A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT (LDCT) scan, according to new research published at the ATS 2025 International Conference.
A novel deep-learning approach automatically finds and classifies microcalcifications found in mammography images—bringing both accuracy and consistency to breast-cancer screening.
Results of a new retrospective study demonstrate the potential of a novel, CT-based deep learning-driven tool to enhance liver cancer diagnosis, treatment planning, and response evaluation.
Researchers have developed an AI tool that creates synthetic yet medically accurate models of fibrotic heart tissue (heart scarring), aiding treatment planning for atrial fibrillation (AF) patients.
Despite previous global declines, pulmonary tuberculosis (TB) is on the rise again. A new AI-powered lung ultrasound shows promise in improving diagnostic performance of TB.
A new AI tool can extract key information from brain MRI scans of multiple sclerosis (MS) patients. This could be used to improve disease and treatment response monitoring.
New findings show how AI-assisted mammography may not only reveal breast cancer, but can also assess calcium buildup in the arteries within breast tissue—an indicator of cardiovascular health.
To reduce the radiation exposure for patients undergoing frequent CT scans for pneumonia diagnosis, deep learning-based denoising of ultra-low dose CT presents a viable alternative.
Researchers developed an advanced AI tool for automatic analysis of colorectal cancer tissue slides. The new model outperformed all predecessors in the classification of tissue microscopy samples.
Analysing long-term ECG recordings for signs of heart abnormalities such as arrhythmias is a time-consuming process. New research finds that AI is better suited for this task than humans.
Using 3D imaging and deep learning AI, researchers have developed a new way to accurately assess body fat and muscle distribution, which are crucial for understanding health risks.
A new deep learning model shows promise in detecting and segmenting lung tumors. The findings of the study could have important implications for lung cancer treatment.
Precise segmentation of anatomical structures greatly benefits cancer diagnosis. Using AI and deep learning methods, researchers are developing a high-precision 3D viewer software for medical image data.
US researchers have developed a comprehensive deep learning AI model designed to more accurately identify and classify cells in high-content tissue images.
The journey of a baby through the birth canal can be fraught with obstacles and risks. A new AI-based tool to evaluate the head position of the baby could lead to fewer childbirth complications.
Recent developments in deep learning techniques are enhancing clinical imaging quality and reducing radiation exposure for patients while also maintaining diagnostic accuracy. The latest AI (artificial intelligence) component to clinical imaging – referred to as deep learning reconstruction (DLR) – is having a particular benefit in paediatric imaging, according to Dr Samuel Brady from…
A newly-developed wearable camera system is designed to detect potential errors in medication delivery by identifying contents of vials and syringes with the help of deep-learning AI.
Early detection and management of asthma and COPD is critical. US researchers have developed a deep learning model paired with a wearable sensor patch to automatically detect wheezing sounds.
Combining risk markers, systematic evaluation of MRI images and AI, researchers aim to predict the risk of prostate cancer more accurately than before. This could save many patients from a biopsy.
