Dr. Alexander Sardiña[email protected]BreastBringing humans and AI together to improve mammography outcomesCould artificial intelligence (AI) yield more accurate breast cancer screening while also decreasing the workload of radiologists? Dr. Alexander Sardiña of AI software developer Whiterabbit makes the case that combining artificial and human intelligence will deliver the best results.August 18, 2022Page 1 of 1Top StoriesRisk ManagementPodcast: Whose duty is it to ensure MRI safety?The investigation suggests there was little attention from those you might expect.Womens ImagingThe future of BI-RADS includes AIMRINew MRI scoring system predicts early liver cancer recurrenceMRIUnstable brain networks may drive multiple sclerosis fatigueWomens ImagingAI-calculated BAC on mammograms predicts cardiovascular disease in women
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
