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Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFImproving Thoracic Trauma Care: Locoregional Analgesia in the Intensive Care Unit.
Cureus
December 2024
Intensive Care Unit, Unidade Local Saúde Viseu Dão-Lafões, Viseu, PRT.
Article Synopsis
- Pain management for thoracic trauma patients traditionally relies on opioids, which can have significant side effects; locoregional anesthesia/analgesia (LRAA) offers a targeted alternative.
- The study analyzed 43 LRAA procedures in 33 ICU patients and found that 50% who received LRAA avoided intubation, leading to shorter ICU stays (9 vs. 13 days) and no severe complications from the LRAA techniques.
- Results indicate that early use of LRAA can enhance clinical outcomes for thoracic trauma patients, highlighting its potential but also the need for further investigation to understand its full benefits.
A cerebral clue in the stomach: Brain lesion leads to discovery of hidden gastric cancer.
Int J Surg Case Rep
January 2025
Department of Radio Oncology, School of Medicine Cancer Prevention Research Center Seyyed Al-Shohada Hospital Isfahan University of Medical Sciences, Iran. Electronic address:
Article Synopsis
- Brain metastases from gastric adenocarcinoma are very rare, accounting for only 0.1-0.16% of cases, and can present significant diagnostic challenges, especially when neurological symptoms appear before gastrointestinal ones.
- An 82-year-old male experienced non-specific neurological symptoms and was ultimately diagnosed with brain metastasis from a primary gastric tumor through advanced imaging and biopsies, underscoring the complexity of diagnosis in such cases.
- The case illustrates the necessity for a thorough diagnostic process and a coordinated treatment approach involving chemotherapy and targeted radiation therapy to effectively manage complex cancer presentations.
Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.
J Imaging
December 2024
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. This study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks.
View Article and Find Full Text PDFMulti-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.
Biomed Eng Online
December 2024
Department of Bioengineering, University of Louisville, Louisville, KY, USA.
Purpose: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic resonance imaging (MRI) and the clinical marker prostate-specific antigen (PSA). It addresses the limitations of Convolutional Neural Networks (CNNs) in capturing long-range spatial relations and the Vision Transformer (ViT)'s deficiency in localization information due to consecutive downsampling. The research question focuses on improving PC response prediction accuracy by combining both approaches.
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