Predicting Time to Total Knee Replacement Using Deep Learning
Author Information
Author(s): Ozkan Cigdem, Shengjia Chen, Chaojie Zhang, Kyunghyun Cho, Richard Kijowski, Cem Deniz
Primary Institution: New York University Grossman School of Medicine
Hypothesis
Integrating clinical variables, quantitative and semiquantitative assessments from radiographs and MRIs, and deep learning features into survival models will result in more accurate estimations of time-to-TKR compared to models using a single modality.
Conclusion
The proposed model demonstrated the potential of self-supervised learning and multimodal data fusion in accurately predicting time-to-TKR that may assist physicians to develop personalized treatment strategies.
Supporting Evidence
- The model achieved an area under the curve of 94.5 for predicting the time-to-TKR.
- Integrating self-supervised deep learning features with clinical variables improved prediction accuracy.
- The study utilized data from the Osteoarthritis Initiative and the Multicenter Osteoarthritis Study.
Takeaway
This study created a smart computer program that helps doctors predict when a patient might need knee surgery by looking at their medical images and health information.
Methodology
The study used a survival analysis model developed from features extracted from medical images and clinical measurements, utilizing deep learning techniques.
Potential Biases
Potential bias may arise from treating knees from the same patient as independent data points, which could correlate.
Limitations
The study's generalizability may be limited due to the homogeneity of the sample population and the use of knees from the same patient as independent data points.
Participant Demographics
The study included 895 knees from the Osteoarthritis Initiative, with a mean age of 64.2 years for males and 62.4 years for females.
Statistical Information
P-Value
<.001
Confidence Interval
94.0-95.1
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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