Using machine learning, researchers have trained an electronic nose to detect early signs of ovarian cancer in the blood. The method could eventually be used to find many different cancers.
Clues in the CSF: Researchers have developed the first high-precision method that can theoretically diagnose common brain tumors in children and adolescents without surgery.
An AI-assisted stethoscope could help doctors identify patients with valvular heart disease who may otherwise go undiagnosed. This could reduce hospital admissions and overall healthcare costs.
Severe headache, neck pain or confusion: More than 25% of pregnant women suffering non-specific stroke symptoms sought care but were misdiagnosed, University of Pittsburgh research finds.
To better diagnose patients who appear unconscious in ICUs, and predict their recovery potential, researchers developed a tool that integrates multiple modalities for analyzing consciousness.
An international study shows that Alzheimer’s disease biomarkers can be accurately detected using simple finger-prick blood samples that can be collected at home and mailed to a laboratory.
A novel machine-learning-based solution analyses CT images and helps radiologists detect both malignant and benign lesions in the kidney more quickly and reliably.
A sample of inequality: A new study shows that AI models can infer demographic information from pathology slides, leading to bias in cancer diagnosis among different populations.
Now, a research team has developed a novel diagnostic approach that enables the rapid and simultaneous detection of both antibiotic resistance and high virulence in Klebsiella pneumoniae.
Researchers have discovered that low physical activity is associated with a higher risk of lymphedema. They have also noted that a lymph scanner objectively measures changes in the condition.
Can CT-derived fractional flow reserve (FFR-CT) be used in patients with angina to predict future major cardiovascular events? A novel AI-based approach for CCTA analysis yields promising results.
A novel AI-based method can distinguish between progressive brain tumours and radiotherapy-induced necrosis on advanced MRI. This could help clinicians more accurately identify and treat the issues.
Philips and Cortechs.ai have extended their collaboration to integrate AI-enabled quantitative neuroimaging analytics directly into MR systems. The partnership aims to provide radiologists with automated, objective measurements of brain structures and lesions to support diagnosis and monitoring of neurological conditions.
A patient reports with chest pain, but in the coronary angiogram, the main heart arteries look clear, so it cannot be angina – right? A new study reveals that this approach can easily lead to misdiagnosis.
Just like the “ABCDE” mnemonic for melanoma awareness, researchers hope their diagnostic tool will transform how doctors approach persistent rashes, reminding them to consider lymphoma sooner.
An innovative Group A Streptococcus “One-Step Antigen Extraction + Detection” Kit (immunochromatography), developed by GenSure Biotech Inc., has successfully obtained both Class B and Class C EU IVDR certifications for in vitro diagnostic medical devices.
In emergency settings, AI to analyze ECG data can improve detection of severe heart attacks, including those with unconventional symptoms, or atypical ECG patterns, and reduced false positives.
Developmental dysplasia of the hip occurs in 2-4% of infants, but screening via ultrasound is notoriously hard to master. An AI-assisted application could prevent many unnecessary hospital referrals.
Magnetic resonance imaging (MRI) holds great potential to spare men with suspected prostate cancer a painful biopsy. In our interview, Dr. Peter Seidensticker, Head of Medical Affairs Radiology at Bayer, outlines how MRI could be implemented as a first-line diagnostic.
