AIMC Topic: Retrospective Studies

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Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs.

Joint diseases and related surgery
OBJECTIVES: This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP p...

Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.

Medical physics
BACKGROUND: Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) c...

Robotic inguinal hernia repair with the new Hugo RAS system: first worldwide case series report.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
INTRODUCTION: Robotic-assisted surgery has been a part of surgical procedures for more than two decades. Recently new robotic platforms with a different design entered the market. The modular design with independent arms enables increased flexibility...

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane.

BMC ophthalmology
BACKGROUND: To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.

Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.

European radiology
OBJECTIVE: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

European journal of nuclear medicine and molecular imaging
PURPOSE: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in ...

Robot-assisted Simple Enucleation Versus Standard Robot-assisted Partial Nephrectomy for Low- or Intermediate-complexity, Clinical T1 Renal Tumors: A Randomized Controlled Noninferiority Trial.

European urology oncology
BACKGROUND: Although partial nephrectomy has become the gold standard for T1 renal tumors whenever technically feasible, simple enucleation has shown superior results. To the best of our knowledge, no randomized controlled trials comparing these two ...