Chien‑Min Chena, b, c†, Pei‑Chen Chend†, Ying‑Chieh Chena, Guan‑Chyuan Wange*
aDivision of Neurosurgery, Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan, bSchool of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, cCollege of Nursing and Health Sciences, Dayeh University, Changhua, Taiwan, dDepartment of Obstetrics and Gynecology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan, eDivision of Neurosurgery, Department of Surgery, Mennonite Christian Hospital, Hualien, Taiwan
†Both authors contributed equally to this work.
Open Access funded by Buddhist Compassion Relief Tzu Chi Foundation
Abstract
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.
Keywords: Artificial intelligence, Artificial neural network, Deep learning, Machine learning, Spinal endoscope
