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ORIGINAL ARTICLE
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Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor


1 Division of Neurosurgery, Department of Surgery, Changhua Christian Hospital, Changhua; School of Medicine, Kaohsiung Medical University, Kaohsiung; College of Nursing and Health Sciences, Dayeh University, Changhua, Taiwan
2 Department of Obstetrics and Gynecology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
3 Division of Neurosurgery, Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan
4 Division of Neurosurgery, Department of Surgery, Mennonite Christian Hospital, Hualien, Taiwan

Correspondence Address:
Guan-Chyuan Wang,
Division of Neurosurgery, Department of Surgery, Mennonite Christian Hospital, 44, Min-Chuan Road, Hualien
Taiwan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/tcmj.tcmj_281_21

Objectives: The transforaminal and interlaminar approaches are the two main surgical corridors of full endoscopic lumbar surgery. However, there are no quantifying methods for assessing the best surgical approach for each patient. This study aimed to establish an artificial intelligence (AI) model using an artificial neural network (ANN). Materials and Methods: Patients who underwent full endoscopic lumbar spinal surgery were enrolled in this research. Fourteen pre-operative factors were fed into the ANN. A three-layer deep neural network was constructed. Patient data were divided into the training, validation, and testing datasets. Results: There were 899 patients enrolled. The accuracy of the training, validation, and test datasets were 87.3%, 85.5%, and 85.0%, respectively. The positive predictive values for the transforaminal and interlaminar approaches were 85.1% and 89.1%, respectively. The area under the curve of the receiver operating characteristic was 0.91. The SHapley Additive exPlanations algorithm was utilized to explain the relative importance of each factor. The surgical lumbar level was the most important factor, followed by herniated disc localization and migrating disc zone level. Conclusion: ANN can effectively learn from the choice of an experienced spinal endoscopic surgeon and can accurately predict the appropriate surgical approach.


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    -  Chen CM
    -  Chen PC
    -  Chen YC
    -  Wang GC
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