AIMC Topic: Deep Learning

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Non-invasive decision support for NSCLC treatment using PET/CT radiomics.

Nature communications
Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during...

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Comm...

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.

NMR in biomedicine
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T ), the spin-spin relaxation time (T ), and the spin-lattice relaxation in the rotating frame (T ), referred to as MR relaxometry in general, has demonstrated imp...

CT iterative vs deep learning reconstruction: comparison of noise and sharpness.

European radiology
OBJECTIVES: To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Magnetic resonance in medicine
PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.

Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

The Journal of dermatology
In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant archives is helpful when applied for patients with darker skin. This study compared the performance ...

OCT Signal Enhancement with Deep Learning.

Ophthalmology. Glaucoma
PURPOSE: To establish whether deep learning methods are able to improve the signal-to-noise ratio of time-domain (TD) OCT images to approach that of spectral-domain (SD) OCT images.

Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study.

Journal of hepato-biliary-pancreatic sciences
BACKGROUND/PURPOSE: The application of artificial intelligence to clinical diagnostics using deep learning has been developed in recent years. In this study, we developed an original computer-assisted diagnosis (CAD) system using deep learning analys...

Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function.

Photodiagnosis and photodynamic therapy
BACKGROUND: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function.

Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.

Medical physics
PURPOSE: Four-dimensional cone-beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due ...