AIMC Topic: Child, Preschool
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Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.
Scientific reports
Aug 22, 2024
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, ...
Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.
Artificial intelligence in medicine
Aug 20, 2024
Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical s...
Performance of two different artificial intelligence (AI) methods for assessing carpal bone age compared to the standard Greulich and Pyle method.
La Radiologia medica
Aug 20, 2024
PURPOSE: Evaluate the agreement between bone age assessments conducted by two distinct machine learning system and standard Greulich and Pyle method.
Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas.
Scientific reports
Aug 17, 2024
The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features ...
Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis.
Medical & biological engineering & computing
Aug 16, 2024
The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients....
Predicting graft survival in paediatric kidney transplant recipients using machine learning.
Pediatric nephrology (Berlin, Germany)
Aug 16, 2024
BACKGROUND: Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine...
Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective.
Pathogens (Basel, Switzerland)
Aug 14, 2024
Leptospirosis is a zoonosis with global public health impact, particularly in poor socio-economic settings in tropical regions. Transmitted through urine-contaminated water or soil from rodents, dogs, and livestock, leptospirosis causes over a millio...
A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.
Journal of endourology
Aug 12, 2024
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 ...
Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.
BMC cancer
Aug 12, 2024
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 ...
Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models.
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
Aug 10, 2024
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.
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