Diagnostic screening programs help catch cancer, abnormalities or other diseases before they reach an advanced stage, saving lives and healthcare costs. Screening programs include, lung, breast, prostate, and cervical cancer, among many others.
New findings support the routine use of deep learning-based risk assessments, as this method can decrease subjectivity, reduce unnecessary imaging and improve diagnostic accuracy.
The COlorectal Cancer detection with AI, or COCA, model is a cost-effective, scalable solution that turns routine CT scans into opportunistic exams that can be used to proactively identify CRC.
Two respected radiology organizations have issued a stark warning on the new recommendations, stating that they risk confusing patients and “may contribute to thousands of additional breast cancer deaths each year.”
Also, up to 26% of eligible women have not had a formal conversation with their physician regarding their breast cancer risks, despite current guidelines that suggest these discussions start sooner rather than later.
Social vulnerability index scores account for factors like socioeconomic status, household composition, disability, minority status, language, housing type and transportation.
Researchers found that cancer risk in premenopausal women with fatty breasts at initial imaging nearly doubled if an increase in density was observed during their second and third mammograms.
This image gallery shows examples from various breast imaging modalities, including digital breast tomosynthesis, ultrasound and breast MRI, in addition to clinical presentations of breast cancer and other pathologies.
A new artificial intelligence system can detect active tuberculosis on chest radiographs with accuracy comparable to radiologists, a recent paper in Radiology reports.
