AIMC Topic: Pelvis
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A dataset for quality evaluation of pelvic X-ray and diagnosis of developmental dysplasia of the hip.
Scientific data
May 26, 2025
Developmental Dysplasia of the Hip (DDH) stands as one of the preeminent hip disorders prevalent in pediatric orthopedics. Automated diagnostic instruments, driven by artificial intelligence methodologies, are capable of providing substantial assista...
Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.
Biomedical physics & engineering express
May 19, 2025
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided ...
Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.
Scientific reports
Apr 30, 2025
This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic cancer using a prototype DL-based automated planning support system, named RatoGuide, to evaluate it...
Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 17, 2025
BACKGROUND: Accurate identification and evaluation of lymph nodes (LNs) in prostate cancer (PCa) patients is crucial for effective staging but can be time-consuming. We utilized a 3D V-Net model to improve the efficiency and accuracy of LN detection ...
Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.
AJR. American journal of roentgenology
Feb 12, 2025
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.
Radiation oncology (London, England)
Feb 4, 2025
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
PSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.
European journal of nuclear medicine and molecular imaging
Jan 27, 2025
PURPOSE: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pel...
Automatic skeletal maturity grading from pelvis radiographs by deep learning for adolescent idiopathic scoliosis.
Medical & biological engineering & computing
Jan 22, 2025
Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed of the spine. A child's Risser stage of skeletal maturity must be carefully considered for AIS evaluation and treatment. However, there are intra-observer and inter...
Improved Image Quality Through Deep Learning Acceleration of Gradient-Echo Acquisitions in Uterine MRI: First Application with the Female Pelvis.
Academic radiology
Jan 21, 2025
RATIONALE AND OBJECTIVES: The aim of this study was to compare the image quality of a deep learning (DL)-accelerated volumetric interpolated breath-hold examination (VIBE) sequence with a standard (ST) VIBE sequence in assessing the uterus.
Deep Learning for Predicting Difficulty in Radical Prostatectomy: A Novel Evaluation Scheme.
Urology
Jan 13, 2025
OBJECTIVE: To explore new metrics for assessing radical prostatectomy difficulty through a two-stage deep learning method from preoperative magnetic resonance imaging.
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