AIMC Topic: Deep Learning

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Ultrafast diffusion tensor imaging based on deep learning and multi-slice information sharing.

Physics in medicine and biology
. Diffusion tensor imaging (DTI) is excellent for non-invasively quantifying tissue microstructure. Theoretically DTI can be achieved with six different diffusion weighted images and one reference image, but the tensor estimation accuracy is poor in ...

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review.

Clinical implant dentistry and related research
Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article ...

Deep Learning-Based Reconstruction Improves the Image Quality of Low-Dose CT Colonography.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the image quality of low-dose CT colonography (CTC) using deep learning-based reconstruction (DLR) compared to iterative reconstruction (IR).

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

European radiology
BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (D...

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.

Ultrasonics
Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator v...

Hemodynamic factors of spontaneous vertebral artery dissecting aneurysms assessed with numerical and deep learning algorithms: Role of blood pressure and asymmetry.

Neuro-Chirurgie
BACKGROUND AND OBJECTIVES: The pathophysiology of spontaneous vertebral artery dissecting aneurysms (SVADA) is poorly understood. Our goal is to investigate the hemodynamic factors contributing to their formation using computational fluid dynamics (C...

A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies.

Computers in biology and medicine
Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance...

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

Breast cancer research : BCR
BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncerta...

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.

Biomedical engineering online
BACKGROUND: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hy...

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

Scientific reports
In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coeffi...