Medicolegal

Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.

The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are larg...

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study.

In clinical routine, wound documentation is one of the most important contributing factors to treating patients with acute or chronic wounds. The wound documentation process is currently very time-consuming, often examiner-dependent, and therefore imprecise. This study aimed to validate a software-based method for automated segmentation and measurement of wounds on photographic images using the Ma...

PHOTONAI-A Python API for rapid machine learning model development.

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allow...

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.

CONTEXT: Large multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance c...

CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning.

Ion intercalation based programmable resistors have emerged as a potential next-generation technology for analog deep-learning applications. Proton, b...

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, targ...

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach.

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the dep...

Toward autonomous design and synthesis of novel inorganic materials.

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and ...

Improvements to PTSD quality metrics with natural language processing.

RATIONALE AIMS AND OBJECTIVES: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) d...

Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?

We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to re...

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal.

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics...

Extraperitoneal Laparoscopic Versus Transperitoneal Robot-Assisted Laparoscopic Approaches for Extended Pelvic Lymph Node Dissection During Radical Prostatectomy.

We aim to directly compare the feasibility and safety of extended pelvic lymph node dissection (PLND) during transperitoneal robotic-assisted radical...

Increasing the confidence of F-Florbetaben PET interpretations: Machine learning quantitative approximation.

AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of F-Florbetaben brain deposition.

Clinical applications of AI in MSK imaging: a liability perspective.

Artificial intelligence (AI) applications have been gaining traction across the radiology space, promising to redefine its workflow and delivery. Howe...

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy co...

Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry.

The model of artificial intelligence DENTOMO, which allows automated deciphering the CT in maxillofacial area, was developed and implemented in practi...

Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio.

The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diaze...

Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.

Information extraction (IE), the distillation of specific information from unstructured data, is a core task in natural language processing. For rare ...

Timely and Efficient AI Insights on EHR: System Design.

A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review...

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learnin...