Clinical News: Page 393

Stricter Lung-RADS protocol helps reclassify nodules on follow-up CT

By Kate Madden Yee

Stricter Lung-RADS categories in terms of nodule size help clinicians to categorize those found on follow-up lung cancer screening CT exams, according to a study by researchers from Harvard University. Their results were published November 10 in the American Journal of Roentgenology.

November 14, 2021

AI model shows promise in detecting lesions from DBT data

By Amerigo Allegretto

Wednesday, December 1 | 3:00 p.m.-4:00 p.m. | SSBR09-3 | Room TBAIn this talk, researchers will discuss an artificial intelligence (AI) algorithm that detects lesions using datasets from digital breast tomosynthesis (DBT) exams.

November 14, 2021

Deep-learning tool triages women with decreased breast density

By Amerigo Allegretto

Wednesday, December 1 | 3:00 p.m.-4:00 p.m. | SSBR09-4 | Room TBAIn this Wednesday talk, researchers will present findings from their study of nearly 2,700 women that used a convolutional neural network in predicting mammographic density percentage from MRI scans.

November 14, 2021

'Plug-and-play' AI method helps with microcalcifications on DBT

By Amerigo Allegretto

Tuesday, November 30 | 9:30 a.m.-10:30 a.m. | SSPH09-6 | Room TBAResearchers will present results from a "plug-and-play" artificial intelligence (AI) algorithm that they say has the potential to improve detection of microcalcifications on digital breast tomosynthesis (DBT) images.

November 14, 2021

AI reduces false positives on breast ultrasound

By Amerigo Allegretto

Monday, November 29 | 3:00 p.m.-4:00 p.m. | SSBR04-6 | Room TBAResearchers will present their findings from a clinical trial that used artificial intelligence (AI) in an effort to reduce false positives on breast ultrasound.

November 14, 2021

Combining radiomics, AI with PET/MRI helps assess nodal status

By Amerigo Allegretto

Sunday, November 28 | 10:30 a.m.-11:30 a.m. | SSNMMI01‐3 | Room TBAHere, researchers will talk about how applying radiomics and machine learning to FDG-PET/MRI can noninvasively assess nodal status and treatment planning for breast cancer patients.

November 14, 2021

AI-based measurements increase utility of cardiac MRI

By Erik L. Ridley

Thursday, December 2 | 9:30 a.m.-10:30 a.m. | SSCA10-1 | Room TBAAutomated cardiac MRI measurements correlate better with invasive pulmonary hemodynamics than manual measurements and could also help in predicting mortality, according to this study.

November 14, 2021

Deep learning meets need for speed in spine MRI

By Erik L. Ridley

Thursday, December 2 | 9:30 a.m.-10:30 a.m. | SSNR14-5 | Room TBAIn this session, researchers will show how a deep learning-based image reconstruction method can deliver up to 72% faster spine MRI scan times along with perceived improvements in image quality.

November 14, 2021

Can AI enable 90% lower gadolinium dose in brain MRI?

By Erik L. Ridley

Thursday, December 2 | 9:30 a.m.-10:30 a.m. | SSNR14-2 | Room TBAAn artificial intelligence (AI) algorithm can enable high-quality contrast-enhanced brain MRI exams to be acquired using only 10% of the normal dose of a gadolinium-based contrast agent, according to this multicenter clinical study.

November 14, 2021

Deep-learning algorithm triages emergency head CTs

By Erik L. Ridley

Wednesday, December 1 | 1:30 p.m.-2:30 p.m. | SSMS05-3 | Room TBAA deep-learning algorithm trained on only normal head CT exams can triage emergency cases for priority review by radiologists, according to this presentation.

November 14, 2021

Deep learning identifies CT biomarkers that help diagnose type 2 diabetes

By Kate Madden Yee

Wednesday, December 1 | 9:30 a.m.-10:30 a.m. | SSGI11-3 | Room TBADeep learning can identify CT biomarkers that help detect and predict type 2 diabetes in patients undergoing CT for other indications, according to this presentation.

November 14, 2021

Explainable artificial intelligence elevates prostate MRI reads

By Erik L. Ridley

Wednesday, December 1 | 8:00 a.m.-8:30 a.m. | SSGU05-4 | Room TBAAn explainable deep-learning software application can be highly accurate for characterizing prostate lesions on biparametric MRI exams, according to this scientific presentation.

November 14, 2021

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