Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.

Background: The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments.

Artificial Intelligence for Otosclerosis Detection: A Pilot Study.

The gold standard for otosclerosis diagnosis, aside from surgery, is high-resolution temporal bone computed tomography (TBCT), but it can be compromised by the small size of the lesions. Many artificial intelligence (AI) algorithms exist, but they are not yet used in daily practice for otosclerosis diagnosis. The aim was to evaluate the diagnostic performance of AI in the detection of otosclerosis.

Three artificial intelligence data challenges based on CT and ultrasound.

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.

Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

Purpose: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.

Materials And Methods: An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies.

Robot-Assisted Laparoscopic Management of Caliceal Diverticular Calculi.

Standard treatment modalities of caliceal diverticular calculi range from extracorporal shockwave lithotripsy (SWL) over retrograde intrarenal surgery (RIRS), percutaneous nephrolithotomy (PNL) and laparoscopic stone removal. A 55-year-old woman presented with a history of pyelonephritis based on a caliceal diverticular calculus. Due to the narrow infundibulum and anterior location, a robot-assisted laparoscopic calicotomy with extraction of the calculi and fulguration of the diverticulum was performed, with no specific perioperative problems and good stone-free results.