Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.-C.; Tsai, P.-I.; Chen, K.-J.; Hsieh, K.L.-C.; Huang, E.-W.; Hsu, C.-C.; Mathew, M.; Lee, C.-Y.; Wang, P.-Y.; Huang, T.-J.; Wu, M.-H. Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion. Bioengineering2024, 11, 164.
Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.-C.; Tsai, P.-I.; Chen, K.-J.; Hsieh, K.L.-C.; Huang, E.-W.; Hsu, C.-C.; Mathew, M.; Lee, C.-Y.; Wang, P.-Y.; Huang, T.-J.; Wu, M.-H. Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion. Bioengineering 2024, 11, 164.
Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.-C.; Tsai, P.-I.; Chen, K.-J.; Hsieh, K.L.-C.; Huang, E.-W.; Hsu, C.-C.; Mathew, M.; Lee, C.-Y.; Wang, P.-Y.; Huang, T.-J.; Wu, M.-H. Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion. Bioengineering2024, 11, 164.
Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.-C.; Tsai, P.-I.; Chen, K.-J.; Hsieh, K.L.-C.; Huang, E.-W.; Hsu, C.-C.; Mathew, M.; Lee, C.-Y.; Wang, P.-Y.; Huang, T.-J.; Wu, M.-H. Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion. Bioengineering 2024, 11, 164.
Abstract
Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, a deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.
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