- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Siemens sells $16 million in PACS
The PACS division of Siemens Medical Systems in Iselin, NJ, roared through spring by landing PACS contracts in March worth a total of $16 million. The biggest purchaser is Saint Barnabas Medical Center in Livingston, NJ, which will spend more than $11
The PACS division of Siemens Medical Systems in Iselin, NJ, roared through spring by landing PACS contracts in March worth a total of $16 million. The biggest purchaser is Saint Barnabas Medical Center in Livingston, NJ, which will spend more than $11 million in a four-phase project that will convert the site to filmless operation.
In other big PACS sales, St. John's Hospital in Springfield, IL, is spending $1.6 million to expands its PACS network, while University Health System in San Antonio will be installing $1.4 million worth of PACS technology to provide digital image distribution for several modalities. Another client, University of Michigan Hospital in Ann Arbor, will spend $1 million on digital image management. Finally, Baylor Medical Center in Irving, TX, will be investing an additional $1 million to expand its filmless outpatient center at its North Campus.
In other Siemens PACS news, the company this month introduced MagicView 300, a Windows NT-based software package that can provide image distribution, teleradiology, and archiving capabilities. Siemens expects that both radiologists and clinicians will be users of the product.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
April 15th 2024Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.
MRI-Based AI Model Shows Promise in Predicting Lymph Node Metastasis with Breast Cancer
April 15th 2024For the prediction of axillary lymph node metastasis in patients with breast cancer, an MRI-based, 4D convolutional neural network model demonstrated an AUC of 87 percent and sensitivity of 89 percent, according to new research.
