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STUDY DESIGN: Retrospective study. OBJECTIVES: The objective of this investigation was to formulate and internally verify a customized machine learning (ML) framework for forecasting cerebrospinal fluid leakage (CSFL) in lumbar fusion surgery. This was accomplished by integrating imaging parameters and employing the SHapley Additive exPlanation (SHAP) technique to elucidate the interpretability of the model. SUMMARY OF BACKGROUND DATA: Given the increasing incidence and surgical volume of spinal degeneration worldwide, accurate predictions of postoperative complications are urgently needed. SHAP-based interpretable ML models have not been used for CSFL risk factor analysis in lumbar fusion surgery. METHODS: Clinical and imaging data were retrospectively collected from 3505 patients who underwent lumbar fusion surgery. Six distinct machine learning models were formulated: extreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GaussianNB), and K-nearest neighbors (KNN) models. Evaluation of model performance on the test dataset was performed using performance metrics, and the analysis was executed through the SHAP framework. RESULTS: CSFL was detected in 95 out of 3505 patients (2.71%). Notably, the XGBoost model exhibited outstanding accuracy in forecasting CSFLs, with high precision (0.9815), recall (0.6667), accuracy (0.8182), F1 score (0.7347), and AUC (0.7343). Additionally, through SHAP analysis, significant predictors of CSFL were identified, including ligamentum flavum thickness, zygapophysial joint degeneration grade, central spinal stenosis grade, decompression segment count, decompression mode, intervertebral height difference, Cobb angle, intervertebral height index difference, operation mode, lumbar segment lordosis angle difference, Meyerding grade of lumbar spondylolisthesis, and revision surgery. CONCLUSION: The combination of the XGBoost model with the SHAP is an effective tool for predicting the risk of CSFL during lumbar fusion surgery. Its implementation could aid clinicians in making informed decisions, potentially enhancing patient outcomes and lowering healthcare expenses. This study advocates for the adoption of this approach in clinical settings to enhance the evaluation of CSFL risk among patients undergoing lumbar fusion.
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OBJECTIVE: To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs). METHODS: EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision. RESULTS: The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485). CONCLUSIONS: This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.
