Mitchell Goldburgh[email protected]AIAvoid AI silos with a vendor-neutral analytics platformThe advent of advanced analytics and artificial intelligence (AI) is threatening to bring back data silos that were common in radiology before the adoption of vendor-neutral archives. But there is a solution thanks to vendor-neutral analytics platforms, according to Mitch Goldburgh of NTT Data Services.April 19, 2018PACS/VNAHow to make VNA lemonade from stage 2 MU's lemonsFor many healthcare organizations, the stage 2 meaningful use (MU) criteria will mean some heavy lifting for the folks tasked with managing diagnostic images and integrating them with electronic health records. But there is lemonade to be made from what feels like lemons to many chief information officers through the creation of a community-based image archive.May 27, 2015Page 1 of 1Top StoriesWomens ImagingAI-calculated BAC on mammograms predicts cardiovascular disease in womenAI-calculated breast arterial calcification (BAC) on mammograms could help predict cardiovascular disease risk in women.Cardiac ImagingECR: Cardiac imaging is at the 'heart' of precision medicineUltrasoundUltrasound MinnieCast, Episode 3: Focused ultrasound brings hope to patientsWomens ImagingECR: LLMs help patients understand breast imaging reportsCTDL segmentation of chest muscle volume shows promise for COPD patients
![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&fit=crop&h=167&q=70&w=250)
![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)
