AIMC Topic: Deep Learning

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RetOCTNet: Deep Learning-Based Segmentation of OCT Images Following Retinal Ganglion Cell Injury.

Translational vision science & technology
PURPOSE: We present RetOCTNet, a deep learning tool to segment the retinal nerve fiber layer (RNFL) and total retinal thickness automatically from optical coherence tomography (OCT) scans in rats following retinal ganglion cell (RGC) injury.

Arthroscopy-validated Diagnostic Performance of 7-Minute Five-Sequence Deep Learning Super-Resolution 3-T Shoulder MRI.

Radiology
Background Deep learning (DL) methods enable faster shoulder MRI than conventional methods, but arthroscopy-validated evidence of good diagnostic performance is scarce. Purpose To validate the clinical efficacy of 7-minute threefold parallel imaging ...

A Neural Network Approach to Identify Left-Right Orientation of Anatomical Brain MRI.

Brain and behavior
PURPOSE: This study presents a novel application of deep learning to enhance the accuracy of left-right orientation identification in anatomical brain MRI scans. Left-right orientation misidentification in brain MRIs presents significant challenges d...

An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction ...

Foundation Models in Radiology: What, How, Why, and Why Not.

Radiology
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are train...

Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.

Clinical implant dentistry and related research
OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipelin...

Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction.

Korean journal of radiology
OBJECTIVE: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality.

Assessment of deep learning technique for fully automated mandibular segmentation.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.

Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.

Clinical implant dentistry and related research
OBJECTIVES: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number imp...

AggNet: Advancing protein aggregation analysis through deep learning and protein language model.

Protein science : a publication of the Protein Society
Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, dr...