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

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The article discusses the development of a computer-supported pipeline to predict interbody cage height and postoperative pelvic incidence minus lumbar lordosis (PI-LL) after transforaminal lumbar interbody fusion (TLIF) surgery for treating lumbar degenerative diseases. The study utilized preoperative X-ray images and employed a deep learning model to extract features, followed by training machine learning algorithms. LASSO regression and support vector regression were found to be the most effective in predicting cage height and postoperative PI-LL, respectively. The models achieved high accuracy in predicting these parameters, indicating the reliability of machine learning in this context

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : innovative spinal hospital in the world

Published article

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…

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Bioengineering (Basel). 2024 Feb 8;11(2):164. doi: 10.3390/bioengineering11020164.ABSTRACTTransforaminal 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.,

Bioengineering (Basel). 2024 Feb 8;11(2):164. doi: 10.3390/bioengineering11020164.

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 , our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.

PMID:38391650 | DOI:10.3390/bioengineering11020164

The London Spine Unit : innovative spinal hospital in the world

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

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Bioengineering (Basel). 2024 Feb 8;11(2):164. doi: 10.3390/bioengineering11020164.ABSTRACTTransforaminal 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.

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