AIMC Topic: Child

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DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.

Computational intelligence and neuroscience
PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine lea...

A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm.

Scientific reports
Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system...

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches.

PloS one
BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using tradition...

Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

PloS one
OBJECTIVES: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network.

International journal of legal medicine
In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segme...

Annual Research Review: Translational machine learning for child and adolescent psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the li...

Segmenting pediatric optic pathway gliomas from MRI using deep learning.

Computers in biology and medicine
Optic pathway gliomas are low-grade neoplastic lesions that account for approximately 3-5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging (MRI) plays a central role in its efficient management, yet it is a challen...

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We aimed to investigate the electroclinical spectrum of GPFA along with other atypical EEG features in ...

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters.

Sensors (Basel, Switzerland)
Dental age is one of the most reliable methods for determining a patient's age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual's developme...