AIMC Topic: Patient Care Planning
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Artificial intelligence and radiomics in pediatric molecular imaging.
Methods (San Diego, Calif.)
Jun 13, 2020
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving fie...
Automatic mandibular canal detection using a deep convolutional neural network.
Scientific reports
Mar 31, 2020
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques fo...
Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.
International journal of medical informatics
Mar 21, 2020
BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledg...
Attitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery.
World neurosurgery
Mar 14, 2020
BACKGROUND: Artificial intelligence (AI) may favorably support surgeons but can result in concern among patients and their relatives. The aim of this study was to evaluate attitudes of patients and their relatives regarding use of AI in neurosurgery.
Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET.
Acta neurochirurgica
May 30, 2019
BACKGROUND: Growing interest exists for superolateral medial forebrain bundle (slMFB) deep brain stimulation (DBS) in psychiatric disorders. The surgical approach warrants tractographic rendition. Commercial stereotactic planning systems use determin...
Learning to Personalize from Practice: A Real World Evidence Approach of Care Plan Personalization based on Differential Patient Behavioral Responses in Care Management Records.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Recent studies documented the importance of individuality and heterogeneity in care planning. In practice, varying behavioral responses are revealed in patients' care management (CM) records. However, today's care programs are structured around popul...
Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning.
Artificial intelligence in medicine
Nov 23, 2018
The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized con...
State of the Art: Machine Learning Applications in Glioma Imaging.
AJR. American journal of roentgenology
Oct 17, 2018
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MR...
Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
European radiology
Oct 9, 2018
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...
Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge-based planning.
Journal of applied clinical medical physics
Mar 25, 2018
PURPOSE: Eclipse treatment planning system has not been able to optimize the jaw positions for Volumetric Modulated Arc Therapy (VMAT). The arbitrary and planner-dependent jaw placements define the maximum field size within which multi-leaf-collimato...
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