Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Diagnostic stewardship for testing in an acute care hospital: A quality improvement intervention.

Objective: To evaluate the impact of a diagnostic stewardship intervention on healthcare-associated infections (HAI).

Design: Quality improvement study.

Setting: Two urban acute care hospitals.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

Purpose: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

Methods: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model.

Level of Attention to Motherese Speech as an Early Marker of Autism Spectrum Disorder.

Importance: Caregivers have long captured the attention of their infants by speaking in motherese, a playful speech style characterized by heightened affect. Reduced attention to motherese in toddlers with autism spectrum disorder (ASD) may be a contributor to downstream language and social challenges and could be diagnostically revealing.

Objective: To investigate whether attention toward motherese speech can be used as a diagnostic classifier of ASD and is associated with language and social ability.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation.

Large scale validation of an early-age eye-tracking biomarker of an autism spectrum disorder subtype.

Few clinically validated biomarkers of ASD exist which can rapidly, accurately, and objectively identify autism during the first years of life and be used to support optimized treatment outcomes and advances in precision medicine. As such, the goal of the present study was to leverage both simple and computationally-advanced approaches to validate an eye-tracking measure of social attention preference, the GeoPref Test, among 1,863 ASD, delayed, or typical toddlers (12-48 months) referred from the community or general population via a primary care universal screening program. Toddlers participated in diagnostic and psychometric evaluations and the GeoPref Test: a 1-min movie containing side-by-side dynamic social and geometric images.

Get SET Early to Identify and Treatment Refer Autism Spectrum Disorder at 1 Year and Discover Factors That Influence Early Diagnosis.

Objectives: To examine the impact of a new approach, Get SET Early, on the rates of early autism spectrum disorder (ASD) detection and factors that influence the screen-evaluate-treat chain.

Study Design: After attending Get SET Early training, 203 pediatricians administered 57 603 total screens using the Communication and Symbolic Behavior Scales Infant-Toddler Checklist at 12-, 18-, and 24-month well-baby examinations, and parents designated presence or absence of concern. For screen-positive toddlers, pediatricians specified if the child was being referred for evaluation, and if not, why not.

Does the Lack of Gender-Specific Apnea-Hypopnea Index Cutoff for Obstructive Sleep Apnea Impact Surgical Selection?

Purpose: The apnea-hypopnea index (AHI) is the parameter on which the severity of obstructive sleep apnea (OSA) is based and is also the determinant for both clinicians and third-party payers for surgical procedures. The purpose of this retrospective cross-sectional chart review is to examine differences in symptoms and AHI between men and women with OSA and whether this may impact timing and selection of surgical care.

Methods: Retrospective cross-sectional study of patients aged 18 years and older who presented at a single center for surgical evaluation of OSA from January 2017 to 2020.

Unexpected case of pneumomediastinum and subcutaneous emphysema: primary or secondary aetiology?

A 77-year-old man was admitted with a relapse of antineutrophil cytoplasmic antibody-positive vasculitis with pulmonary involvement and acute kidney injury. There was a background of pulmonary fibrosis (non-specific interstitial pneumonia type pattern) and superadded pulmonary haemorrhage, acute pulmonary oedema and sepsis. The patient was intubated for 4 days and remained dependent on high flow oxygen and continuous positive airway pressure after extubation.

Developmental and reproductive effects of chemicals associated with unconventional oil and natural gas operations.

Unconventional oil and gas (UOG) operations have the potential to increase air and water pollution in communities located near UOG operations. Every stage of UOG operation from well construction to extraction, operations, transportation, and distribution can lead to air and water contamination. Hundreds of chemicals are associated with the process of unconventional oil and natural gas production.