Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have remained constant for decades. The application of machine learning has the potential to substantially improve clinical workflow efficiency, pathology detection accuracy, error rates and clinical decision making in cardiology.

Focusing a Bioethics Lens on the Development and Use of Artificial Intelligence in Medicine and Neurosurgery.

The rapid pace of development and application of digital technology and data science, including artificial intelligence (AI), is transforming our world. In this chapter, we address the question: "Is bioethics relevant to how we should develop, govern, and use AI in healthcare, specifically in neurosurgery?" We recognize that medical decision-making involves uncertainty and is complex, and predicting potential outcomes is difficult. We conclude that the use of AI in neurosurgery is not inherently unethical.

Opportunities for developing neural stem cell treatments for acute ischemic stroke: A systematic review and gap analysis.

Ischemic stroke is a leading cause of disability and death. Current treatments are limited. Stem cell therapy has been highlighted as a potentially effective treatment to mitigate damage and restore function, but efficacy results are mixed.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

Objectives: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.

This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated.

Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning.

Objectives: The aim of the study was to train and test supervised machine-learning classifiers to predict acoustic hearing preservation after CI using preoperative clinical data.

Study Design: Retrospective predictive modeling study of prospectively collected single-institution CI dataset.

Methods: One hundred and seventy-five patients from a REDCap database including 761 patients >18 years who underwent CI and had audiometric testing preoperatively and one month after surgery were included.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation.

Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery.

Background: Patients with pituitary lesions experience decrements in quality of life (QoL) and treatment aims to arrest or improve QoL decline.

Objective: To detect associations with QoL in trans-nasal endoscopic skull base surgery patients and train supervised learning classifiers to predict QoL improvement at 12 months.

Methods: A supervised learning analysis of a prospective multi-institutional dataset (451 patients) was conducted.

Charting the potential of brain computed tomography deep learning systems.

Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients.

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

Objectives: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.

Design: This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting.

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

Objectives: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.

Design: A retrospective case-control study was undertaken.

Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

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 deep-learning model.

Clinical outcomes associated with robotic and computer-navigated total knee arthroplasty: a machine learning-augmented systematic review.

Background: Robotic (RTKA) and computer-navigated total knee arthroplasty (CNTKA) are increasingly replacing manual techniques in orthopaedic surgery. This systematic review compared clinical outcomes associated with RTKA and CNTKA and investigated the utility of natural language processing (NLP) for the literature synthesis.

Methods: A comprehensive search strategy was implemented.

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information.

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes.

Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

Background: Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial.

Team Approach: Safety and Value in the Practice of Complex Adult Spinal Surgery.

Surgical management of complex adult spinal deformities is of high risk, with a substantial risk of operative mortality. Current evidence shows that potential risk and morbidity resulting from surgery for complex spinal deformity may be minimized through risk-factor optimization. The multidisciplinary team care model includes neurosurgeons, orthopaedic surgeons, physiatrists, anesthesiologists, hospitalists, psychologists, physical therapists, specialized physician assistants, and nurses.

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes.

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

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.

Risk stratification in deep brain stimulation surgery: Development of an algorithm to predict patient discharge disposition with 91.9% accuracy.

Clinical decision making is susceptible to biases and can be improved with the application of predictive models and decision support systems (DSS). The purpose of this study was to develop a predictive risk stratification model and DSS that could accurately predict whether a patient was likely to be of high- or low-acuity discharge disposition (DD) status subsequent to DBS surgery. Data were collected for 135 DBS patients by reviewing medical records.

Correction of severe spinopelvic mismatch: decreased blood loss with lateral hyperlordotic interbody grafts as compared with pedicle subtraction osteotomy.

OBJECTIVE Pedicle subtraction osteotomy (PSO) provides extensive correction in patients with fixed sagittal plane imbalance but is associated with high estimated blood loss (EBL). Anterior column realignment (ACR) with lateral graft placement and sectioning of the anterior longitudinal ligament allows restoration of lumbar lordosis (LL). The authors compare peri- and postoperative measures in 2 groups of patients undergoing correction of a sagittal plane imbalance, either through PSO or the use of lateral lumbar fusion and ACR with hyperlordotic (20°-30°) interbody cages, with stabilization through standard posterior instrumentation in all cases.

The Seattle spine score: Predicting 30-day complication risk in adult spinal deformity surgery.

Background: Complication rates in complex spine surgery range from 25% to 80% in published studies. Numerous studies have shown that surgeons are not able to accurately predict whether patients are likely to face post-operative complications, in part due to biases based on individual experience. The purpose of this study was to develop and evaluate a predictive risk model and decision support system that could accurately predict the likelihood of 30-day postoperative complications in complex spine surgery based on routinely measured preoperative variables.