AI Medical Compendium Journal:
Physical and engineering sciences in medicine

Showing 11 to 20 of 88 articles

Enhanced 3D dose prediction for hypofractionated SRS (gamma knife radiosurgery) in brain tumor using cascaded-deep-supervised convolutional neural network.

Physical and engineering sciences in medicine
Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant ...

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Physical and engineering sciences in medicine
Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions usi...

Robot-assisted system for non-invasive wide-range flexible eye positioning and tracking in particle radiotherapy.

Physical and engineering sciences in medicine
Particle (proton, carbon ion, or others) radiotherapy for ocular tumors is highly dependent on precise dose distribution, and any misalignment can result in severe complications. The proposed eye positioning and tracking system (EPTS) was designed to...

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

Physical and engineering sciences in medicine
The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explor...

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

Physical and engineering sciences in medicine
This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithm...

LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.

Physical and engineering sciences in medicine
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroe...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Physical and engineering sciences in medicine
Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the...

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

Physical and engineering sciences in medicine
To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT s...

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Physical and engineering sciences in medicine
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose s...

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

Physical and engineering sciences in medicine
We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146...