PURPOSE: To compare the accuracy and reliability of a natural language processing (NLP) algorithm with manual coding by radiologists, and the combination of the two methods, for the identification of patients whose computed tomography (CT) reports ra...
Since 2012, we have been developing a remote-controlled robotic system (Zerobot®) for needle insertion during computed tomography (CT)-guided interventional procedures, such as ablation, biopsy, and drainage. The system was designed via a collaborati...
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevan...
Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ ...
Clinical orthopaedics and related research
Sep 1, 2018
BACKGROUND: Pelvic resections are challenging, and reconstruction of the resected acetabulum to restore mobility and stability is even more difficult. Extracorporeal radiation therapy (ECRT or extracorporeal irradiation) of autograft bone and reimpla...
BACKGROUND: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) i...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.