AIMC Topic: Sensitivity and Specificity
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Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence.
Journal of the American College of Radiology : JACR
Mar 1, 2025
OBJECTIVE: Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as t...
Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis.
Korean journal of radiology
Mar 1, 2025
OBJECTIVE: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for full-field digital mammography (FFDM) when applied to synthetic mammography (SM).
Application of artificial intelligence in the detection of Borrmann type 4 advanced gastric cancer in upper endoscopy (with video).
Cancer
Feb 15, 2025
BACKGROUND: Borrmann type-4 (B-4) advanced gastric cancer is challenging to diagnose through routine endoscopy, leading to a poor prognosis. The objective of this study was to develop an artificial intelligence (AI)-based system capable of detecting ...
Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels.
Clinical chemistry
Feb 3, 2025
BACKGROUND: Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter res...
Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.
Clinical and experimental dental research
Feb 1, 2025
OBJECTIVES: Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Conseque...
The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effec...
Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications.
The British journal of radiology
Feb 1, 2025
OBJECTIVES: To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications.
ASSESSMENT OF ARTIFICIAL INTELLIGENCE SOFTWARE FOR AUTOMATIC SCREENING OF DIABETIC RETINOPATHY BASED ON FUNDUS PHOTOGRAPHS IN MELANODERM SUBJECTS.
Retina (Philadelphia, Pa.)
Feb 1, 2025
PURPOSE: To assess the Gaiha Prio Retino +™ Artificial Intelligence (AI) software for detecting diabetic retinopathy (DR).
Use of artificial intelligence to detect dental caries on intraoral photos.
Quintessence international (Berlin, Germany : 1985)
Jan 30, 2025
Dental caries is one of the most common diseases globally. It affects children and adults living in poverty, who have the most limited access to dental care. Left unexamined and untreated in the early stages, treatments for late-stage and severe cari...
Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Translational vision science & technology
Jan 2, 2025
PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
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