Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor
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 u...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2022-01-01
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| Series: | Tzu Chi Medical Journal |
| Subjects: | |
| Online Access: | http://www.tcmjmed.com/article.asp?issn=1016-3190;year=2022;volume=34;issue=4;spage=434;epage=440;aulast=Chen |
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| _version_ | 1850245314866315264 |
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| author | Chien-Min Chen Pei-Chen Chen Ying-Chieh Chen Guan-Chyuan Wang |
| author_facet | Chien-Min Chen Pei-Chen Chen Ying-Chieh Chen Guan-Chyuan Wang |
| author_sort | Chien-Min Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0c0256f990e341a3bf205f49ab873d2d |
| institution | OA Journals |
| issn | 1016-3190 2223-8956 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Tzu Chi Medical Journal |
| spelling | doaj-art-0c0256f990e341a3bf205f49ab873d2d2025-08-20T01:59:30ZengWolters Kluwer Medknow PublicationsTzu Chi Medical Journal1016-31902223-89562022-01-0134443444010.4103/tcmj.tcmj_281_21Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridorChien-Min ChenPei-Chen ChenYing-Chieh ChenGuan-Chyuan WangObjectives: 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.http://www.tcmjmed.com/article.asp?issn=1016-3190;year=2022;volume=34;issue=4;spage=434;epage=440;aulast=Chenartificial intelligenceartificial neural networkdeep learningmachine learningspinal endoscope |
| spellingShingle | Chien-Min Chen Pei-Chen Chen Ying-Chieh Chen Guan-Chyuan Wang Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor Tzu Chi Medical Journal artificial intelligence artificial neural network deep learning machine learning spinal endoscope |
| title | Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| title_full | Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| title_fullStr | Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| title_full_unstemmed | Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| title_short | Use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| title_sort | use artificial neural network to recommend the lumbar spinal endoscopic surgical corridor |
| topic | artificial intelligence artificial neural network deep learning machine learning spinal endoscope |
| url | http://www.tcmjmed.com/article.asp?issn=1016-3190;year=2022;volume=34;issue=4;spage=434;epage=440;aulast=Chen |
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