AIMC Topic: Sensitivity and Specificity
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Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis.
Clinical imaging
Jan 26, 2024
PURPOSE: Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia on CT of the chest, these results have not been collectively evaluated. We performed a meta-analysis...
Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?
Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie
Jan 22, 2024
PURPOSE: The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers.
An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Jan 20, 2024
BACKGROUND AND AIMS: The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endosco...
Application of artificial intelligence in gastrointestinal endoscopy.
Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
Jan 15, 2024
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stom...
Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.
European radiology
Jan 11, 2024
OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in t...
Diagnostic accuracy of an artificial intelligence algorithm versus radiologists for fracture detection on cervical spine CT.
European radiology
Jan 11, 2024
OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising ther...
Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jan 8, 2024
BACKGROUND: Pneumothorax is a common acute presentation in healthcare settings. A chest radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time between presentation and diagnosis is critical to deliver optimal treatment. De...
Novel B-DNA dermatophyte assay for demonstration of canonical DNA in dermatophytes: Histopathologic characterization by artificial intelligence.
Clinics in dermatology
Jan 6, 2024
We describe a novel assay and artificial intelligence-driven histopathologic approach identifying dermatophytes in human skin tissue sections (ie, B-DNA dermatophyte assay) and demonstrate, for the first time, the presence of dermatophytes in tissue ...
A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists.
Academic radiology
Jan 5, 2024
RATIONALE AND OBJECTIVES: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) ima...
CT-Based Super-Resolution Deep Learning Models with Attention Mechanisms for Predicting Spread Through Air Spaces of Solid or Part-Solid Lung Adenocarcinoma.
Academic radiology
Jan 5, 2024
RATIONALE AND OBJECTIVES: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD), and preoperative knowledge of STAS status is helpful in choosing an appropriate surgical approach.
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