AI model predicts malignant breast cancer as well as humans: IBM
Ubicación actual:
> This Story

Conexión o Registro to rate this News Story
Forward Printable StoryPrint Comment




Women's Health Homepage

AI solution distinguishes complex pathologies for accurate breast cancer diagnosis Classify ductal carcinoma in situ from atypia

Dense breast laws not boosting ultrasound screening rates: study Researchers suggest risk of overdiagnosis may outweigh benefits in some cases

Three reasons growth in the mammo systems market will likely slow Insights from the market analysts at Signify Research

Radiotherapy beats anti-hormonal therapy for some breast cancer patients, says study Avoiding side effects such as hot flashes, weight gain and bone fracture

NY law requires coverage for medically necessary mammo for women under 40 More than 12,000 younger women diagnosed with breast cancer annually

Insights on implementing digital breast tomosynthesis from someone who knows As a radiologist launching her third DBT program at a breast imaging facility, Dr. Stacy Smith-Foley is uniquely poised to discuss its benefits

AI could enhance efficiency and accuracy of DBT, says study Can help reduce reading times for DBT

Study calls for greater discussion of cost in breast cancer surgery decisions Nearly one in three women consider cost when choosing breast cancer surgery procedures

The significance of the MQSA updates and ACP guidelines Setbacks and milestones for the breast imaging community

Improving care by enhancing fetal ultrasound imaging New tech is supporting better outcomes at NYU Winthrop

The algorithm is trained on EHR data
and mammogram images to predict
malignant breast cancer

AI model predicts malignant breast cancer as well as humans: IBM

por John R. Fischer , Staff Reporter
A new AI model is just as good as human radiologists at predicting the development of malignant breast cancer in patients within the course of a year.

That’s the conclusion reached by a group of researchers at IBM Research – Haifa, who are responsible for developing an algorithm they say is the first to incorporate both mammogram and comprehensive electronic health record data. They hope to make the solution a “second reader” to aid radiologists in their analyses.

Story Continues Below Advertisement


Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.

“Our algorithm takes into account many personal health factors, such as a family history of disease, breast density, and other elements that have been correlated with a higher risk of developing breast cancer,” Michal Rosen-Zvi, director of IBM Research, told HCB News. “It generates a per person risk prediction that is based on the unique combinations of many personal factors. Our hope is that this algorithm lays the groundwork for technology that could one day more accurately take into account all of these factors, and better assess a woman's risk from all of these predictive elements analyzed in conjunction with each other.”

Potential types of findings that can be found in mammograms vary in shape, size, color and texture among other factors, making assessments challenging. While a second reading by another radiologist can increase sensitivity and specificity, the lack of trained radiologists and limited time often prevent providers in many countries from including second readers in standard screening procedures.

The aim of the system is to predict biopsy malignancy and differentiate normal from abnormal screening examinations, which it can do using both bilateral craniocaudal (CC) and mediolateral oblique (MLO) views, the typical processes used by radiologists.

The team developed and trained an algorithm with 9,611 clinically-collected, de-identified mammography images from 13,234 women who underwent at least one mammogram between 2013 and 2017 and had healthy records at least one year prior to their mammograms. The images were linked to holistic clinical data and biomarkers of each patient, such as thyroid function and reproduction history. Also incorporated were detailed records of each individual’s clinical data, including cancer history, pregnancy history, status of menopause, follow-up from biopsies, cancer registry data, lab results, and codes for various other procedures and diagnoses.

Utilizing deep learning and machine learning, the model maps connections between these and other additional clinical risk features to decrease the risk for breast cancer misdiagnosis and achieve accuracy comparable to radiologists, as defined by the American benchmark for screening digital mammography.
  Pages: 1 - 2 >>

Women's Health Homepage

You Must Be Logged In To Post A Comment