OBJECTIVE: A ceiling-mounted robotic C-arm cone beam CT (CBCT) system was developed for use with a 190° proton gantry system and a 6-degree-of-freedom robotic patient positioner. We report on the mechanical design, system accuracy, image quality, ima...
OBJECTIVES: To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT.
OBJECTIVES: To develop a deep learning (DL) model based on ultrasound (US) images of lymph nodes for predicting cervical lymph node metastasis (CLNM) in postoperative patients with differentiated thyroid carcinoma (DTC).
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, bio...
OBJECTIVES: The purpose of this study was to assess the severity of hemifacial spasm (HFS) through quantitative measures that associated it with neurovascular contact (NVC).
OBJECTIVES: Artificial intelligence (AI) software including Brainomix "e-CTA" which detect large vessel occlusions (LVO) have clinical potential. We hypothesized that in real world use where prevalence is low, its clinical utility may be overstated.
PURPOSE: To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.
OBJECTIVES: To audit prospectively the accuracy, time saving, and utility of a commercial artificial intelligence auto-contouring tool (AIAC). To assess the reallocation of time released by AIAC.
OBJECTIVES: To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications.
OBJECTIVES: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning.