182 research outputs found
Artificial intelligence : Training the trainer
Including artificial intelligence in haematological education is compulsory but should not be limited to students. Experienced haematologist and decision-makers in the clinical environment have at least similar needs. This is because of the tremendous potential, opportunities and benefits the timely inclusion of artificial intelligence offers in diagnosis, prediction and personalised therapy
Proceedings of the Eleventh International Meeting on Neuroacanthocytosis Syndromes
The 11th International Meeting on Neuroacanthocytosis Syndromes was held on
September 15th–17th, 2023 at the University Hospital Campus in Homburg/Saar, Germany.
The meeting followed the previous ten international symposia, the last of which was held
online due to restrictions due to COVID19, in March 2021. The setting of the meeting
encouraged interactions, exchange of ideas, and networking opportunities among the
participants from around the globe, including basic and clinical scientists, clinicians, and
especially patients, their relatives and caregivers. A total of about 20 oral communications
were presented in five scientific sessions accompanied by a keynote lecture, a “PosterBlitz” session, the “Glenn Irvine Prize” lecture and a panel discussion about “Patient
registries, international cooperation & future perspectives”.
In summary, attendees discussed recent advances and set the basis for the next steps,
action points, and future studies in close collaboration with the patient associations,
which were actively involved in the whole process
Classification of red blood cell shapes in flow using outlier tolerant machine learning
The manual evaluation, classification and counting of biological objects
demands for an enormous expenditure of time and subjective human input may be a
source of error. Investigating the shape of red blood cells (RBCs) in
microcapillary Poiseuille flow, we overcome this drawback by introducing a
convolutional neural regression network for an automatic, outlier tolerant
shape classification. From our experiments we expect two stable geometries: the
so-called `slipper' and `croissant' shapes depending on the prevailing flow
conditions and the cell-intrinsic parameters. Whereas croissants mostly occur
at low shear rates, slippers evolve at higher flow velocities. With our method,
we are able to find the transition point between both `phases' of stable shapes
which is of high interest to ensuing theoretical studies and numerical
simulations. Using statistically based thresholds, from our data, we obtain
so-called phase diagrams which are compared to manual evaluations.
Prospectively, our concept allows us to perform objective analyses of
measurements for a variety of flow conditions and to receive comparable
results. Moreover, the proposed procedure enables unbiased studies on the
influence of drugs on flow properties of single RBCs and the resulting
macroscopic change of the flow behavior of whole blood.Comment: 15 pages, published in PLoS Comput Biol, open acces
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