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.
The interpretation of prostate MRI is notoriously difficult. Annotating AI shows promise to help improve diagnostic performance through increased cancer detection rates with fewer false positives.
Researchers have developed an AI model that increases the potential for detecting cancer through sugar analyses. The AI model is faster and better at finding abnormalities than current methods.
Adding a new dimension to pathology: Researchers explore new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions for curated prostate cancer specimens.
Researchers have developed a deep-learning model that predicts the transition from a normal cardiac rhythm to atrial fibrillation 30 minutes before onset, with an accuracy of around 80%.
Osteosarcoma is the most prevalent malignant bone tumor. Now, researchers have developed a machine-learning model to predict the density of viable tumor cells after surgery and chemotherapy treatment.
Using ultrasound imaging to detect Covid-19 infections, a new automated detection tool could help doctors in the emergency room diagnose patients quickly and accurately.
A novel AI-based, non-invasive diagnostic tool enables accurate brain tumor diagnosis, outperforming current classification methods. The tool leverages MRI information to aid clinical decision making.
Fujifilm Healthcare Europe will present its Echelon Synergy MRI system at the European Congress of Radiology 2024. The 1.5 T scanner employs AI features to enhance image quality and scanning speed.
With a combination of radiomics and deep learning, researchers aim to noninvasively determine lymph node metastasis before surgery. This could lead to more accurate diagnosis and treatment strategies.
Certain genetic features are crucial for treatment decisions for AML leukaemia. A team from Münster shows how an AI-based method can predict these features from images of bone marrow smears.
Research from Shenzhen proposes an integrated diagnosis model for automatic classification of adult-type diffuse gliomas directly from annotation-free standard whole-slide pathological images.
A new deep-learning approach to AMR testing has been shown to detect antimicrobial susceptibility within as little as 30 minutes - significantly faster than current gold-standard approaches.
