Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

Objective: The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined traditionally as no repeat cerebrospinal fluid diversion surgery needed within 6 months. Nonetheless, the performance of the logistic regression (LR) model in the original 2009 study was modest, with an area under the receiver operating characteristic curve (AUROC) of 0.68.

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

Background And Purpose: To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center.

Materials And Methods: We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available.

Using an artificial intelligence software improves emergency medicine physician intracranial haemorrhage detection to radiologist levels.

Background: Tools to increase the turnaround speed and accuracy of imaging reports could positively influence ED logistics. The Caire ICH is an artificial intelligence (AI) software developed for ED physicians to recognise intracranial haemorrhages (ICHs) on non-contrast enhanced cranial CT scans to manage the clinical care of these patients in a timelier fashion.

Methods: A dataset of 532 non-contrast cranial CT scans was reviewed by five board-certified emergency physicians (EPs) with an average of 14.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date.

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection.

Background: Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes.

Methods: A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included.

Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage.

Background: Intracranial hemorrhage (ICH) requires emergent medical treatment for positive outcomes. While previous artificial intelligence (AI) solutions achieved rapid diagnostics, none were shown to improve the performance of radiologists in detecting ICHs. Here, we show that the Caire ICH artificial intelligence system enhances a radiologist's ICH diagnosis performance.

The Scope, Growth, and Inequities of the Global Neurosurgery Literature: A Bibliometric Analysis.

Background: Here, we evaluate the evolution and growth of global neurosurgery publications over time, further focusing on the contributions and impact of authors in low- and middle-income countries (LMICs).

Methods: In this systematic bibliometric analysis, we conducted a two-stage blinded screening process of global neurosurgery publications from 5 databases from inception through July 2021. Articles involving multi-national/multi-institutional research collaborations, detailing any area of global neurosurgery collaboration, or influencing global neurosurgery practice were included.

Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

Background: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit.

Objective: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR).

Methods: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015.

Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.

Background: Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints.

Objective: To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

Objective: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

Background: Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches.

Objective: To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings.

Identification of successful cerebral reperfusions (mTICI ≥2b) using an artificial intelligence strategy.

Background: The modified thrombolysis in cerebral infarction (mTICI) scale is a widely used and validated qualitative tool to evaluate angiographic intracerebral inflow following endovascular thrombectomy (EVT). We validated a machine-learning (ML) algorithm to grade digital subtraction angiograms (DSA) using the mTICI scale.

Materials And Methods: We included angiograms of identified middle cerebral artery (MCA) occlusions who underwent EVT.

Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities.

Background The use of CT imaging enhanced by artificial intelligence to effectively diagnose COVID-19, instead of or in addition to reverse transcription-polymerase chain reaction (RT-PCR), can improve widespread COVID-19 detection and resource allocation. Methods 904 axial lung window CT slices from 338 patients in 17 countries were collected and labeled. The data included 606 images from COVID-19 positive patients (confirmed via RT-PCR), 224 images of a variety of other pulmonary diseases including viral pneumonias, and 74 images of normal patients.

Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer.

Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling leading to what is known as the . Understanding how tumor cells can disrupt bone homeostasis through their interactions with the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease.