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The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment.

JASA express letters
This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4-7 years old, who participated in a letter and digit identification task and...

[Effect of an artificial intelligence-assisted recognition system on colonoscopy quality].

Zhonghua nei ke za zhi
To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. From January 2023, the data on 700 patients who underwent colonoscopy in the Digestive Endoscopy Center of the First Affil...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Journal of psychopathology and clinical science
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors...

Leveraging normative personality data and machine learning to examine the brain structure correlates of obsessive-compulsive personality disorder traits.

Journal of psychopathology and clinical science
Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-powered studies. Here, we tested whether machine learning (ML; elastic net regression, gradient boos...

Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.

Journal of psychopathology and clinical science
Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imag...

Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.

Radiologic technology
PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative re...

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an ind...

Deep Learning to Discriminate Arteritic From Nonarteritic Ischemic Optic Neuropathy on Color Images.

JAMA ophthalmology
IMPORTANCE: Prompt and accurate diagnosis of arteritic anterior ischemic optic neuropathy (AAION) from giant cell arteritis and other systemic vasculitis can contribute to preventing irreversible vision loss from these conditions. Its clinical distin...

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Radiology. Imaging cancer
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...