OBJECTIVE: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality.
BACKGROUND: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We aimed to develop and test artificial intelligence (AI) models to screen f...
Clinical implant dentistry and related research
Feb 1, 2025
OBJECTIVES: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number imp...
Liver international : official journal of the International Association for the Study of the Liver
Feb 1, 2025
BACKGROUND AND AIM: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.
Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and Methods This retrospective review included 21 108 female patients (mean age ± SD, 58.1 years ± 11.5)...
Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This mult...
Journal of neuropathology and experimental neurology
Feb 1, 2025
Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive patholo...
OBJECTIVES: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.
OBJECTIVES: To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications.
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