Three-dimensional visualization technology (3DVT) has been recently introduced to achieve a precise preoperative planning of liver surgery. The aim of this observational study was to assess the accuracy of 3DVT for complex liver resections. 3DVT with hyper accuracy three-dimensional (HA3D™) technology was introduced at our institution on February 2020. Anatomical characteristics were collected from two-dimensional imaging (2DI) and 3DVT, while intraoperative and postoperative outcomes were recorded prospectively. A total of 62 patients were enrolled into the study. 3DVT was able to study tumor extension and liver anatomy, identifying at least one vascular variation in 37 patients (59.7%). Future remnant liver volume (FRLV) was measured using 2DI and 3DVT. The paired samples t test assessed positive correlation between the two methods (p < 0.001). At least one vessel was suspected to be invaded by the tumor in 8 (15.7%) 2DI cases vs 16 (31.4%) 3DVT cases, respectively. During surgery, vascular invasion was detected in 17 patients (33.3%). A total of 73 surgical procedures were proposed basing on 2DI, including 2 alternatives for 16 patients. After 3DVT, the previously planned procedure was changed in 15 cases (29.4%), due to the clearer information provided. A total of 51 patients (82%) underwent surgery. The most frequent procedure was right hepatectomy (33.3%), followed by left hepatectomy (23.5%) and left trisectionectomy (13.7%). Vascular resection and reconstruction were performed in 10 patients (19.6%) and portal vein was resected in more than half of these cases (66.7%). 3DVT leads to a more detailed and tailored approach to complex liver surgery, improving surgeons' knowledge of liver anatomy and accuracy of liver resection.
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http://dx.doi.org/10.1007/s13304-022-01365-8 | DOI Listing |
BMC Cancer
January 2025
Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
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View Article and Find Full Text PDFSci Rep
January 2025
Information Initiative Center, Hokkaido University, Sapporo, Japan.
This research utilizes time series models to forecast electricity generation from renewable energy sources and electricity consumption. The configuration of optimal parameters for these models typically requires optimization algorithms, but conventional algorithms may struggle with fixed search patterns and limited robustness. To address this, we propose an auto-evolution hyper-heuristic algorithm named AE-GAPB.
View Article and Find Full Text PDFAn assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and DeepBraTumIA generally provide accurate segmentations, particularly for the most common round-shaped or well-demarked tumors, where: (1) gross tumor segmentation correctly includes necrosis and contrast enhanced tumor in 100% and 97.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Software Engineering Department, LUT University, Lahti, Finland.
Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.
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Sensors (Basel)
December 2024
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
Due to its non-contact characteristics, remote photoplethysmography (rPPG) has attracted widespread attention in recent years, and has been widely applied for remote physiological measurements. However, most of the existing rPPG models are unable to estimate multiple physiological signals simultaneously, and the performance of the limited available multi-task models is also restricted due to their single-model architectures. To address the above problems, this study proposes MultiPhys, adopting a heterogeneous network fusion approach for its development.
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