AIMC Topic: Sensitivity and Specificity
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Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis.
The Journal of prosthetic dentistry
Jan 4, 2024
STATEMENT OF PROBLEM: With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising r...
An Evaluation of the Efficacy of Machine Learning in Predicting Thyrotoxicosis and Hypothyroidism: A Comparative Assessment of Biochemical Test Parameters Used in Different Health Checkups.
Internal medicine (Tokyo, Japan)
Jan 2, 2024
Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Ja...
Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support-a reader study.
European radiology
Jan 2, 2024
OBJECTIVES: The aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support.
Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 2, 2024
PURPOSE: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs).
Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone.
World journal of surgical oncology
Jan 2, 2024
BACKGROUND: Breast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practic...
Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity.
Academic radiology
Dec 30, 2023
RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DL) are at higher risk of developing COPD. It remains...
Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.
Journal of cancer research and therapeutics
Dec 28, 2023
PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.
Journal of magnetic resonance imaging : JMRI
Dec 27, 2023
BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were...
Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.
Academic radiology
Dec 27, 2023
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning rad...
Nomograms
Treatment Outcome
Neoadjuvant Therapy
Contrast Media
Magnetic Resonance Imaging
Osteosarcoma
Bone Neoplasms
Humans
Predictive Value of Tests
Retrospective Studies
Middle Aged
Female
Deep Learning
Young Adult
Adolescent
Child
Adult
Chemotherapy, Adjuvant
Sensitivity and Specificity
Radiomics
Male
Artificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software.
Journal of medical screening
Dec 20, 2023
OBJECTIVE: Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of pot...
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