BACKGROUND: The aim of this study was to develop an artificial neural network (ANN) model to predict recurrent lumbar disk herniation (LDH). METHODS: An ANN model and a logistic regression model were used to predict recurrent LDH. The age, sex, duration of symptoms, smoking status, recurrent LDH, level of herniation, type of herniation, sports activity; occupational lifting, occupational driving, duration of symptoms, visual analog scale (VAS), the Zung Depression Scale (ZDS), and the Japanese Orthopaedic Association (JOA) Score, were determined as the input variables for the established ANN model. The Macnab classification, VAS, and JOA were used for outcome assessment. ANNs on data from LDH patients, who underwent surgery, were trained to predict LDH using several input variables. The patients were divided into a recurrent LDH group (R group) and a primary LDH group (P group). Sensitivity analysis was applied to identify the relevant variables. The receiver-operating characteristic curve, accuracy rate of predicting, and Hosmer-Lemeshow statistics were considered for evaluating the 2 models. RESULTS: A total of 402 patients were categorized into training, testing, and validation data sets consisting of 201, 101, and 100 cases, respectively. The recurrence rate was 8.7%, and the median time to recurrence was 26.2 months (SD=4 mo). The VAS of leg/back pain and JOA were improved at 1-year follow-up (P<0.05) and no significant difference was observed between the 2 groups. Surgical successful outcome was categorized as: excellent, 31.1%; good, 44.3%; fair, 18.9%; and poor, 5.7% at 1-year follow-up. Compared with the logistic regression model, the ANN model was associated with superior results: accuracy rate, 94.1%; Hosmer-Lemeshow statistic, 40.2%; and area under the curve, 0.83% of patients. CONCLUSION: The findings show that an ANNs can be used to predict the diagnostic statues of recurrent and nonrecurrent group of LDH patients before the first or index microdiscectomy.
BACKGROUND: The aim of this study was to develop an artificial neural network (ANN) model to predict recurrent lumbar disk herniation (LDH). METHODS: An ANN model and a logistic regression model were used to predict recurrent LDH. The age, sex, duration of symptoms, smoking status, recurrent LDH, level of herniation, type of herniation, sports activity; occupational lifting, occupational driving, duration of symptoms, visual analog scale (VAS), the Zung Depression Scale (ZDS), and the Japanese Orthopaedic Association (JOA) Score, were determined as the input variables for the established ANN model. The Macnab classification, VAS, and JOA were used for outcome assessment. ANNs on data from LDH patients, who underwent surgery, were trained to predict LDH using several input variables. The patients were divided into a recurrent LDH group (R group) and a primary LDH group (P group). Sensitivity analysis was applied to identify the relevant variables. The receiver-operating characteristic curve, accuracy rate of predicting, and Hosmer-Lemeshow statistics were considered for evaluating the 2 models. RESULTS: A total of 402 patients were categorized into training, testing, and validation data sets consisting of 201, 101, and 100 cases, respectively. The recurrence rate was 8.7%, and the median time to recurrence was 26.2 months (SD=4 mo). The VAS of leg/back pain and JOA were improved at 1-year follow-up (P<0.05) and no significant difference was observed between the 2 groups. Surgical successful outcome was categorized as: excellent, 31.1%; good, 44.3%; fair, 18.9%; and poor, 5.7% at 1-year follow-up. Compared with the logistic regression model, the ANN model was associated with superior results: accuracy rate, 94.1%; Hosmer-Lemeshow statistic, 40.2%; and area under the curve, 0.83% of patients. CONCLUSION: The findings show that an ANNs can be used to predict the diagnostic statues of recurrent and nonrecurrent group of LDH patients before the first or index microdiscectomy.
Authors: Scott D Tagliaferri; Maia Angelova; Xiaohui Zhao; Patrick J Owen; Clint T Miller; Tim Wilkin; Daniel L Belavy Journal: NPJ Digit Med Date: 2020-07-09
Authors: Xiao Chen; Qingshan Deng; Qiang Wang; Xinmiao Liu; Lei Chen; Jinjin Liu; Shuangquan Li; Meihao Wang; Guoquan Cao Journal: Front Public Health Date: 2022-04-26
Authors: Cesar D Lopez; Venkat Boddapati; Joseph M Lombardi; Nathan J Lee; Justin Mathew; Nicholas C Danford; Rajiv R Iyer; Marc D Dyrszka; Zeeshan M Sardar; Lawrence G Lenke; Ronald A Lehman Journal: Global Spine J Date: 2022-02-28