AIMC Topic: Radiopharmaceuticals

Clear Filters Showing 81 to 90 of 205 articles

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (rese...

PSMA-PET improves deep learning-based automated CT kidney segmentation.

Zeitschrift fur medizinische Physik
UNLABELLED: For dosimetry of radiopharmaceutical therapies, it is essential to determine the volume of relevant structures exposed to therapeutic radiation. For many radiopharmaceuticals, the kidneys represent an important organ-at-risk. To reduce th...

Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias : A Randomized Trial.

Annals of internal medicine
BACKGROUND: The role of computer-aided detection in identifying advanced colorectal neoplasia is unknown.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

Computers in biology and medicine
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range...

Process identification and discrimination in the environmental dose rate time series of a radiopharmaceutical facility using machine learning techniques.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Multi-facility nuclear sites with research reactors have several environmental area gamma monitors in a network as a part of their surveillance capability. However, the routine release of low levels of Ar gas from the reactor is prone to interfere wi...

Differentiation between normal and abnormal kidneys using Tc-DMSA SPECT with deep learning in paediatric patients.

Clinical radiology
AIM: To investigate the feasibility of using deep learning (DL) to differentiate normal from abnormal (or scarred) kidneys using technetium-99m dimercaptosuccinic acid (Tc-DMSA) single-photon-emission computed tomography (SPECT) in paediatric patient...

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival.

Contrast media & molecular imaging
Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent...

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

Physics in medicine and biology
A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for a...

Acquisition time reduction in pediatric Tc-DMSA planar imaging using deep learning.

Journal of applied clinical medical physics
PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time imag...

The emerging role of artificial intelligence and digital twins in pre-clinical molecular imaging.

Nuclear medicine and biology
INTRODUCTION: Pre-clinical molecular imaging, particularly with mice, is an essential part of drug and radiopharmaceutical development. There remain ethical challenges to reduce, refine and replace animal imaging where possible.