AIMC Topic: Retrospective Studies

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Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different...

A multimodal deep learning model for predicting severe hemorrhage in placenta previa.

Scientific reports
Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using M...

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

Respiratory medicine
RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). How...

Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer.

Auris, nasus, larynx
OBJECTIVE: Transoral surgery (TOS) is a widely used treatment for laryngopharyngeal cancer. There are some difficult cases of setting the extent of resection in TOS, particularly in setting the vertical margins. However, positive vertical margins req...

The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight-bearing CT scanning.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Delayed diagnosis of syndesmosis instability can lead to significant morbidity and accelerated arthritic change in the ankle joint. Weight-bearing computed tomography (WBCT) has shown promising potential for early and reliable detection of i...

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

European radiology
OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed ...

Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic e...

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

Journal of thoracic imaging
PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiog...