Pediatrics

Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, poses significant treatment challenges. Recent studies suggest that senescent cells in ACP tumors may contribute to tumor growth and invasion by releas...

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with dia...

[New Technology, Warm Care: Social Robots in Nursing Practice and Research].

Recent, rapid advancements in technology have enabled the introduction and integration of robots into everyday life. Different from the traditional im...

Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes.

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-ag...

[An overview of investments in the development of the Russian Medtech market and the prospects for the impact of digitalization in medicine on the economic performance of companies until 2030].

Today, the topic of digitalization, the introduction of innovations based on Big Data, the complexity of technologies due to the introduction of artif...

A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials.

The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near re...

An interpretable predictive deep learning platform for pediatric metabolic diseases.

OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on q...

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral p...

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.

Purpose To evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variations in image appearance. Materials and Me...

Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depen...

Role of artificial intelligence in multiple sclerosis management.

From a clinical viewpoint, there are enormous obstacles to early detection and diagnosis as well as treatment interventions for multiple sclerosis (MS...

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in nor...

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the...

Digital technology and artificial intelligence for improving congenital heart disease care: alea iacta est.

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

OBJECTIVES: The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and pro...

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review.

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious ...

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.

IMPORTANCE: Acute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple ...

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

IMPORTANCE: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have ...

[The Generative Artificial Intelligence Revolution in Nursing: A New Chapter in Enhancing Care Quality and Education].

Artificial intelligence (AI) represents a recent major breakthrough in technology development and, in recent years, generative AI has emerged as anoth...

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.

Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation...