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A deep learning model for burn depth classification using ultrasound imaging.

Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ultrasound images. The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstruct...

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal post-operative brain scans (T1 MRI, post-Gadolinium T1 MRI, and T2-FLAIR images) of 15 patients who were treated with post-operative radiation therap...

Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System.

This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inhere...

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis.

: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evide...

Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.

Refined understanding of the association of retinal microstructure with current and future (post-treatment) function in chronic central serous chorior...

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients th...

5FU-loaded PCL/Chitosan/FeO Core-Shell Nanofibers Structure: An Approach to Multi-Mode Anticancer System.

5-Fluorouracil (5FU) and FeO nanoparticles were encapsulated in core-shell polycaprolactone (PCL)/chitosan (CS) nanofibers as a multi-mode anticancer...

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features ...

Impact of Early Exposure to Robotic Surgery Among Pre-clinical Medical Students on Career Choice and Simulation Skills.

We aimed to assess whether early exposure of medical students to robotic surgery training influences their interest in a surgical career and improves ...

Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have devel...

Predicting post-operative right ventricular failure using video-based deep learning.

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments redu...

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative...

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-de...

Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-the...

Explaining Black-Box Models for Biomedical Text Classification.

In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine...

A geometry-guided deep learning technique for CBCT reconstruction.

Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT...

Deep Learning-Guided Fiberoptic Raman Spectroscopy Enables Real-Time Diagnosis and Assessment of Nasopharyngeal Carcinoma and Post-treatment Efficacy during Endoscopy.

In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time nasopharyngeal carcinoma (NPC...

Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability.

The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, ...

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they genera...

AI-based language models powering drug discovery and development.

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artif...