AIMC Topic: Sensitivity and Specificity
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Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.
Journal of thoracic imaging
Jan 1, 2025
PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set.
Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
European radiology
Dec 31, 2024
OBJECTIVES: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by ra...
Validation of AI-driven measurements for hip morphology assessment.
European journal of radiology
Dec 31, 2024
RATIONALE AND OBJECTIVES: Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI...
Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.
International journal of environmental research and public health
Dec 31, 2024
The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital ima...
Attention-based image segmentation and classification model for the preoperative risk stratification of thyroid nodules.
World journal of surgery
Dec 29, 2024
BACKGROUND: Despite widespread use of standardized classification systems, risk stratification of thyroid nodules is nuanced and often requires diagnostic surgery. Genomic sequencing is available for this dilemma however, costs and access restricts g...
Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.
Scientific reports
Dec 28, 2024
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and miti...
Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.
Academic radiology
Dec 26, 2024
RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...
Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review.
The British journal of oral & maxillofacial surgery
Dec 26, 2024
This systematic review aimed to evaluate the application of artificial intelligence (AI) in the identification of temporomandibular joint (TMJ) disc position in normal or temporomandibular joint disorder (TMD) individuals using magnetic resonance ima...
MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.
Clinical imaging
Dec 26, 2024
We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) stat...
Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.
Abdominal radiology (New York)
Dec 25, 2024
PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.
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