AIMC Topic: Tomography, X-Ray Computed
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Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?
European journal of radiology
Sep 17, 2024
BACKGROUND AND PURPOSE: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics...
A natural language processing-informed adrenal gland incidentaloma clinic improves guideline-based care.
World journal of surgery
Sep 17, 2024
INTRODUCTION: Adrenal gland incidentalomas (AGIs) are found in up to 5% of cross-sectional images. However, rates of guideline-based workup for AGIs are notoriously low. We sought to determine if a natural language processing (NLP)-informed AGI clini...
Benchmarking deep learning-based low-dose CT image denoising algorithms.
Medical physics
Sep 17, 2024
BACKGROUND: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques hav...
Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control.
Journal of imaging informatics in medicine
Sep 16, 2024
Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of d...
Intratumoral and Peritumoral Radiomics for Predicting the Prognosis of High-grade Serous Ovarian Cancer Patients Receiving Platinum-Based Chemotherapy.
Academic radiology
Sep 16, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning (DL) prognostic model to evaluate the significance of intra- and peritumoral radiomics in predicting outcomes for high-grade serous ovarian cancer (HGSOC) patients receiving platin...
SafeRPlan: Safe deep reinforcement learning for intraoperative planning of pedicle screw placement.
Medical image analysis
Sep 16, 2024
Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of the anatomy. Robotic surgery systems have been proposed to improve placeme...
A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.
Annals of medicine
Sep 16, 2024
OBJECTIVE: To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB).
An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.
Journal of medical engineering & technology
Sep 16, 2024
An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation fr...
Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.
Acta radiologica (Stockholm, Sweden : 1987)
Sep 15, 2024
BACKGROUND: Radiation dose should be as low as reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), the radiation dose may be considerably reduced.
Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.
Diagnostic and interventional imaging
Sep 14, 2024
PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of autom...
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