Measuring habituation to stimuli: The Italian version of the Sensory Habituation Questionnaire.

Sensory habituation allows us to decrease responsiveness to repetitive or prolonged stimuli over time, making them easy to filter out and not interfere with ongoing activities. As such, habituation could be an important aspect to be evaluated within a sensory and cognitive assessment. The main aim of the present study was to validate an Italian version of the Sensory Habituation Questionnaire (S-Hab-Q), a self-report tool assessing how long an adult individual takes to adapt to daily sensory stimuli.

Early developmental milestone clusters of autistic children based on electronic health records.

Autistic children vary in symptoms, co-morbidities, and response to interventions. This study aimed to identify clusters of autistic children with a distinct pattern of attaining early developmental milestones (EDMs). The clustering of 5836 autistic children was based on the attainment of 43 gross motor, fine motor, language, and social developmental milestones during the first 3 years of life as recorded in baby wellness visits.

Predicting autism traits from baby wellness records: A machine learning approach.

Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life.

Incidental Apical Pleuroparenchymal Scarring on Computed Tomography: Diagnostic Yield, Progression, Morphologic Features and Clinical Significance.

Purpose: Apical pleuroparenchymal scarring (APPS) is commonly seen on chest computed tomography (CT), though the imaging and clinical features, to the best of our knowledge, have never been studied. The purpose was to understand APPS's typical morphologic appearance and associated clinical features.

Patients And Methods: A random generator selected 1000 adult patients from all 21516 chest CTs performed at urban outpatient centers from January 1, 2016 to December 31, 2016.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients.