More than 6.1 million children were diagnosed with ADHD in 2016. Despite these numbers, there is no single test or imaging exam that can confidently diagnose a patient.
The tool, detailed in an EBioMedicine study published last month, can sift through the multitudes of cells in a tissue sample and identify tumors’ growth patterns, along with other highly useful information for predicting health outcomes.
The public workshop will take place this upcoming February and will discuss computer-aided detection and diagnosis software, computer-aided triage systems and image quality enhancement algorithms, among other topics.
Massachusetts Institute of Technology researchers harnessed machine learning to create conditional atlases that can help clinicians diagnose a wider subset of patients.
The models encompass a wide variety of diagnostic tasks, including pneumothorax detection on chest x-rays and highlighting brain segments on MRI scans.
Experts have long talked about an ideal future in which radiologists work alongside AI. A new platform may have the answer, combining the intelligence of man and machine to better diagnose pneumonia.
In an exclusive conversation with HealthImaging, John D. Banja, a professor of medical ethics at Emory University, discusses plans to launch a series of audio chats with radiologists, exploring one of the profession's stickiest issues.
The novel method uses a deep neural network to improve fluorescence lifetime imaging, which allowed scientists at Rensselaer Polytechnic Institute to view molecular-level interactions within cells.
Coronary artery calcium scoring has proven to be more predictive of cardiovascular risk than any other biomarker, but quantifying scores via imaging remains a time-consuming and labor-intensive task.
