AIMC Topic: Child

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A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals.

Disability and rehabilitation. Assistive technology
PURPOSE: This study proposes a concept for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) based on artificial intelligence (AI) using physiological and motion signals.

Adverse Events and Morbidity in a Multidisciplinary Pediatric Robotic Surgery Program. A prospective, Observational Study.

Annals of surgery
OBJECTIVE: To report one-year morbidity of robotic-assisted laparoscopic surgery (RALS) in a dedicated, multidisciplinary, pediatric robotic surgery program. Summary Background Data. RALS in pediatric surgery is expanding, but data on morbidity in ch...

Automatic medical specialty classification based on patients' description of their symptoms.

BMC medical informatics and decision making
In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register th...

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.

Journal of pediatric surgery
PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artifici...

Detecting pediatric wrist fractures using deep-learning-based object detection.

Pediatric radiology
BACKGROUND: Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to ...

A randomized, cross-over trial comparing the effect of innovative robotic gait training and functional clinical therapy in children with cerebral palsy; a protocol to test feasibility.

Contemporary clinical trials
PURPOSE: Robotic gait training is relatively new in the world of pediatric rehabilitation. Preliminary feasibility studies and case reports include stationary robot-assisted treadmill training. Mobile robotic gait trainers hold greater promise for in...

Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW est...

Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs.

BMC oral health
BACKGROUND: It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into norm...

Application of robot-assisted endoscopic technique in the treatment of patent ductus arteriosus in 106 children.

Journal of robotic surgery
The objective is to evaluate and apply the robot-assisted endoscopic surgical technique for treatment of patent ductus arteriosus (PDA) in children. Clinical data of 106 children with PDA who underwent robot-assisted endoscopic operation were retrosp...

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging.

Tomography (Ann Arbor, Mich.)
BACKGROUND: The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to...