AIMC Topic: Deep Learning

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Automated material flow characterization of WEEE in sorting plants using deep learning and regression models on RGB data.

Waste management (New York, N.Y.)
Waste from electrical and electronic equipment (WEEE) is a rapidly growing waste stream. Notably, electronic equipment contains valuable and critical raw materials. State of the art in WEEE recycling uses a combination of automated comminution and se...

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Molecular diversity
Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; ...

Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitud...

Accuracy and time efficiency of deep learning-based three-dimensional crown segmentation on intraoral scanning: a systematic review and meta-analysis.

Journal of dentistry
OBJECTIVES: Clinically, accurate tooth segmentation in intraoral scans is vital for clinical diagnosis and treatments. Deep learning is utilized for image segmentation in mesh segmentation such as intraoral scans lately. This review and meta-analysis...

Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Continuous, real-time monitoring of Length of Stay (LoS) for critically ill patients in Intensive Care Units (ICUs) is essential for anticipating patient needs, reduce the risk of adverse events, optimize resource allocation...

Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

Academic radiology
RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among ...

Daily proton dose re-calculation on deep-learning corrected cone-beam computed tomography scans.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Synthetic CT (sCT) generation from cone-beam CT (CBCT) must maintain stable performance and allow for accurate dose calculation across all treatment fractions to effectively support adaptive proton therapy. This study evaluate...

Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasopharyngeal carcinoma (NPC) after radiation therapy (RT) based on radiation dose distribution.

Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis.

Kidney international
BACKGROUND: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we investigated if computationally quantified tubular features can enhance prognostication and reveal sp...

Deep learning Radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients.