Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion

Anh Tuan Bui, Hieu Le, Tung Thanh Hoang, Giam Minh Trinh, Hao-Chiang Shao, Pei-I Tsai, Kuan-Jen Chen, Kevin Li-Chun Hsieh, E-Wen Huang, Ching-Chi Hsu, Mathew Mathew, Ching-Yu Lee, Po-Yao Wang, Tsung-Jen Huang, Meng-Huang Wu

Research output: Contribution to journalArticlepeer-review

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, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients 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.

Original languageEnglish
JournalBioengineering
Volume11
Issue number2
DOIs
Publication statusPublished - Feb 8 2024

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