Application of Artificial Intelligence and Machine Learning for the Prediction of Interbody Cage Height and Postoperative Alignment in Transforaminal Lumbar Interbody Fusion
Year: 2023
Type: preprint
Abstract: Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. Here, we developed a fully computer-supported pipeline to predict the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery through preoperative X-ray images. The automated pipeline included two primary stages. First, a deep learning model was used to extract essential features from X-ray images. Second, five machine learning algorithms were trained to identify the optimal models to predict the interbody cage height and postoperative PI-LL. Lasso regression and support vector regression exhibited superior performance for predicting the 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. In addition, the model demonstrated adequate performance for predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, the interbody cage height and postoperative PI-LL can be reliably predicted using artificial intelligence and ML models.
Source: Preprints.org
Authors: 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
Institutions: Vietnam Military Medical University, Taipei Medical University, École Polytechnique Fédérale de Lausanne, Pham Ngoc Thach University of Medicine, Hospital for Traumatology and Orthopedics, National Chung Hsing University, Industrial Technology Research Institute, Taipei Medical University Hospital, National Yang Ming Chiao Tung University, National Taiwan University of Science and Technology, University of Illinois Chicago
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4389291877