AIMC Topic: Humans

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Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design.

PET clinics
Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medic...

Anatomy and Physiology of Artificial Intelligence in PET Imaging.

PET clinics
Artificial intelligence (AI) has seen an explosion in interest within nuclear medicine. This interest is driven by the rapid progress and eye-catching achievements of machine learning algorithms. The growing foothold of AI in molecular imaging is exp...

Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.

PET clinics
Recent developments in artificial intelligence (AI) technology have enabled new developments that can improve attenuation and scatter correction in PET and single-photon emission computed tomography (SPECT). These technologies will enable the use of ...

HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.

Journal of bioinformatics and computational biology
Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides...

Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience.

European journal of radiology
PURPOSE: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in pr...

REUR: A unified deep framework for signet ring cell detection in low-resolution pathological images.

Computers in biology and medicine
Detecting signet ring cells (SRCs) in pathological images is essential for carcinoma diagnosis. However, it is time consuming for pathologists to detect SRCs manually from pathological images, and the accuracy of detecting them is also relatively low...

Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning.

Computers in biology and medicine
When doctors use contrast-enhanced computed tomography (CECT) images to predict the metastasis of axillary lymph nodes (ALN) for breast cancer patients, the prediction performance could be degraded by subjective factors such as experience, psychologi...

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors.

Accident; analysis and prevention
Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to g...

[Digital transformation in urology-opportunity, risk or necessity?].

Der Urologe. Ausg. A
Ultimately, new (digital) techniques and artificial intelligence (AI) applications are changing the working environment in urology. This can be an opportunity for further development, but also a change which is not desired. Adjustments to work proces...