Fujifilm to launch new mammo workstation
Fujifilm Medical Systems USA plans to unveil its new Aspire Bellus II mammography...Read more on AuntMinnie.comRelated Reading: Fujifilm reveals branding for AI project at HIMSS Fujifilm to highlight enterprise imaging, AI at HIMSS Fuji wins 9 mammography contracts Fuji unveils AI initiative at RSNA 2017 Fuji to bring new portable DR system to RSNA 2017
Publication date: Available online 15 October 2018Source: Journal of the American College of RadiologyAuthor(s): Adarsh GhoshAbstractObjectivesWith much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist’s opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist’s opinion (in the form of BI-RADS classification) in additio...
CONCLUSION: The screening detection rate in age-eligible breast cancer patients was lower than published population-wide screening rates. Geographic mapping of the diagnostic interval and DAU use reveals regional variations in cancer diagnostic care that need to be addressed. PMID: 30303656 [PubMed - in process]
iCADâs digital breast tomosynthesis cancer detection software could make a big splash at the upcoming Radiological Society of North America meeting if recent study results are any indication. The solution will also be displayed at The European Society of Breast Imaging annual meeting, occurring this weekend. The Nashua, NH-based company said its software, which incorporates artificial intelligence (AI) and machine learning, had exceptional study results that demonstrated significant advantages for digital breast tomosynthesis. In the study, 24 radiologists reviewed about 260 3D exams. Of that number, 65 w...
The main purpose of this study was to retropectively compare the clinical and pathologic characteristics of ductal carcinoma in situ (DCIS) detected on mammography and ultrasound (US) in asymptomatic patients. From February 2014 to September 2016, 236 asymptomatic patients with primary pure DCIS and dense breasts were included. The patients were classified into two groups. The mammography group (n = 165) included patients with DCIS detected on mammography, and the US group (n = 71) included patients with DCIS detected on US only.
(MedPage Today) -- News, features, and commentary about cancer-related issues
An artificial intelligence (AI) algorithm reduced the number of unnecessary...Read more on AuntMinnie.comRelated Reading: NYMIIS: Next-gen PACS will include 'best-of-breed' AI New opportunities emerge for AI in medical imaging Radiologist factors influence mammography recall rates Group uses AI to assess mammo interpretation bias How should AI be used in breast ultrasound?
Researchers from Taiwan have created 3D-printed models of rib structures based...Read more on AuntMinnie.comRelated Reading: Virtual reality, 3D printing resolve obscure lung cancer 3D-printed device aids knee replacement surgery 3D-printed hips may improve complex fracture diagnosis 3D-printed breast phantoms help refine mammography Study: 3D-printed mandibles may reduce OR time
(American Association for Cancer Research) An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls. Clin Cancer Res; 1-8. ©2018 AACR. PMID: 30309858 [PubMed - as supplied by publisher]