AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Head

Showing 101 to 110 of 207 articles

Clear Filters

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

IEEE transactions on bio-medical engineering
OBJECTIVE: Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been develope...

Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans.

Neuroradiology
PURPOSE: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments ...

Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning.

Scientific reports
Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and so...

Efficacy of a deep leaning model created with the transfer learning method in detecting sialoliths of the submandibular gland on panoramic radiography.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to compare the performance of 3 deep learning models, including a model constructed with the transfer learning method, in detecting submandibular gland sialoliths on panoramic radiographs.

Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach.

IEEE journal of biomedical and health informatics
Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consumi...

[Robotics in otorhinolaryngology, head and neck surgery].

HNO
In many surgical specialities, e.g., visceral surgery or urology, the use of robotic assistance is widely regarded as standard for many interventions. By contrast, in European otorhinolaryngology, robotic-assisted surgery (RAS) is rarely conducted. T...

Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents.

Medical physics
PURPOSE: Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic...

A review of deep learning based methods for medical image multi-organ segmentation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical ap...

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

Computers in biology and medicine
The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, includin...