AIMC Topic: Female

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Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.

Radiology. Artificial intelligence
Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (...

Interpretable Artificial Intelligence for Analysing Changes in Gases in the Uterine Environment of Cows According to Physiological Structures in the Ovary.

Veterinary medicine and science
The objective of the present study was to examine the relationship between the gases in a cow's uterine environment and its ovarian physiological structures using the sunflower optimisation algorithm (SFOA) deployed in a device called Metrisör, devel...

Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

International journal of neural systems
Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD...

Artificial Intelligence Literacy Levels of Perioperative Nurses: The Case of Türkiye.

Nursing & health sciences
Artificial intelligence (AI) experience among nurses in perioperative settings is crucial for effective healthcare delivery. This study aimed to assess AI literacy levels and associated characteristics among perioperative nurses in Türkiye. This cros...

A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
In this analytical cross-sectional method comparison study, we evaluated brain MR images in 30 dogs and cats with and without using a DICOM-based deep-learning (DL) denoising algorithm developed specifically for veterinary patients. Quantitative comp...

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.

Radiology. Artificial intelligence
Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated...

Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data.

NMR in biomedicine
The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field...

A Multidimensional Regression Model for Predicting Recurrence in Chronic Low Back Pain.

European journal of pain (London, England)
BACKGROUND: Recurrence is common in chronic low back pain (CLBP). However, predicting the recurrence risk remains a challenge. The aim is to develop and validate a machine learning tool to predict the recurrence risk in patients with CLBP by using mu...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

JAMA cardiology
IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of arti...

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Radiology. Artificial intelligence
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...