AIMC Topic: Retrospective Studies

Clear Filters Showing 3811 to 3820 of 9989 articles

An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential.

Scientific reports
Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In t...

Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

European radiology
OBJECTIVES: The differential between high-grade glioma (HGG) and metastasis remains challenging in common radiological practice. We compare different natural language processing (NLP)-based deep learning models to assist radiologists based on data co...

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study.

BMC medical informatics and decision making
BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images.

Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

European radiology
OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity ...

The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOOD.

Nutrients
A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct aut...

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in differ...

Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study.

Reproductive biomedicine online
RESEARCH QUESTION: What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial?

Robot-assisted radical nephrectomy using novel surgical robot platform, hinotori: Report of initial series of 13 cases.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: The aims of the present study were to describe the perioperative findings of the first series of patients undergoing robot-assisted radical nephrectomy (RARN) with a newly launched platform, the hinotori surgical robot system, and compare...

Infant death prediction using machine learning: A population-based retrospective study.

Computers in biology and medicine
BACKGROUND: Despite declines in infant death rates in recent decades in the United States, the national goal of reducing infant death has not been reached. This study aims to predict infant death using machine-learning approaches.