AIMC Topic: Deep Learning

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Deep Learning-Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study.

JMIR medical informatics
BACKGROUND: In hospitals, Code Blue is an emergency that refers to a patient requiring immediate resuscitation. Over 85% of patients with cardiopulmonary arrest exhibit abnormal vital sign trends prior to the event. Continuous monitoring and accurate...

Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images.

Scientific reports
Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improvi...

Deep unrolled primal dual network for TOF-PET list-mode image reconstruction.

Physics in medicine and biology
Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of positron emission tomography (PET) reconstruction images and reducing noise. List-mode reconstruction has a significant a...

AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction.

JMIR medical informatics
This viewpoint article explores the transformative role of artificial intelligence (AI) in predicting perioperative hypoxemia through the integration of deep learning with multimodal clinical data, including lung imaging, pulmonary function tests, an...

From Consensus to Standardization: Evaluating Deep Learning for Nerve Block Segmentation in Ultrasound Imaging.

A&A practice
BACKGROUND: Deep learning can automate nerve identification by learning from expert-labeled examples to detect and highlight nerves in ultrasound images. This study aims to evaluate the performance of deep-learning models in identifying nerves for ul...

Progressive decomposition of infrared and visible image fusion network with joint transformer and Resnet.

PloS one
The objective of image fusion is to synthesize information from multiple source images into a single, high-quality composite that is information-rich, thereby enhancing both human visual interpretation and machine perception capabilities. This proces...

Deep learning model for predicting extraprostatic extension of prostate cancer based on H&E-stained biopsy digital images.

Annals of medicine
BACKGROUND: To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients.

Deep learning-based regression of food quality attributes using near-infrared spectroscopy and hyperspectral imaging: A review.

Food chemistry
Near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) are two popular non-destructive tools for food quality and safety inspection. For food quality attributes quantification, the key is to develop regression models to link the features (s...

MetaMBP: Few-Shot Multilabel Prediction of Bioactive Peptides Based on Deep Metric Meta-Learning.

Journal of chemical information and modeling
Bioactive peptides are highly specific and have low toxicity, making them a promising treatment option. There are many different types of bioactive peptides, while some types have limited samples (under 500). Methods that can handle limited types of ...

BiVAE-CPI: An Interpretable Generative Model Using a Bilateral Variational Autoencoder for Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However...