AIMC Topic: Humans

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Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey.

Physics in medicine and biology
Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interac...

Measuring and modelling perceptions of the built environment for epidemiological research using crowd-sourcing and image-based deep learning models.

Journal of exposure science & environmental epidemiology
BACKGROUND: Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the diffi...

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

IEEE journal of biomedical and health informatics
Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is erro...

Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery.

International journal of oral and maxillofacial surgery
The purpose of this ambispective study was to investigate whether deep learning-based automatic segmentation and landmark detection, the SkullEngine, could be used for orthognathic surgical planning. Sixty-one sets of cone beam computed tomography (C...

Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.

European radiology
OBJECTIVES: To compare the image quality of three-dimensional breath-hold magnetic resonance cholangiopancreatography with deep learning-based compressed sensing reconstruction (3D DL-CS-MRCP) to those of 3D breath-hold MRCP with compressed sensing (...

Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.

European radiology
OBJECTIVES: To develop a deep learning-based harmonization framework, assessing whether it can improve performance of radiomics models given different kernels in different clinical tasks and additionally generalize to mitigate the effects of new/unob...

Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning.

Plastic and reconstructive surgery
BACKGROUND: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with ...

Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques.

Computational biology and chemistry
Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predi...

How long does image based robotic total knee arthroplasty take during the learning phase? Analysis of the key steps from the first fifty cases.

International orthopaedics
INTRODUCTION: Robotically assisted total knee arthroplasty (RA-TKA) is an emerging surgical tool. The purpose of this study was to analyze the length of time taken to perform the key steps of a RA-TKA for a surgeon and centre new to the MAKO robotic ...

What are the perceived benefits and barriers to the use of robot-assisted total knee arthroplasty? A survey of members of the European Knee Society.

International orthopaedics
PURPOSE: Members of the European Knee Society (EKS) recently took part in an online questionnaire on robot-assisted TKA. The purpose of this study was to report and analyse the questionnaire results on the demographics of users versus non-users, the ...