Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Anh Tuan Bui
,
Hieu Le
ORCID logo ,
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: 21 December 2023 / Approved: 21 December 2023 / Online: 22 December 2023 (09:50:22 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

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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