AIMC Topic: Deep Learning

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Image Quality Assessment of Deep Learning Image Reconstruction in Torso Computed Tomography Using Tube Current Modulation.

Acta medica Okayama
Novel deep learning image reconstruction (DLIR) reportedly changes the image quality characteristics based on object contrast and image noise. In clinical practice, computed tomography image noise is usually controlled by tube current modulation (TCM...

Detection of vertical root fractures by cone-beam computed tomography based on deep learning.

Dento maxillo facial radiology
OBJECTIVES: This study aims to evaluate the performance of ResNet models in the detection of and vertical root fractures (VRF) in Cone-beam Computed Tomography (CBCT) images.

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.

Translational vision science & technology
PURPOSE: (1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) c...

Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs.

Korean journal of radiology
OBJECTIVE: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the conc...

Machine learning and deep learning systems for automated measurement of "advanced" theory of mind: Reliability and validity in children and adolescents.

Psychological assessment
Understanding individual differences in theory of mind (ToM; the ability to attribute mental states to others) in middle childhood and adolescence hinges on the availability of robust and scalable measures. Open-ended response tasks yield valid indic...

Utilisation of deep learning for COVID-19 diagnosis.

Clinical radiology
The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course o...

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

Journal of medical virology
One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the ...

Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning.

Clinical chemistry
BACKGROUND: Immunofixation electrophoresis (IFE) is important for diagnosis of plasma cell disorders (PCDs). Manual analysis of IFE images is time-consuming and potentially subjective. An artificial intelligence (AI) system for automatic and accurate...

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.

Neuropathology and applied neurobiology
AIM: Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and n...