Table 3.
A list of papers on ANN used in spine outcome prediction
| Author | Year | Country | No. of sample size |
Model type | Conditions | Comparison with non-ANN models | Main focus | Results/conclusion(s) | |
|---|---|---|---|---|---|---|---|---|---|
| Training | Testing | ||||||||
| Azimi et al. [3] | 2014 | Iran | 84 | 84 | ANN model | LSS | Yes | To develop an ANN model for predicting 2-year surgical satisfaction, and to compare the new model with traditional predictive tools in patients with lumbar spinal canal stenosis | The ANN model displayed a better accuracy rate in 96.9% of patients, a better Hosmer-Lemeshow statistic in 42.4% of patients, and a better receiver operating characteristic-AUC in 80% of patients, compared with the LR model. ANNs can predict 2-year surgical satisfaction in LSS patients with a high level of accuracy. |
| Azimi et al. [66] | 2015 | Iran | 201 | 201 | ANN model | Recurrent LDH | Yes | To develop an ANN model to predict recurrent LDH | Compared with the LR model, the ANN model was associated with superior results: accuracy rate, 94.1%; H-L statistic, 40.2%; and AUC, 0.83% of patients. ANNs could be used to predict the diagnostic statues of recurrent and nonrecurrent group of LDH patients before the first or index microdiscectomy. |
| Azimi et al. [4] | 2016 | Iran | 102 | 101 | ANN model | LDH | Yes | To develop an ANNs model for predict successful surgery outcome in LDH | Compared to the LR model, the ANN model showed better results: accuracy rate, 95.8%; H-L statistic, 41.5%; and AUC, 0.82% of patients. ANNs can predict successful surgery outcome with a high level of accuracy in LDH patients. |
| Azimi et al. [67] | 2017 | Iran | 174 | 86 | ANN model | LSCS | Yes | To accurately select patients for surgery or non-surgical options and to compare such with the traditional clinical decisionmaking approach in LSCS patients | The ANN model displayed better accuracy rate (97.8%), a better H-L statistic (41.1%) which represented a good-fit calibration, and a better AUC (89.0%), compared to the LR model. ANN model could predict the optimal treatment choice for LSCS patients in clinical setting and is superior to LR model. |
| Karhade et al. [68] | 2019 | USA | 844 (80%) | 209 (20%) | ML algorithm | SEA | NR | To develop ML algorithms for prediction of in-hospital and 90-day postdischarge mortality in SEA | Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. ML algorithms showed promise on internal validation for prediction of 90-day mortality in SEA. |
| Stopa et al. [69] | 2019 | USA | 144 Patients | 144 Patients | ML algorithm | Lumbar disc disorders surgery | NR | To predict nonroutine discharge for patients undergoing surgery for lumbar disc disorders | A nonroutine discharge rate of 6.9% (n=10). The neural network algorithm generalized well to the institutional data, with a c-statistic of 0.89. ML showed that a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge, |
| Zhang et al. [70] | 2019 | China | 58 | 22 | ML | Lumbar vertebral strength of elderly men | NR | To predict vertebral strength based on clinical quantitative computed tomography images by using machine learning | High accuracy was achieved to predict vertebral strength. This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk. |
| Hopkins et al. [71] | 2019 | USA | 17,448 | 5,816 | DNN | Spinal fusions | NR | To develop an AI model to predict 30-day readmissions after posterior lumbar fusion | Mean positive predictive value was 78.5%. Mean negative predictive value was 97%. The DNN model was able to predict those patients who would not require readmission. |
| Hopkins et al. [72] | 2020 | USA | 3,034 | 1,012 | DNN | Spinal fusions | NR | To develop an AI model for predict surgical site infections after posterior spinal fusions | The five highest weighted variables were congestive heart failure, chronic pulmonary failure, hemiplegia/ paraplegia, multilevel fusion, and cerebrovascular disease, respectively. Notable factors that were protective against infection were intensive care unit admission, increasing Charlson Comorbidity Index score, race (White), and being male. They reported that AI was relevant and impressive tools that should be employed in the clinical decision making for patients. |
ANN, artificial neural network; LSS, lumbar spinal stenosis; AUC, area under the curve; LR, logistic regression; LDH, lumbar disk herniation; H-L statistic, Hosmer-Lemeshow statistic; LSCS, lumbar spinal canal stenosis; ML, machine learning; SEA, spinal epidural abscess; NR, not reported; DNN, deep neural network; AI, artificial intelligence.