AIMC Topic: Deep Learning

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Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

Orthopaedic surgery
BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicator...

Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors.

Scandinavian journal of gastroenterology
OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this st...

Deep Learning Analysis of Surgical Video Recordings to Assess Nontechnical Skills.

JAMA network open
IMPORTANCE: Assessing nontechnical skills in operating rooms (ORs) is crucial for enhancing surgical performance and patient safety. However, automated and real-time evaluation of these skills remains challenging.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.

Sensors (Basel, Switzerland)
This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabil...

Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning.

Scientific reports
Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine,...

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging.

BMC medical informatics and decision making
BACKGROUND: Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of...

Predicting glycan structure from tandem mass spectrometry via deep learning.

Nature methods
Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throug...

Deep Learning-Enabled Quantification of Tc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with Tc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard...