Allergy & Immunology

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.

INTRODUCTION: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.

Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images.

Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%-40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved ...

Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning.

Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term mon...

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).

Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leuko...

Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors.

Although Alternaria spores are well-known allergenic fungal spores, automatic bioaerosol recognition systems have not been trained to recognize these ...

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma.

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated...

Artificial intelligence for prediction of response to cancer immunotherapy.

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human inter...

The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms.

In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Cli...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening m...

Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.

Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligan...

PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.

Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventual...

Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders.

Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental...

Intraoperative angiography of the neurovascular bundle using indocyanine green and near-infrared fluorescence improves anatomical dissection during robot-assisted radical prostatectomy: initial clinical experience.

Landmark artery identification in the neurovascular bundle (NVB) is important for nerve-sparing in radical prostatectomy. We aimed to investigate intr...

Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.

BACKGROUND: An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle-Invasive Bladder Cancer (MIBC) in Bladder Can...

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome.

DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there a...

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS ...

A validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening.

BACKGROUND: Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ens...

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respir...

A guide to systems-level immunomics.

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with divers...

Individualized Diagnosis and Prescription in Traditional Medicine: Decision-Making Process Analysis and Machine Learning-Based Analysis Tool Development.

While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implic...