Feature selection, or the process of selecting a subset of relevant variables for use in the model, is essential for determining the performance of the model’s decision-making step.
The performance of the model can be evaluated using data from the same source (internal validation) or an independent source (external validation). External validation can improve the model’s generalizability, making it applicable in other similar populations.
In conclusion, the use of machine learning for prediction models in knee OA has the potential to advance diagnosis and treatment, providing patients with tailored and effective care. However, there is still much work to be done in this field to develop comprehensive prediction models that can be applied in practice.
