Publications by authors named "T Caramella"
Purpose: The purpose of this study was to report the initial results of the declaration of care-related adverse events (CRAEs) in radiology in the French National Authority for Health (HAS) database for accreditation of radiological medical teams.
Materials And Methods: Between October 2018 and December 2020, 48 radiological teams (32 teams in 2019 and 16 teams in 2020; 471 registered radiologists) signed up to the team accreditation process, a system supported by the HAS. Reports of the CRAEs in radiology started in September 2019 after the team registration phase.
Purpose: The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4.
Materials And Methods: An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results.
Purpose: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers.
Materials And Methods: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams.
Article Synopsis
- The study aimed to create an algorithm using convolutional neural networks (CNN) that can automatically estimate coronary artery calcium (CAC) from unenhanced ECG-gated CT scans.
- Researchers trained a CNN with a 3D U-Net architecture on 783 CT scans to detect and segment calcifications, calculating the Agatston score and comparing it to radiologist assessments.
- The final model achieved a high accuracy (C-index of 0.951), although it struggled with small or low-density calcifications near the mitral valve, potentially enhancing workflow by automating the CAC scoring process.
Purpose: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018.
Materials And Methods: Relevant clinical questions were chosen by the Société Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints.