AIMC Topic: Deep Learning

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Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.

Deep implicit optimization enables robust learnable features for deformable image registration.

Medical image analysis
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, existing DLIR methods forego many of the benefi...

Artificial intelligence applications for the diagnosis of pulmonary nodules.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: This review evaluates the role of artificial intelligence (AI) in diagnosing solitary pulmonary nodules (SPNs), focusing on clinical applications and limitations in pulmonary medicine. It explores AI's utility in imaging and blood/...

Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US.

Radiology. Artificial intelligence
Purpose To develop and evaluate an artificial intelligence-based model for real-time nuchal translucency (NT) plane identification and measurement in prenatal US assessments. Materials and Methods In this retrospective multicenter study conducted fro...

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

Artificial intelligence in medicine
The prediction of Intensive Care Unit (ICU) readmission has become a crucial area of research due to the increasing demand for ICU resources and the need to provide timely interventions to critically ill patients. In recent years, several studies hav...

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans.

Artificial intelligence in medicine
Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumer...

Deep generative models for physiological signals: A systematic literature review.

Artificial intelligence in medicine
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG) and electromyogram (EMG). Compared to the existin...

Multitask Deep Learning for Automated Detection of Endoleak at Digital Subtraction Angiography during Endovascular Aneurysm Repair.

Radiology. Artificial intelligence
Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subtraction angiography (DSA) performed during real-world endovascular aneurysm repair (EVAR) procedures...

Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet).

Accident; analysis and prevention
Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with fast...

A lightweight spatial and spectral CNN model for classifying floating marine plastic debris using hyperspectral images.

Marine pollution bulletin
Marine plastic debris poses a significant environmental threat. In order to study and combat this pollution, efficient and automated detection methods are essential. Hyperspectral imaging and deep learning provide a robust framework for classifying f...