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.

After extensive community and health industry consultation, the final report of the National Health and Hospitals Reform Commission, A healthier future for all Australians, was presented to the Australian Government on 30 June 2009. The reform agenda aims to tackle major access and equity issues that affect health outcomes for people now; redesign our health system so that it is better positioned to respond to emerging challenges; and create an agile, responsive and self‐improving health system for long‐term sustainability. The 123 recommendations are grouped in four themes: Taking responsibility: supporting greater individual and collective action to build good health and wellbeing. Connecting care: delivering comprehensive care for people over their lifetime, by strengthening primary health care, reshaping hospitals, improving subacute care, and opening up greater consumer choice and competition in aged care services. Facing inequities: taking action to tackle the causes and impact of health inequities, focusing on Aboriginal and Torres Strait Islander people, people in rural and remote areas, and access to mental health and dental services. Driving quality performance: having leadership and systems to achieve the best use of people, resources and knowledge, including “one health system” with national leadership and local delivery, revised funding arrangements, and changes to health workforce education, training and practice.

No abstract