AIMC Topic: Sensitivity and Specificity
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Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.
European journal of radiology
Aug 1, 2025
OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.
Artificial Intelligence-Assisted Hippocampal Segmentation and Its Diagnostic Value for Alzheimer's Disease: A Meta-analysis.
Academic radiology
Aug 1, 2025
BACKGROUND: Hippocampal atrophy is a key marker of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Diverse artificial intelligence (AI) architectures for automated hippocampal segmentation have been increasingly reported in neuroimaging...
Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
Aug 1, 2025
BACKGROUND: Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented.
The detection of apical radiolucencies in periapical radiographs: A comparison between an artificial intelligence platform and expert endodontists with CBCT serving as the diagnostic benchmark.
International endodontic journal
Aug 1, 2025
AIM: Accurate detection of periapical radiolucent lesions (PARLs) is crucial for endodontic diagnosis. While cone beam computed tomography (CBCT) is considered the radiographic gold standard for detecting PARLs in non-root filled teeth, its use is of...
Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.
Acta obstetricia et gynecologica Scandinavica
Aug 1, 2025
INTRODUCTION: We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze their accuracy in differentiating between benign and malignant adnexal masses.
Automated detection of metastatic lymph nodes in head and neck malignant tumors on high - resolution MRI images using an improved convolutional neural network.
International journal of medical informatics
Aug 1, 2025
PURPOSE: To develop an AI-based diagnostic model for assessing cervical lymph nodes in head and neck malignant tumors using MRI, enabling non-invasive pre-surgical metastasis diagnosis.
Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.
Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
Jul 1, 2025
BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more...
Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System.
Cancer cytopathology
Jul 1, 2025
BACKGROUND: Medical technologies powered by artificial intelligence are quickly transforming into practical solutions by rapidly leveraging massive amounts of data processed via deep learning algorithms. There is a necessity to validate these innovat...
Real-time FT-IR typing of Klebsiella pneumoniae: a flexible and rapid approach for outbreak detection and infection control.
The Journal of antimicrobial chemotherapy
Jul 1, 2025
BACKGROUND: Expansion of carbapenemase-producing Klebsiella pneumoniae (CP-Kp) is driven by within-hospital transmission, requiring timely typing data for effective infection control.
Meta-analysis of AI-based pulmonary embolism detection: How reliable are deep learning models?
Computers in biology and medicine
Jul 1, 2025
RATIONALE AND OBJECTIVES: Deep learning (DL)-based methods show promise in detecting pulmonary embolism (PE) on CT pulmonary angiography (CTPA), potentially improving diagnostic accuracy and workflow efficiency. This meta-analysis aimed to (1) determ...
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