AI Medical Compendium Journal:
Experimental biology and medicine (Maywood, N.J.)

Showing 11 to 20 of 27 articles

Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.

Experimental biology and medicine (Maywood, N.J.)
This study aims to construct and evaluate a deep learning model, utilizing ultrasound images, to accurately differentiate benign and malignant thyroid nodules. The objective includes visualizing the model's process for interpretability and comparing ...

Machine learning and deep learning for brain tumor MRI image segmentation.

Experimental biology and medicine (Maywood, N.J.)
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturi...

Explainable hierarchical clustering for patient subtyping and risk prediction.

Experimental biology and medicine (Maywood, N.J.)
We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely c...

Data science in drug discovery safety: Challenges and opportunities.

Experimental biology and medicine (Maywood, N.J.)
Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, ...

Review of machine learning and deep learning models for toxicity prediction.

Experimental biology and medicine (Maywood, N.J.)
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional ...

URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.

Experimental biology and medicine (Maywood, N.J.)
Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatment strategy, the severity of the disease must first be divided into referral-warranted diabetic retin...

Deep learning for artery-vein classification in optical coherence tomography angiography.

Experimental biology and medicine (Maywood, N.J.)
Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal ...

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

Experimental biology and medicine (Maywood, N.J.)
This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioe...

Weakly supervised learning and interpretability for endometrial whole slide image diagnosis.

Experimental biology and medicine (Maywood, N.J.)
Fully supervised learning for whole slide image-based diagnostic tasks in histopathology is problematic due to the requirement for costly and time-consuming manual annotation by experts. Weakly supervised learning that utilizes only slide-level label...

Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification.

Experimental biology and medicine (Maywood, N.J.)
Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires nu...