AIMC Topic: Feasibility Studies
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A feasibility study on deep learning-based individualized 3D dose distribution prediction.
Medical physics
Jul 11, 2021
PURPOSE: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models...
Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.
Frontiers in public health
Jul 5, 2021
Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging t...
A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.
Clinical nutrition (Edinburgh, Scotland)
Jul 3, 2021
BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This s...
Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China.
BMC medical informatics and decision making
Jun 26, 2021
BACKGROUND: With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can...
Artificial intelligence for quality assurance in radiotherapy.
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Jun 24, 2021
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...
Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images.
International forum of allergy & rhinology
Jun 20, 2021
BACKGROUND: Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning-based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility...
Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.
BMC nephrology
Jun 19, 2021
BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible ...
Endoscopic submucosal dissection using a detachable assistant robot: a comparative in vivo feasibility study (with video).
Surgical endoscopy
Jun 18, 2021
BACKGROUND: Appropriate tissue tension and clear visibility of the dissection area using traction are essential for effective and safe endoscopic submucosal dissection (ESD). We developed a robotic assistive traction device for flexible endoscopy and...
Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI.
Magnetic resonance imaging
Jun 18, 2021
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hol...
Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.
La Radiologia medica
Jun 16, 2021
BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounte...
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