Preoperative Radiographic Prediction Tool for Early Postoperative Segmental and Lumbar Lordosis Alignment After Transforaminal Lumbar Interbody Fusion

Porche, Ken; Dru, Alexander; Moor, Rachel; Kubilis, Paul; Vaziri, Sasha; Hoh, Daniel J.

doi:10.7759/cureus.18175

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…However, because the PI value is regarded as a constant anatomic feature with slight variation in pathologic disorders or lumbar spine interventions [38], determining the postoperative LL is typically necessary for predicting the optimal PI-LL. According to previous research, LL restoration after surgery is closely linked to preoperative LL and PI [39][40][41][42]. Therefore, LL and PI can be used to predict the LL and PI-LL values after surgery, as in our model.…”

Section: Discussionmentioning

confidence: 91%

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

Bui,

Le,

Hoang

et al. 2024

Bioengineering

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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.

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Section: Discussionmentioning

confidence: 91%

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

Bui,

Le,

Hoang

et al. 2024

Bioengineering

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show abstract

“…However, because the PI value is regarded as a constant anatomic feature with slight variation in pathologic disorders or lumbar spine interventions [30], determining the postoperative LL is typically necessary for predicting the optimal PI-LL. According to previous research, LL restoration after surgery is closely linked to preoperative LL and PI [31][32][33][34]. Therefore, LL and PI can be used to predict the LL and PI-LL values after surgery, as in our model.…”

Section: Discussionmentioning

confidence: 91%

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

Bui,

Le,

Hoang

et al. 2023

Preprint

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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.

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Preoperative Radiographic Prediction Tool for Early Postoperative Segmental and Lumbar Lordosis Alignment After Transforaminal Lumbar Interbody Fusion

Cited by 2 publications

References 36 publications

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

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

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

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