From the November 2021 issue of HealthCare Business News magazine
The ASPIRE Cristalle with DBT solution is designed to enhance image quality by adding additional compression and intelligent image processing. It can do this at low doses for every breast type, including implants.
In clinical studies, Transpara identified up to 35% of exams with interval cancers found on earlier mammograms and labeled up to 70% of exams as most likely normal. This reduces workload for radiologists.
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Deep learning approach almost 100% accurate in detecting signs of Alzheimer's
Lithuanian researchers announced in September they had developed a deep-learning approach that is 99% accurate in predicting the possible onset of Alzheimer’s disease.
Utilizing functional MR, the method identified and differentiated between signs of mild cognitive impairment (MCI) — the stage of expected cognitive decline of normal ageing, dementia, early MCI, and Alzheimers.
This does away with the time-consuming task of manually analyzing fMR images, which requires specific knowledge, according to the IT researchers at Kaunas University of Technology. They say that while not necessarily confirming the presence of illness, identifying MCI can help encourage patients to be seen by a medical professional and may be a sign of other related diseases, if not Alzheimer’s. The detection of the earliest stages of MCI is especially promising, they say, as often there are no clear symptoms.
Assessing 138 subjects, the deep learning model classified early MCI from Alzheimer’s cases with 99.99% accuracy; late MCI from Alzheimer’s with 99.95% accuracy; and MCI, also with 99.95% accuracy. Sensitivity and specificity were also higher than previously developed methods.
The algorithm was shared on Deep Residual Networks. Rytis Maskeliūnas, a researcher in the department of multimedia engineering at KTU, told HCB News that numerous researchers adopted and worked on its imaging analysis in various applications.
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