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Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

Journal of affective disorders
BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would enhance our understanding of how suicidal behaviour manifests in this period of rapid development. T...

A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

Journal of endourology
We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Data from pediatric patients who underwent ureteroscopy for ...

Capability of multimodal large language models to interpret pediatric radiological images.

Pediatric radiology
BACKGROUND: There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They ar...

Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.

BMC cancer
Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute ...

Being diagnosed with a rhabdomyosarcoma in the era of artificial intelligence: Whom can we trust?

Pediatric blood & cancer
In the era of big data, young patients may be overwhelmed by artificial intelligence-based tools, like chatbots. Five clinical experts were asked to evaluate the performance of the most currently used chatbots in providing information on a rare cance...

Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
OBJECTIVE: This study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors.

Automated Method for Growing Rod Length Measurement on Ultrasound Images in Children With Early Onset Scoliosis.

Ultrasound in medicine & biology
OBJECTIVE: To develop and validate machine learning algorithms to automatically extract the rod length of the magnetically controlled growing rod from ultrasound images (US) in a pilot study.

Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence.

Journal of pediatric gastroenterology and nutrition
OBJECTIVES: Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rectal biopsy, which reveals the aganglionosis and the presence of cholinergic hyperinnervation. However...

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is tim...

Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?

Skeletal radiology
OBJECTIVE: To evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults.