Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F (2,19) = 6.56, p < 0.01) and specificity (F (2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

Background Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deeplearning model. MethodsIn this retrospective study, a deep-learning model was trained on 821 681 images (284 649 patients) from five data sets from Australia, Europe, and the USA. 2568 enriched chest x-ray cases from adult patients (≥16 years) who had at least one frontal chest x-ray were included in the test dataset; cases were representative of inpatient, outpatient, and emergency settings. 20 radiologists reviewed cases with and without the assistance of the deep-learning model with a 3-month washout period. We assessed the change in accuracy of chest x-ray interpretation across 127 clinical findings when the deep-learning model was used as a decision support by calculating area under the receiver operating characteristic curve (AUC) for each radiologist with and without the deep-learning model. We also compared AUCs for the model alone with those of unassisted radiologists. If the lower bound of the adjusted 95% CI of the difference in AUC between the model and the unassisted radiologists was more than -0•05, the model was considered to be non-inferior for that finding. If the lower bound exceeded 0, the model was considered to be superior. Findings Unassisted radiologists had a macroaveraged AUC of 0•713 (95% CI 0•645-0•785) across the 127 clinical findings, compared with 0•808 (0•763-0•839) when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deeplearning model. Unassisted radiologists had a macroaveraged mean AUC of 0•713 (0•645-0•785) across all findings, compared with 0•957 (0•954-0•959) for the model alone. Model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. Interpretation This study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice. Funding Annalise.ai.

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OBJECTIVESystematic multidisciplinary approaches to improving quality and safety in complex surgical care have shown promise. Complication rates from complex spine surgery range from 10% to 90% for all surgeries, and the overall mortality rate is 1%–4%. These rates suggest the need for improved perioperative complex spine surgery processes designed to minimize risk and improve quality.METHODSThe Group Health Research Institute and Virginia Mason Medical Center implemented a systematic multidisciplinary protocol, the Seattle Spine Team Protocol, in 2010. This protocol involves the following elements: 1) a comprehensive multidisciplinary conference including clinicians from neurosurgery, anesthesia, orthopedics, internal medicine, behavioral health, and nursing, collaboratively deciding on each patient's suitability for surgery; 2) a mandatory patient education course that reviews the risks of surgery, preparation for the surgery, and postoperative care; 3) a dual-attending-surgeon approach involving 1 neurosurgeon and 1 orthopedic spine surgeon; 4) a dedicated specialist complex spine anesthesia team; and 5) rigorous intraoperative monitoring of a patient's blood loss and coagulopathy. The authors identified 71 patients who underwent complex spine surgery involving fusion of 6 or more levels before implementation of the protocol (surgery between 2008 and 2010) and 69 patients who underwent complex spine surgery after the implementation of the protocol (2010 and 2012). All patient demographic variables, including age, sex, body mass index, smoking status, diagnosis of diabetes and/or osteoporosis, previous surgery, and the nature of the spinal deformity, were comprehensively assessed. Also comprehensively assessed were surgical variables, including operative time, number of levels fused, and length of stay. The authors assessed overall complication rates at 30 days and 1 year and detailed deaths, cardiovascular events, infections, instrumentation failures, and CSF leaks. Chi-square and Wilcoxon rank-sum tests were used to assess differences in patient characteristics for patients with a procedure in the preimplementation period from those in the postimplementation period under a Poisson distribution model.RESULTSPatients who underwent surgery after implementation of the Seattle Spine Team Protocol had a statistically significant reduction (relative risk 0.49 [95% CI 0.30–0.78]) in all measured complications, including cardiovascular events, wound infections, other perioperative infections, and implant failures within 30 days after surgery; the analysis was adjusted for age and Charlson comorbidity score. A trend toward fewer deaths in this group was also found.CONCLUSIONSThis type of systematic quality improvement strategy can improve quality and patient safety and might be applicable to other complex surgical disciplines. Implementation of these strategies in the treatment of adult spinal deformity will likely lead to better patient outcomes.

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