AIMC Topic: Adolescent

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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 ...

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 ...

Machine-Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To develop a machine-learning model to predict clinical outcomes after medial patellofemoral ligament reconstruction (MPFLR) and identify the important predictive indicators.

Predictive roles of cognitive biases in health anxiety: A machine learning approach.

Stress and health : journal of the International Society for the Investigation of Stress
Prior work suggests that cognitive biases may contribute to health anxiety. Yet there is little research investigating how biased attention, interpretation, and memory for health threats are collectively associated with health anxiety, as well as the...

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.

Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.

Journal of imaging informatics in medicine
To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 8...

The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.

Biomedical engineering online
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Bo...

Deep learning-based automated angle measurement for flatfoot diagnosis in weight-bearing lateral radiographs.

Scientific reports
This study aimed to develop and evaluate a deep learning-based system for the automatic measurement of angles (specifically, Meary's angle and calcaneal pitch) in weight-bearing lateral radiographs of the foot for flatfoot diagnosis. We utilized 3960...