Identification of Sulcal Hyperintense Vessel (Vessel Wall MR Ivy Sign) in Adult Moyamoya Disease with High-resolution Vessel Wall Imaging : A Pilot Study.

Objective: The leptomeningeal ivy sign is a distinctive finding of moyamoya disease (MMD), characterized by a linear high signal intensity along the cortical sulci on contrast-enhanced T1 magnetic resonance imaging (MRI) and fluid-attenuated inversion-recovery MRI. We recently identified a similar linear enhancement along the cortical sulci using gadolinium-enhanced vessel wall MRI (VWMR) in patients with MMD. The aim of this study was to introduce the concept of the "VWMR ivy sign (VIS)".

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks-Based Deep Learning: Development and Validation Study.

Background: Accurate and timely assessment of children's developmental status is crucial for early diagnosis and intervention. More accurate and automated developmental assessments are essential due to the lack of trained health care providers and imprecise parental reporting. In various areas of development, gross motor development in toddlers is known to be predictive of subsequent childhood developments.

Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms.

Background: Moyamoya disease (MMD) is a rare and complex pathological condition characterized by an abnormal collateral circulation network in the basal brain. The diagnosis of MMD and its progression is unpredictable and influenced by many factors. MMD can affect the blood vessels supplying the eyes, resulting in a range of ocular symptoms.

Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs.

Importance: Screening for autism spectrum disorder (ASD) is constrained by limited resources, particularly trained professionals to conduct evaluations. Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections. Whether deep learning algorithms can aid in objective screening for ASD and symptom severity using retinal photographs is unknown.

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker.

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis.

Development and Validation of a Joint Attention-Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder.

Importance: Joint attention, composed of complex behaviors, is an early-emerging social function that is deficient in children with autism spectrum disorder (ASD). Currently, no methods are available for objectively quantifying joint attention.

Objective: To train deep learning (DL) models to distinguish ASD from typical development (TD) and to differentiate ASD symptom severities using video data of joint attention behaviors.