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Financial Estimation of the Uncertainty in Medicine Using Present Value of Medical Fees and a Mortality Risk Prediction Model: a Retrospective Cohort Study
This study aimed to develop a method to enable the financial estimation of each patient ’s uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level elec...
Source: Journal of Medical Systems - October 1, 2021 Category: Information Technology Source Type: research
PA-Seg: Learning From Point Annotations for 3D Medical Image Segmentation Using Contextual Regularization and Cross Knowledge Distillation
The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Cond...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research
Reducing The Need For Laboratory Animals In Basic Medical Research
What genes are responsible for the development of breast cancer? What are the brain cell mutations that lead to the onset of Alzheimer's? To find new therapies, scientists have to understand how diseases are triggered at cell level. Experiments on genetically modified mice are an indispensable part of basic medical research. Now a method has been found to help laboratories carry out their work with fewer test animals. Scientists use genetically modified laboratory mice to investigate the underlying mechanisms of diseases...
Source: Health News from Medical News Today - April 4, 2013 Category: Consumer Health News Tags: Genetics Source Type: news
Nanotube-based medical implants inserted under the skin effective for a year
Nitric oxide (NO) is one of the most important signaling molecules in living cells, carrying messages within the brain and coordinating immune system functions. In many cancerous cells, levels are perturbed, but very little is known about how NO behaves in both healthy and cancerous cells. "Nitric oxide has contradictory roles in cancer progression, and we need new tools in order to better understand it," says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT...
Source: Health News from Medical News Today - November 6, 2013 Category: Consumer Health News Tags: Medical Devices / Diagnostics Source Type: news
United Patients Group Remembers Child Medical Marijuana Pioneer Cash...
Today marks one year since Cash “Cashy” Hyde, one of the nation’s first child medical marijuana users, succumbed to terminal brain cancer. United Patients Group takes a look at how things have changed...(PRWeb November 14, 2013)Read the full story at http://www.prweb.com/releases/Cash-Hyde-Foundation/Pediatric-Cancer/prweb11333289.htm
Source: PRWeb: Medical Pharmaceuticals - November 14, 2013 Category: Pharmaceuticals Source Type: news
Interactive 3D medical data cutting using closed curve with arbitrary shape
Volume clipping is typically used in visualizing medical data, preparing surgical plans and processing images, because it conveniently clips away selected parts of volume data. Volume clipping is also a useful approach in exploring the interior details of volume data [1]. Accurate volume data clipping can meet users’ requirements to design more complicated volume model by cutting and pasting existing volume data [2]. Combined with image segmentation algorithms, clipping operation can be used as a pre-processing step to extract region of interest (ROI) in volume images, such as tumor organizations or important brain functional areas.
Source: Computerized Medical Imaging and Graphics - October 25, 2014 Category: Radiology Authors: Hai Ning, Rongqian Yang, Amin Ma, Xiaoming Wu Source Type: research
Monteris Medical raises $7m for NeuroBlate
Monteris Medical raised nearly $7 million of a hoped-for $10 million debt-and-options round for the NeuroBlate neurosurgery device it’s developing.
Plymouth, Minn.-based Monteris last month won FDA approval for an investigational device exemption trial of NeuroBlate in patients newly diagnosed with a form of brain cancer called glioblastoma multiforme. The MRI-guided device is designed to ablate, necrotize or coagulate soft tissue during neurosurgery procedures using laser thermotherapy.
A pair of unnamed investors participated in the $6.8 million round, according to a Nov. 18 regulatory filing.
Monteris has s...
Source: Mass Device - November 21, 2016 Category: Medical Equipment Authors: Brad Perriello Tags: Funding Roundup Neurological Oncology Radiosurgery Monteris Medical Source Type: news
Clinical analysis of the approximate, 3-dimensional, biological effective dose equation in multiphase treatment plans
A multiphase, approximate biological effective dose (BEDA) equation was introduced because most treatment planning systems (TPS) are incapable of calculating the true BED (BEDT). This work investigates the accuracy and precision of the multiphase BEDA relative to the BEDT in clinical cases. Ten patients with head and neck cancer and 10 patients with prostate cancer were studied using their treatment plans from Pinnacle3 9.2 (Philips Medical, Fitchburg, WI). The organs at risk (OARs) that were studied are the normal brain, left and right optic nerves, optic chiasm, spinal cord, brainstem, bladder, and rectum.
Source: Medical Dosimetry - September 1, 2017 Category: Radiology Authors: Kevin I. Kauweloa, Alonso N. Gutierrez, Angelo M. Bergamo, Sotirios Stathakis, Nikos Papanikolaou, Panayiotis Mavroidis Tags: Medical Physics Contribution: Source Type: research
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine tuning to make a CNN model a...
Source: IEE Transactions on Medical Imaging - July 1, 2018 Category: Biomedical Engineering Source Type: research
Study Points to New Method to Deliver Drugs to the Brain
Researchers at the University of Rochester Medical Center have discovered a potentially new approach to deliver therapeutics more effectively to the brain. The research could have implications for the treatment of a wide range of diseases, including Alzheimer ’s, Parkinson’s, ALS, and brain cancer.
Source: University of Rochester Medical Center Press Releases - October 19, 2018 Category: Universities & Medical Training Authors: University of Rochester Medical Center Source Type: news
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders
Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relatively small in size. In this paper, we aim to alleviate this limitation by proposing a variational generative model along with an effective data augmentation approach that utilizes the generative model to synthesize data. In our approach, the model learns the probability distribution of image data conditioned on a latent variable and the corresponding labels. The trained model ...
Source: IEE Transactions on Medical Imaging - November 29, 2019 Category: Biomedical Engineering Source Type: research
Imaging through the looking glass
In 2017, I had a brain tumour removed. It had been growing inside me for 10 years undiagnosed, which is a long time. I had lots of different medical issues and no one ever linked any of them together to really understand the root of the problem. I'd gone to doctors for different symptoms and got some resolution, but I never felt like that was the final answer. I used Dr. Google because I was determined I had cancer. I finally had to demand imaging and my doctor said, “Okay, fine, we'll just do a CT to see what it shows us,” and we found a large tumour that had fully compressed my brainstem.
Source: Journal of Medical Imaging and Radiation Sciences - August 2, 2021 Category: Radiology Authors: Sarah Hamilton Tags: Medical Radiation Sciences Narratives Source Type: research
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training
Med Image Comput Comput Assist Interv. 2022 Sep;13435:66-76. doi: 10.1007/978-3-031-16443-9_7. Epub 2022 Sep 16.ABSTRACTUnsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfactory in the presence of significant domain shifts. Nonetheless, delineating a few target samples is usually manageable and particularly worthwhile, due to the substantial performance gain....
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 13, 2023 Category: Radiology Authors: Xiaofeng Liu Fangxu Xing Nadya Shusharina Ruth Lim C-C Jay Kuo Georges El Fakhri Jonghye Woo Source Type: research
