Alternative Medicine

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology.

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new w...

Application of machine learning and deep learning methods for hydrated electron rate constant prediction.

Accurately determining the second-order rate constant with e (k) for organic compounds (OCs) is crucial in the e induced advanced reduction processes (ARPs). In this study, we collected 867 k values at different pHs from peer-reviewed publications and applied machine learning (ML) algorithm-XGBoost and deep learning (DL) algorithm-convolutional neural network (CNN) to predict k. Our results demons...

Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.

The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tai...

Bayesian inference in ring attractor networks.

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each...

Recurrent networks endowed with structural priors explain suboptimal animal behavior.

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statis...

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study.

BACKGROUND: Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized wit...

Robotics and cybersurgery in ophthalmology: a current perspective.

Ophthalmology is one of the most enriched fields, allowing the domain of artificial intelligence to be part of its point of interest in scientific res...

Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study.

STATEMENT OF PROBLEM: Computer-aided design and computer-aided manufacturing (CAD-CAM) technology has greatly improved the efficiency of the fabricati...

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic lev...

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in random...

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-...

Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutio...

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-Ray Images.

Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakl...

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Tuberculosis (TB) is an airborne disease caused by . It is imperative to detect cases of TB as early as possible because if left untreated, there is a...

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of .

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique c...

Spherical DNNs and Their Applications in 360 Images and Videos.

Spherical images or videos, as typical non-euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projecti...

Comparison of Eye and Face Features on Drowsiness Analysis.

Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep ...

Gating Attractor Dynamics of Frontal Cortex Under Acupuncture via Recurrent Neural Network.

Acupuncture can regulate the functions of human body and improve the cognition of brain. However, the mechanism of acupuncture manipulations remains u...

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lun...

A multitask deep learning approach for pulmonary embolism detection and identification.

Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses ...