AIMC Topic: Sensitivity and Specificity
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A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).
Surgical endoscopy
Jan 22, 2025
BACKGROUND: Endoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.
Results comparison of cervical cancer early detection using cerviray ® with VIA test.
BMC research notes
Jan 22, 2025
OBJECTIVES: This study investigates the performance of artificial intelligence (AI) technology, namely Cerviray AI, compared with Cerviray expert, aiming to compare its sensitivity, specificity, positive predictive value (PPV), and area under the rec...
A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.
Journal of imaging informatics in medicine
Jan 21, 2025
This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior an...
A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been deve...
Predicting postoperative adhesive small bowel obstruction in infants under 3 months with intestinal malrotation: a random forest approach.
Jornal de pediatria
Jan 21, 2025
OBJECTIVE: This study aimed to develop a predictive model using a random forest algorithm to determine the likelihood of postoperative adhesive small bowel obstruction (ASBO) in infants under 3 months with intestinal malrotation.
Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound.
The American journal of emergency medicine
Jan 20, 2025
RATIONALE: Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, curre...
Machine learning demonstrates clinical utility in distinguishing retinoblastoma from pseudo retinoblastoma with RetCam images.
Ophthalmic genetics
Jan 20, 2025
BACKGROUND: Retinoblastoma is diagnosed and treated without biopsy based solely on appearance (with the indirect ophthalmoscope and imaging). More than 20 benign ophthalmic disorders resemble retinoblastoma and errors in diagnosis continue to be made...
Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.
Gates open research
Jan 20, 2025
BACKGROUND: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single reaction. However, the manual post-run analysis of TAC data is time consuming and subject to interpretati...
AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice.
European journal of radiology
Jan 18, 2025
OBJECTIVES: In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of ...
Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.
Emergency radiology
Jan 17, 2025
PURPOSE: Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD i...
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