AIMC Topic: Pelvis
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Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 3, 2024
Artificial intelligence can standardize and automatize highly demanding procedures, such as manual segmentation, especially in an anatomical site as common as the pelvis. This study investigated four automated segmentation tools on computed tomograph...
Recent trends in AI applications for pelvic MRI: a comprehensive review.
La Radiologia medica
Aug 3, 2024
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the ...
Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.
Medical physics
Jul 27, 2024
BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic ...
Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.
Radiation oncology (London, England)
Jul 9, 2024
BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.
Assessment of Pelvic Tilt in Anteroposterior Radiographs by Area Ratio Based on Deep Learning.
Spine
Jul 8, 2024
STUDY DESIGN: Diagnostics.
Artificial Intelligence Imaging Diagnosis Using Super-Resolution and Three-Dimensional Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center.
Diseases of the colon and rectum
Jun 11, 2024
BACKGROUND: Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, the accuracy of present diagnostic modalities has room for improvement.
Multidetector Computed Tomography
Proctectomy
Predictive Value of Tests
Pelvis
Rectal Neoplasms
Male
Middle Aged
Pilot Projects
Imaging, Three-Dimensional
Lymph Nodes
Adenocarcinoma
Lymphatic Metastasis
Lymph Node Excision
Female
Aged
Adult
Sensitivity and Specificity
Humans
Artificial Intelligence
Retrospective Studies
Deep-learning-based pelvic automatic segmentation in pelvic fractures.
Scientific reports
May 28, 2024
With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the pr...
Extended pelvic lymph node dissection in robot-assisted radical prostatectomy is an independent risk factor for major complications.
Journal of robotic surgery
Mar 30, 2024
The aim of this study is to evaluate the major postoperative complication rate after robot-assisted radical prostatectomy (RARP) and to identify related risk factors. A consecutive series of patients who underwent RARP between September 2016 and May ...
Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.
Journal of neurosurgery. Spine
Mar 29, 2024
OBJECTIVE: Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automate...
Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment.
Journal of orthopaedic surgery and research
Mar 25, 2024
PURPOSE: An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations.
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