The establishment of artificial intelligence surgical selection system based on deep learning and its application in lumbar endoscopic surgery
10.3760/cma.j.cn121113-20240630-00379
- VernacularTitle:基于深度学习的人工智能术式选择系统的建立及在腰椎内镜手术中的应用
- Author:
Kaihui ZHANG
1
;
Baoshan XU
;
Yong MIAO
;
Lin CONG
;
Lilong DU
;
Haiwei XU
;
Ning LI
Author Information
1. 天津市天津医院微创脊柱外科，天津 300211
- Keywords:
Deep learning;
Artificial intelligence;
Lumbar vertebrae;
Endoscopy
- From:
Chinese Journal of Orthopaedics
2024;44(17):1143-1150
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish an artificial intelligence-based surgical selection system utilizing deep learning to assist in the decision-making process for lumbar endoscopic surgery.Methods:General data of 1,110 patients who underwent percutaneous transforaminal endoscopic discectomy, 804 patients who underwent percutaneous interlaminar endoscopic discectomy, 923 patients who underwent mobile microendoscopic discectomy and 623 patients who underwent unilateral biportal endoscopic in Tianjin Hospital from January 2018 to June 2023 were included in the study. Clinical outcomes were assessed using the visual analogue scale (VAS) for leg and back pain, the Oswestry disability index (ODI), and MacNab criteria both before surgery and 12 months postoperatively. Using a random number table method, patients were divided into a training dataset (2,768 cases) and a test dataset (692 cases) at a ratio of 4∶1. Patient clinical symptoms, physical signs, and multi-modal imaging data were input into a deep learning model. This model was structured into three main modules: intervertebral disc detection, surgical necessity identification, and surgical recommendation. The final surgical method was determined using a convolutional neural network incorporating U-Net for segmentation and ResNet for classification. The accuracy and recall rates of each module were evaluated using the test dataset.Results:Compared to preoperative values, all patients showed significant improvements at the 12-month postoperative follow-up. For patients who underwent percutaneous transforaminal endoscopic discectomy, percutaneous interlaminar endoscopic discectomy, mobile microendoscopic discectomy, and unilateral biportal endoscopic surgery, the VAS scores for leg pain decreased from 7.69±0.80, 7.82±0.88, 7.62±0.69, and 7.56±1.00 preoperatively to 1.44±1.09, 1.35±0.82, 1.51±1.08, and 1.43±0.91 postoperatively. Similarly, the VAS scores for back pain decreased from 5.73±0.83, 6.17±0.99, 6.11±0.88, and 6.46±0.95 to 0.93±0.75, 1.01±0.67, 1.40±0.72, and 1.27±0.70, respectively. Additionally, the ODI significantly decreased from 39.91%±4.50%, 40.05%±8.05%, 47.08%±9.50%, and 44.43%±4.71% preoperatively to 5.77%±2.22%, 6.05%±2.31%, 8.51%±2.16%, and 9.51%±3.70% postoperatively, with all differences being statistically significant ( P<0.05). The excellent rate according to the MacNab criteria was 93.12% (3,222/3,460). In the deep learning model, the multi-modal data of 2,768 patients were input in the training set for deep learning to form a surgical identification and operation recommendation system, and the preoperative data of 692 patients were input in the test set to compare with the final operation method. In the intervertebral disc location module, the accuracy of location and designation of the five lumbar intervertebral discs was 97.1%(672/692). In the module of intervertebral disc need for surgery, the accuracy was 94.8%(3,280/3,460) and the recall rate was 91.9%(636/692). As for patients, the accuracy rate was 91.9%(636/692). In the operation recommendation module, the accuracy rate of operation recommendation based on intervertebral disc was 89.5%(569/636), and the accuracy rate of surgical recommendation based on patient was 82.2%(569/692). Conclusion:In this study, an artificial intelligent surgical procedures selection system based on deep learning was established, which could effectively integrate relevant data and accurately guide the selection of lumbar endoscopic surgery.
