AIMC Topic: Child

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Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

NeuroImage. Clinical
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of ...

Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment...

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the dat...

Regional Variations in Documentation of Sexual Trauma Concepts in Electronic Medical Records in the United States Veterans Health Administration.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Experiences of sexual trauma are associated with adverse patient and health system outcomes, but are not systematically documented in electronic health records (EHR). To describe variations in how sexual trauma is documented in the Veterans Health ...

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE).

A Novel Surgical Technique for Obstructed Megaureter: Robot-Assisted Laparoscopic Dismembered Extravesical Cross-Trigonal Ureteral Reimplantation-Short-Term Assessment.

Journal of endourology
To describe our experience with robot-assisted laparoscopic dismembered extravesical cross-trigonal ureteral reimplantation (RADECUR)-a novel minimally invasive surgical technique for treatment of obstructed megaureter. A retrospective review of ou...

Using machine learning analyses to explore relations between eyewitness lineup looking behaviors and suspect guilt.

Law and human behavior
OBJECTIVE: We conducted 2 experiments using machine learning to better understand which lineup looking behaviors postdict suspect guilt., Hypotheses: We hypothesized that (a) lineups with guilty suspects would be subject to shorter viewing duration o...

Label-Free Leukemia Monitoring by Computer Vision.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemo...

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city.

Environmental pollution (Barking, Essex : 1987)
Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be under...

Invited Review: DNA methylation-based classification of paediatric brain tumours.

Neuropathology and applied neurobiology
DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this...