AIMC Topic: Algorithms

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Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography.

Skeletal radiology
OBJECTIVE: The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

European radiology
OBJECTIVES: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans.

Actionable artificial intelligence: Overcoming barriers to adoption of prediction tools.

Surgery
Clinical prediction models based on artificial intelligence algorithms can potentially improve patient care, reduce errors, and add value to the health care system. However, their adoption is hindered by legitimate economic, practical, professional, ...

Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment.

Sensors (Basel, Switzerland)
A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic increase in the utilization of CPSs, which makes their security a challenging problem to address. Intru...

CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides.

BMC bioinformatics
BACKGROUND: Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential a...

Neurosurgical skills analysis by machine learning models: systematic review.

Neurosurgical review
Machine learning (ML) models are being actively used in modern medicine, including neurosurgery. This study aimed to summarize the current applications of ML in the analysis and assessment of neurosurgical skills. We conducted this systematic review ...

A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

International orthodontics
INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample.

Targeting operational regimes of interest in recurrent neural networks.

PLoS computational biology
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require trac...

Optimising trajectory planning for stereotactic brain tumour biopsy using artificial intelligence: a systematic review of the literature.

British journal of neurosurgery
PURPOSE: Despite advances in technology, stereotactic brain tumour biopsy remains challenging due to the risk of injury to critical structures. Indeed, choosing the correct trajectory remains essential to patient safety. Artificial intelligence can b...

Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism.

Artificial intelligence in medicine
Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable ...