AIMC Topic: Female

Clear Filters Showing 26631 to 26640 of 29210 articles

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Radiology
Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improv...

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.

JCO clinical cancer informatics
PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans...

Facial adult female acne in China: An analysis based on artificial intelligence over one million.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: To further clarify the acne profile of Chinese adult women, we included 1,156,703 adult women. An artificial intelligence algorithm was used to analyze images taken by high-resolution mobile phones to further explore acne levels in Chines...

Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis.

Korean journal of radiology
OBJECTIVE: To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize ...

Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy.

Korean journal of radiology
OBJECTIVE: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness M...

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

JAMA cardiology
IMPORTANCE: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on re...

A novel single-port robot for total gastrectomy to treat gastric cancer: A case report (with video).

Asian journal of endoscopic surgery
Multiport robots are now widely used for total gastrectomy for gastric cancer, while there is almost a void of research on whether single-port (SP) robots can be used for total gastrectomy. Here, we report a case of a 75-year-old female patient who w...

Mammography-based deep learning model for coronary artery calcification.

European heart journal. Cardiovascular Imaging
AIMS: Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coron...

[Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV).

Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study.

Postgraduate medical journal
BACKGROUND: The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Ad...