AIMC Topic: Retrospective Studies

Clear Filters Showing 3711 to 3720 of 9989 articles

Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperienced operators. We aimed to develop and validate a prenatal-screening artificial intelligence system ...

Differentiating spinal pathologies by deep learning approach.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clin...

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives.

Diagnostic and interventional imaging
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubi...

Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.

Abdominal radiology (New York)
OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.

Current knowledge and availability of machine learning across the spectrum of trauma science.

Current opinion in critical care
PURPOSE OF REVIEW: Recent technological advances have accelerated the use of Machine Learning in trauma science. This review provides an overview on the available evidence for research and patient care. The review aims to familiarize clinicians with ...

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

The British journal of radiology
OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced d...

Preclinical validation of a novel deep learning-based metal artifact correction algorithm for orthopedic CT imaging.

Journal of applied clinical medical physics
PURPOSE: To validate a novel deep learning-based metal artifact correction (MAC) algorithm for CT, namely, AI-MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique.

Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.

Reproductive biomedicine online
RESEARCH QUESTION: Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?

Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department.

JAMA network open
IMPORTANCE: Multimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images.

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans.

Clinical and translational gastroenterology
INTRODUCTION: Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and computed tomo...