AIMC Topic: Humans

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Robustness of Deep Learning models in electrocardiogram noise detection and classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of...

Predicting kidney allograft survival with explainable machine learning.

Transplant immunology
INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning meth...

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies.

Current opinion in structural biology
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the succe...

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

Journal of neuro-oncology
PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this...

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

HGG advances
Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although seve...

Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety.

Nurse education in practice
BACKGROUND: Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of he...

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Nature medicine
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopa...

Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.

Surgical endoscopy
BACKGROUND: Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal ti...

Regulatory barriers to US-China collaboration for generative AI development in genomic research.

Cell genomics
Here, we examine the challenges posed by laws in the United States and China for generative-AI-assisted genomic research collaboration. We recommend renewing the Agreement on Cooperation in Science and Technology to promote responsible principles for...