BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recogni...
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 3, 2024
OBJECTIVE: This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue.
BACKGROUND: Robotic spinal surgery may result in better pedicle screw placement accuracy, and reduction in radiation exposure and length of stay, compared to freehand surgery. The purpose of this randomized controlled trial (RCT) is to compare screw ...
The surgical robot is assumed to be a fixed, indirect cost. We hypothesized rising volume of robotic bariatric procedures would decrease cost per patient over time. Patients who underwent elective, initial gastric bypass (GB) or sleeve gastrectomy (S...
BACKGROUND: The KangDuo surgical robot (KD-SR-01) was recently developed in China. This study aims to evaluate the short-term outcomes of KD-SR-01 for colorectal cancer surgery.
BACKGROUND: Although there is increasing interest in minimally invasive prosthesis breast reconstruction (PBR), whether meshes application in minimally invasive PBR can improve complications and cosmetic effects remains controversial. The author retr...
BACKGROUND: Aim of the current study was to present the results of the implementation phase of a robotic liver surgery program and to assess the validity of the IWATE difficulty score in predicting difficulty and postoperative complications in roboti...
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imagi...
OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network.
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