Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Artificial Intelligence and Machine Learning for the Prediction of Interbody Cage Height and Postoperative Alignment in Transforaminal Lumbar Interbody Fusion

Anh Tuan Bui
,
Hieu Le
,
Tung Thanh Hoang
,
Giam Minh Trinh
ORCID logo ,
Hao-Chiang Shao
,
Pei-I Tsai
ORCID logo ,
Kuan-Jen Chen
,
Kevin Li-Chun Hsieh
,
E-Wen Huang
,
Ching-Chi Hsu
ORCID logo ,
Mathew Mathew
,
Ching-Yu Lee
,
Po-Yao Wang
,
Tsung-Jen Huang
,
Meng-Huang Wu
* ORCID logo
Version 1 : Received: 30 November 2023 / Approved: 1 December 2023 / Online: 1 December 2023 (08:12:03 CET)

How to cite: Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.; Tsai, P.; Chen, K.; Hsieh, K.L.; Huang, E.; Hsu, C.; Mathew, M.; Lee, C.; Wang, P.; Huang, T.; Wu, M. Application of Artificial Intelligence and Machine Learning for the Prediction of Interbody Cage Height and Postoperative Alignment in Transforaminal Lumbar Interbody Fusion. Preprints 2023, 2023120036. https://doi.org/10.20944/preprints202312.0036.v1 Bui, A.T.; Le, H.; Hoang, T.T.; Trinh, G.M.; Shao, H.; Tsai, P.; Chen, K.; Hsieh, K.L.; Huang, E.; Hsu, C.; Mathew, M.; Lee, C.; Wang, P.; Huang, T.; Wu, M. Application of Artificial Intelligence and Machine Learning for the Prediction of Interbody Cage Height and Postoperative Alignment in Transforaminal Lumbar Interbody Fusion. Preprints 2023, 2023120036. https://doi.org/10.20944/preprints202312.0036.v1

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.

Keywords

Spinal fusion; Interbody cage; Sagittal balance; Artificial intelligence; Machine learning; Spinal parameters

Subject

Medicine and Pharmacology, Surgery

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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