Table 1.
Reviewed Studies of Preoperative Patient Selection and Planning in Spine Surgery.
| Author, year | Pathology or procedure | ML algorithms | Prediction outputs | Number of patients | Avg. age | % female | Data source |
|---|---|---|---|---|---|---|---|
| Kalagara, 2018 | Lumbar laminectomy | Boosting/ensemble learning | Readmissions/reoperations | 4030 | 63 | -- | ACS-NSQIP database |
| Stopa, 2019 | Spine fusion | Deep Learning/ANN | Discharge/LOS | 144 | 50 | 45.10 | Single center |
| Ames, 2019 | ASD | Cluster analysis | Pre-op selection/planning | 570 | 56.8 | 78.80 | Multicenter ASD databases |
| Goyal, 2019 | Spine fusion | Regression analysis, boosting/ensemble learning, deep Learning/ANN, decision tree, and Bayesian networks | Discharge/LOS and readmissions/reoperations | 8872 | 57 | 48.50 | ACS-NSQIP database |
| Ogink, 2019 | Spondylolisthesis | Deep learning/ANN, SVM, decision tree, and Bayesian networks | Discharge/LOS | 1868 | 63 | 63.00 | ACS-NSQIP database |
| Kuo, 2018 | Spinal fusion | Regression analysis, SVM, decision tree, and Bayesian networks | Cost prediction | 532 | 62.4 | 58.60 | Single center |
| Lerner, 2019 | Posterior lumbar spinal fusion | Cluster analysis | Pre-op selection/planning | 18770 | 51.3 | 56.10 | IBM MarketScan® commercial database |
| Siccoli, 2019 | Lumbar decompression | Boosting/ensemble learning and decision tree | Discharge/LOS and readmissions/reoperations | 635 | 62 | 48.00 | Single center |
| Chia, 2017 | Cerebral palsy | Deep learning/ANN | Pre-op selection/planning | 242 | -- | -- | Single center |
| Huang, 2019 | ACDF | Bayesian networks, SVM, and regression analysis | Pre-op selection/planning | 321 | -- | -- | Single center |
| Varghese, 2018 | Spinal fusion | Decision tree and regression analysis | Pre-op selection/planning | -- | -- | -- | Single center |
| Karhade, 2018 | Lumbar degeneration | Deep learning/ANN, decision tree, SVM, and Bayesian networks | Discharge/LOS | 5273 | 53 | 46.90 | ACS-NSQIP database |
| Hopkins, 2019 | Posterior lumbar spinal fusion | Deep learning/ANN | Readmissions/reoperations | 5816 | -- | -- | ACS-NSQIP database |
| Ogink, 2019 | Lumbar spinal stenosis | Deep Learning/ANN, decision tree, SVM, and Bayesian networks | Discharge/LOS | 9338 | 67 | 47.30 | ACS-NSQIP database |
| Karnuta, 2019 | Spinal fusion | Bayesian networks | Discharge/LOS and cost prediction | 3807 | -- | 57.80 | New York state SPARCS database |
| Khatri, 2019 | Spinal fusion | Decision tree | Pre-op selection/planning | -- | -- | -- | Single center |
| Bekelis, 2014 | ACDF | Regression analysis | Discharge/LOS and readmissions/reoperations | 2732 | 55.7 | 46.30 | ACS-NSQIP database |
| Assi, 2014 | Scoliosis | Regression analysis | Pre-op selection/planning | 141 | -- | -- | Single center |
Abbreviations: ASD, adult spinal deformity; ACDF, anterior cervical discectomy and fusion; ANN, artificial neural network; SVM, support vector machine; LOS, length of stay; ACS-NSQIP, American College of Surgery-National Surgical Quality Improvement Program; SPARCS, Statewide Planning and Research Cooperative System.