AIMC Topic: Deep Learning

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Graphical user interface-based convolutional neural network models for detecting nasopalatine duct cysts using panoramic radiography.

Scientific reports
Nasopalatine duct cysts are difficult to detect on panoramic radiographs due to obstructive shadows and are often overlooked. Therefore, sensitive detection using panoramic radiography is clinically important. This study aimed to create a trained mod...

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images.

Frontiers in endocrinology
BACKGROUND: Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can ...

Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.

PloS one
Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat inf...

Structural deep multi-view clustering with integrated abstraction and detail.

Neural networks : the official journal of the International Neural Network Society
Deep multi-view clustering, which can obtain complementary information from different views, has received considerable attention in recent years. Although some efforts have been made and achieve decent performances, most of them overlook the structur...

A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature represe...

Research on precise phenotype identification and growth prediction of lettuce based on deep learning.

Environmental research
In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of pheno...

Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy.

Prenatal diagnosis
OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmen...

3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

International journal of surgery (London, England)
BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP).

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Journal of environmental management
Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites...

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds.

Techniques in coloproctology
BACKGROUND: Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewe...