The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs.

Purpose: To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment.

Materials And Methods: This retrospective study used a consecutive cohort of 596 chest radiographs from four US hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections.

Ego4D: Around the World in 3,000 Hours of Egocentric Video.

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries.

Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head.

Background And Purpose: Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) with consensus neuroradiologists' interpretations in detecting mass effect and vasogenic edema.

Materials And Methods: A retrospective stand-alone performance assessment was conducted on data sets of noncontrast CT head cases acquired between 2016 and 2022 for each finding.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use.

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm.

: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation. : With IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 and March 2022 for the following terms: "motion artifacts", "respiratory motion", "technically inadequate", and "suboptimal" or "limited exam".

Head CT deep learning model is highly accurate for early infarct estimation.

Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r > 0.

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.

Importance: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care.

Objective: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax.

Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT.

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.

Use of Artificial Intelligence in Clinical Neurology.

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities.

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.

Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series.

Novel Galectin-3 Roles in Neurogenesis, Inflammation and Neurological Diseases.

Galectin-3 (Gal-3) is an evolutionarily conserved and multifunctional protein that drives inflammation in disease. Gal-3's role in the central nervous system has been less studied than in the immune system. However, recent studies show it exacerbates Alzheimer's disease and is upregulated in a large variety of brain injuries, while loss of Gal-3 function can diminish symptoms of neurodegenerative diseases such as Alzheimer's.

Consensus disease definitions for neurologic immune-related adverse events of immune checkpoint inhibitors.

Expanding the US Food and Drug Administration-approved indications for immune checkpoint inhibitors in patients with cancer has resulted in therapeutic success and immune-related adverse events (irAEs). Neurologic irAEs (irAE-Ns) have an incidence of 1%-12% and a high fatality rate relative to other irAEs. Lack of standardized disease definitions and accurate phenotyping leads to syndrome misclassification and impedes development of evidence-based treatments and translational research.

Unilateral Relapsing Primary Angiitis of the CNS: An Entity Suggesting Differences in the Immune Response Between the Cerebral Hemispheres.

Objective: To determine whether studying patients with strictly unilateral relapsing primary angiitis of the CNS (UR-PACNS) can support hemispheric differences in immune response mechanisms, we reviewed characteristics of a group of such patients.

Methods: We surveiled our institution for patients with UR-PACNS, after characterizing one such case. We defined UR-PACNS as PACNS with clinical and radiographic relapses strictly recurring in 1 brain hemisphere, with or without hemiatrophy.

Neuromyelitis optica after splenectomy: A secondary autoimmune phenomenon.

We describe the case of a 53-year-old woman who undergoes total splenectomy and later presents with aquaporin-4 antibody positive neuromyelitis optica (NMO). The occurrence of NMO after acquired immunosuppression raises the possibility of NMO as a form of secondary autoimmunity.

Teaching the Neurological Examination in a Rapidly Evolving Clinical Climate.

The neurological examination remains the essence of neurology. It allows symptoms to be assessed, diagnoses to be made, and dynamic functions to be followed. Skill in the neurological examination has faced increasing challenges from the encroachment of diagnostic imaging, but has maintained its clinical utility.

Neurology Training Worldwide.

Neurology training is essential for providing neurologic care globally. Large disparities in availability of neurology training exist between higher- and lower-income countries. This review explores the worldwide distribution of neurology training programs and trainees, the characteristics of training programs in different parts of the world, and initiatives aimed at increasing access to neurology training in under-resourced regions.

Cuprizone demyelination induces a unique inflammatory response in the subventricular zone.

Background: Cuprizone leads to demyelination of the corpus callosum (CC) and activates progenitor cells in the adjacent subventricular zone (SVZ), a stem cell niche which contributes to remyelination. The healthy SVZ contains semi-activated microglia and constitutively expresses the pro-inflammatory molecule galectin-3 (Gal-3) suggesting the niche uniquely regulates inflammation.

Methods: We studied the inflammatory response to cuprizone in the SVZ and CC in Gal-3 knockout mice using immunohistochemistry and with the in vitro neurosphere assay.

Loss of galectin-3 decreases the number of immune cells in the subventricular zone and restores proliferation in a viral model of multiple sclerosis.

Multiple sclerosis (MS) frequently starts near the lateral ventricles, which are lined by subventricular zone (SVZ) progenitor cells that can migrate to lesions and contribute to repair. Because MS-induced inflammation may decrease SVZ proliferation and thus limit repair, we studied the role of galectin-3 (Gal-3), a proinflammatory protein. Gal-3 expression was increased in periventricular regions of human MS in post-mortem brain samples and was also upregulated in periventricular regions in a murine MS model, Theiler's murine encephalomyelitis virus (TMEV) infection.