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.
Machine learning and AI are playing an increasingly important role in medicine and healthcare, and not just since ChatGPT. This is especially true in data-intensive specialties such as radiology, pathology or intensive care. The quality of diagnostics and decision-making via AI, however, does not only depend on a sophisticated algorithm but – crucially – on the quality of the training data.
Researchers from Finland have developed an artificial intelligence tool for automatic colorectal cancer tissue analysis that outperforms prior methods.
A new AI method for CT brain imaging may bring the modality to the level of detail usually reserved for MRI scans. This could enhance diagnostic support for conditions such as Alzheimer's disease.
A new artificial intelligence (AI) model combines imaging information with clinical patient data to improve diagnostic performance on chest X-rays, a new study finds.
For the first time, researchers show that AI-based predictions can deliver comparable results to clinical tests on biopsies of patients with colorectal cancer (CRC).
Osaka Metropolitan University scientists have developed an advanced AI model that utilizes chest radiographs to accurately estimate a patient’s "true" age.
Researchers from the University of Chicago developed a deep-learning model to assess chest X-ray radiographs for probable Covid-19 severity.
Experts have highlighted how precision pathology using Artificial Intelligence can provide an effective alternative to molecular diagnostics. This, say a team from the Karolinska Institutet (KI) in Stockholm, Sweden, can also offer multiple advantages within a clinical setting and support risk stratification.
