AIMC Topic: Sensitivity and Specificity
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Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images.
Neuroradiology
Sep 4, 2024
PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduce...
The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Sep 4, 2024
PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.
Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.
AJR. American journal of roentgenology
Sep 4, 2024
Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. The purpose of this article was to evaluate ...
Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.
Scientific reports
Sep 4, 2024
The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. T...
Artificial intelligence in mammography: a systematic review of the external validation.
Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia
Sep 4, 2024
OBJECTIVE: To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography.
Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.
Biomedical physics & engineering express
Sep 3, 2024
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices forma...
A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 2, 2024
PURPOSE: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Genera...
Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma.
Journal of pharmaceutical and biomedical analysis
Sep 2, 2024
Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5-10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific an...
High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.
Cancer science
Sep 2, 2024
Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high-performance artificial intelligence (AI) model to ...
Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis.
European journal of radiology
Sep 1, 2024
PURPOSE: To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches.
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