AIMC Topic: Humans

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Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare de...

Large Language Models with Vision on Diagnostic Radiology Board Exam Style Questions.

Academic radiology
RATIONALE AND OBJECTIVES: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of i...

Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery.

International journal of oral and maxillofacial surgery
The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of impacted mandibular third molars in panoramic radiographs and the classification of the surgical extraction difficulty level. A data...

Diagnostic scope: the AI can't see what the mind doesn't know.

Diagnosis (Berlin, Germany)
BACKGROUND: Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on ...

An adaptive variable-parameter dynamic learning network for solving constrained time-varying QP problem.

Neural networks : the official journal of the International Neural Network Society
To efficiently solve the time-varying convex quadratic programming (TVCQP) problem under equational constraint, an adaptive variable-parameter dynamic learning network (AVDLN) is proposed and analyzed. Being different from existing varying-parameter ...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understand the complexity of breast cancer.ObjectiveThe new strategy laid out in this work, called "Hybrid-Om...

Low-power and lightweight spiking transformer for EEG-based auditory attention detection.

Neural networks : the official journal of the International Neural Network Society
EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals t...

EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. ...

Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

Neural networks : the official journal of the International Neural Network Society
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...