Academic Journal
Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis
| العنوان: | Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis |
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| المؤلفون: | Cui, Yunpeng, Shi, Xuedong, Qin, Yong, Wan, Qiwei, Cao, Xuyong, Che, Xiaotong, Pan, Yuanxing, Wang, Bing, Lei, Mingxing, Liu, Yaosheng |
| المصدر: | International Journal of Surgery ; ISSN 1743-9159 |
| بيانات النشر: | Ovid Technologies (Wolters Kluwer Health) |
| سنة النشر: | 2024 |
| الوصف: | Background: Identification of patients with high risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence brings a promising opportunity to develop accurate prediction models. Methods: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study’s model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. Results: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1097/js9.0000000000001169 |
| DOI: | 10.1097/JS9.0000000000001169 |
| الاتاحة: | http://dx.doi.org/10.1097/js9.0000000000001169 https://journals.lww.com/10.1097/JS9.0000000000001169 |
| Rights: | http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.328D49CE |
| قاعدة البيانات: | BASE |
| DOI: | 10.1097/js9.0000000000001169 |
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