AIMC Topic: Sensitivity and Specificity

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Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.

BMC medical informatics and decision making
BACKGROUND: Patients with severe coronary arterystenosis may present with apparently normal electrocardiograms (ECGs), making it difficult to detect adverse health conditions during routine screenings or physical examinations. Consequently, these pat...

Effectiveness of Comprehensive Video Datasets: Toward the Development of an Artificial Intelligence Model for Ultrasonography-Based Severity Diagnosis of Carpal Tunnel Syndrome.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Advances in diagnosing carpal tunnel syndrome (CTS) using ultrasonography (US) and artificial intelligence (AI) aim to replace nerve conduction studies. However, a method for accurate severity diagnosis remains unachieved. We explored the...

Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.

European journal of pediatrics
UNLABELLED: Machine learning (ML) based on remote video has shown ideal diagnostic value in autism spectrum disorder (ASD). Here, we conducted a meta-analysis of the diagnostic value of home video-based ML in ASD. Relevant articles were systematicall...

A personalized periodontitis risk based on nonimage electronic dental records by machine learning.

Journal of dentistry
OBJECTIVE: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).

An artificial intelligence mechanism for detecting cystic lesions on CBCT images using deep learning.

Journal of stomatology, oral and maxillofacial surgery
INTRODUCTION: The present study aimed to provide and evaluate the efficiency of an artificial intelligence mechanism for detecting cystic lesions on cone beam computed tomography (CBCT) scans.

Application of Machine Learning to Osteoporosis and Osteopenia Screening Using Hand Radiographs.

The Journal of hand surgery
PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing low bone mineral density require specialized dual x-ray absorptiometry (DXA) scans. Plain hand radio...

ResNet-Transformer deep learning model-aided detection of dens evaginatus.

International journal of paediatric dentistry
BACKGROUND: Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to prese...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

Scientific reports
The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% mal...

Artificial intelligence in fracture detection on radiographs: a literature review.

Japanese journal of radiology
Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for...

Human identification via digital palatal scans: a machine learning validation pilot study.

BMC oral health
BACKGROUND: This study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set). The discrimination potential of the palatal intraoral scan-based geometric ...