AIMC Topic: Sensitivity and Specificity
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Deep learning-based fully automatic screening of carotid artery plaques in computed tomography angiography: a multicenter study.
Clinical radiology
May 6, 2024
AIM: To develop and validate a deep learning (DL) algorithm for the automated detection and classification of carotid artery plaques (CAPs) on computed tomography angiography (CTA) images.
The application of deep learning in abdominal trauma diagnosis by CT imaging.
World journal of emergency surgery : WJES
May 6, 2024
BACKGROUND: Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross-sectional images of the abdominal area, particularly in cases of abdominal trauma, which is commonly encountered in traumatic injuries. However, inte...
Pseudo-class part prototype networks for interpretable breast cancer classification.
Scientific reports
May 6, 2024
Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key...
Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.
Abdominal radiology (New York)
May 4, 2024
PURPOSE: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).
Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.
Clinical radiology
May 3, 2024
AIM: This study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image.
Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients.
The Journal of clinical pediatric dentistry
May 3, 2024
Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric...
A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.
Scandinavian journal of urology
May 2, 2024
OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in ...
An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography.
Ultrasound in medicine & biology
May 1, 2024
BACKGROUND: Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is ofte...
Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.
International journal of surgery (London, England)
May 1, 2024
OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data...
Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.
BMC medical imaging
Apr 29, 2024
PURPOSE: To detect the Marchiafava Bignami Disease (MBD) using a distinct deep learning technique.
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