AIMC Topic: Deep Learning

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A novel clinical investigation using deep learning and human-in-the-loop approach in orbital volume measurement.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Orbital volume assessment is crucial for surgical planning. Traditional methods lack efficiency and accuracy. Recent studies explore AI-driven techniques, but research on their clinical effectiveness is limited. This study included 349 patients aged ...

Dissecting the effectiveness of deep features as metric of perceptual image quality.

Neural networks : the official journal of the International Neural Network Society
There is an open debate on the role of artificial networks to understand the visual brain. Internal representations of images in artificial networks develop human-like properties. In particular, evaluating distortions using differences between intern...

PSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.

European journal of nuclear medicine and molecular imaging
PURPOSE: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pel...

Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.

Medical physics
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...

A deep-learning system integrating electrocardiograms and laboratory indicators for diagnosing acute aortic dissection and acute myocardial infarction.

International journal of cardiology
BACKGROUND: Acute Stanford Type A aortic dissection (AAD-type A) and acute myocardial infarction (AMI) present with similar symptoms but require distinct treatments. Efficient differentiation is critical due to limited access to radiological equipmen...

Automatic segmentation and volumetric analysis of intracranial hemorrhages in brain CT images.

European journal of radiology
BACKGROUND: Intracranial hemorrhages (ICH) are life-threatening conditions that require rapid detection and precise subtype classification. Automated segmentation and volumetric analysis using deep learning can enhance clinical decision-making.

Detecting IDH and TERTp mutations in diffuse gliomas using H-MRS with attention deep-shallow networks.

Computers in biology and medicine
BACKGROUND: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep lear...

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.

Nature methods
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...

LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation.

Methods (San Diego, Calif.)
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy ...

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...