AIMC Topic: Deep Learning

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Integrating artificial intelligence in strabismus management: current research landscape and future directions.

Experimental biology and medicine (Maywood, N.J.)
Advancements in artificial intelligence (AI) are transforming strabismus management through improved screening, diagnosis, and surgical planning. Deep learning has notably enhanced diagnostic accuracy and optimized surgical outcomes. Despite these ad...

Deep learning-based image classification of sea turtles using object detection and instance segmentation models.

PloS one
Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promisin...

Utilization of Artificial Intelligence for the automated recognition of fine arts.

PloS one
Fine art recognition, traditionally dependent on human expertise, is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and deep learning. This article introduces a novel AI-based approach for fine art recogn...

Leveraging Artificial Intelligence for Diabetic Retinopathy Screening and Management: History and Current Advances.

Seminars in ophthalmology
AIM: Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intel...

CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation.

Medical image analysis
Deep learning shows promise for medical image segmentation but suffers performance declines when applied to diverse healthcare sites due to data discrepancies among the different sites. Translating deep learning models to new clinical environments is...

Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning.

NeuroImage
Differences in iron accumulation patterns have been observed in susceptibility-weighted images across different classes of atypical parkinsonian syndromes (APS). Deep learning methods have shown great potential in automatically detecting these differ...

An object detection-based model for automated screening of stem-cells senescence during drug screening.

Neural networks : the official journal of the International Neural Network Society
Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to i...

An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography.

International journal of medical informatics
OBJECTIVE: This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic r...

Pulmonary Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.

Academic radiology
RATIONALE AND OBJECTIVES: Hyperpolarized Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of Xe signal-void to the anatomic H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a trea...

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

The American journal of cardiology
Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to monitor treatment response. Although the surgical or percutaneous reduction of the interventricular...