AIMC Topic: Reproducibility of Results

Clear Filters Showing 5211 to 5220 of 5908 articles

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

Milestones in CT: Past, Present, and Future.

Radiology
In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation,...

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Briefings in bioinformatics
Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain...

Radiologist's Guide to Evaluating Publications of Clinical Research on AI: How We Do It.

Radiology
Literacy in research studies of artificial intelligence (AI) has become an important skill for radiologists. It is required to make a proper assessment of the validity, reproducibility, and clinical applicability of AI studies. However, AI studies ar...

Using a New Deep Learning Method for 3D Cephalometry in Patients With Hemifacial Microsomia.

Annals of plastic surgery
Deep learning algorithms based on automatic 3D cephalometric marking points about people without craniomaxillofacial deformities have achieved good results. However, there has been no previous report about hemifacial microsomia (HFM). The purpose of ...

Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning.

Inflammatory bowel diseases
BACKGROUND: Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to...

Validation of an artificial intelligence-based method to automate Cobb angle measurement on spinal radiographs of children with adolescent idiopathic scoliosis.

European journal of physical and rehabilitation medicine
BACKGROUND: Accurately measuring the Cobb angle on radiographs is crucial for diagnosis and treatment decisions for adolescent idiopathic scoliosis (AIS). However, manual Cobb angle measurement is time-consuming and subject to measurement variation, ...

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.

BMJ open ophthalmology
BACKGROUND: Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and va...

XGDAG: explainable gene-disease associations via graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover u...