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Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and 3D Slicer, the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude, this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.
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http://dx.doi.org/10.1007/s10278-019-00239-7 | DOI Listing |
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
Nat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFMol Psychiatry
September 2025
Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
Dysregulated dopaminergic signaling has been implicated in the pathophysiology of major depressive disorder (MDD) and childhood sexual abuse (CSA), but inconsistencies abound. In a multimodal PET-functional MRI study, harnessing the highly selective tracer [C]altropane, we investigated dopamine transporter availability (DAT) and resting-state functional connectivity (rsFC) within reward-related regions among 112 unmedicated individuals (MDD: n = 37, MDD/CSA: n = 18; CSA no MDD: n = 14; controls: n = 43). Striatal DAT and seed-based rsFC were assessed in the dorsal and ventral striatum and the ventral tegmental area.
View Article and Find Full Text PDFZhonghua Jie He He Hu Xi Za Zhi
September 2025
Pulmonary and Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.
To explore the feasibility and accuracy of predicting respiratory tract infections (RTIs) using physiological data obtained from consumer-grade smartwatches. The study used smartwatches and paired mobile applications to continuously collect physiological parameters while participants slept. A personalized baseline model was established using multi-day data, followed by the construction of RTIs risk prediction algorithm based on deviations from physiological parameter trends.
View Article and Find Full Text PDFMed Eng Phys
October 2025
Mechatronics Engineering Department, Sakarya University of Applied Sciences, Serdivan, Sakarya, 54600, Sakarya, Turkey; Systems Engineering Department, Military Technological College, Al Matar, Muscat, 111, Muscat, Oman. Electronic address:
Balance is a critical component of daily activities and overall quality of life. This study aims to develop a cost-effective exercise system for the rehabilitation of balance disorders by combining a sensor module with target-oriented video games. The system, designed using a microcontroller-controlled sensor module and Unity game engine, features a game component that provides visual feedback and is synchronized with the platform movements.
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