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Benchmarking

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Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.

The Annals of thoracic surgery
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...

Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review process.

Clinical chemistry and laboratory medicine
BACKGROUND: In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notab...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...

Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.

Nutrients
Photo-based dietary assessment is becoming more feasible as artificial intelligence methods improve. However, advancement of these methods for dietary assessment in research settings has been hindered by the lack of an appropriate dataset against whi...

An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition.

Brain topography
Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid deep-learning model for detecting drowsiness to address this issue. The proposed model combines the strengths of discrete wavelet long short-term memo...

Use of deep learning to segment bolus during videofluoroscopic swallow studies.

Biomedical physics & engineering express
Anatomical segmentations generated using artificial intelligence (AI) have the potential to significantly improve video fluoroscopic swallow study (VFS) analysis. AI segments allow for various metrics to be determined without additional time constrai...

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

International journal of radiation oncology, biology, physics
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation hav...

Deep learning-based 3D brain multimodal medical image registration.

Medical & biological engineering & computing
Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this pape...

Deep Generative Models in Drug Molecule Generation.

Journal of chemical information and modeling
The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence ...

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

JAMA network open
IMPORTANCE: Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.