AIMC Topic: Humans

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Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics
INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy.

Journal of diabetes research
This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in...

A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks.

Computational intelligence and neuroscience
In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a...

Effects of wheels and tires on high-strength lightweight wheelchair propulsion cost using a robotic wheelchair tester.

Disability and rehabilitation. Assistive technology
PURPOSE: This study was designed to investigate the effect of wheel and tire selections on the propulsion characteristics of a high-strength lightweight manual wheelchair using robotic wheelchair propulsion.

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review.

The American surgeon
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field...

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Academic radiology
RATIONALE AND OBJECTIVES: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction.

Magnetic resonance imaging
Recently, deep learning approaches with various network architectures have drawn significant attention from the magnetic resonance imaging (MRI) community because of their great potential for image reconstruction from undersampled k-space data in fas...

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

Anaesthesia, critical care & pain medicine
BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significan...

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC.