Partial or complete resection of the maxilla during tumour surgery causes oronasal defects, leading to oral-maxillofacial dysfunction, for which the surgical obturator (SO) is an important treatment option. Traditional manufacturing of SOs is complex, time-consuming, and often results in inadequate fit and function. This technical note describes a novel digital workflow to design and manufacture a three-dimensional (3D)-printed hollow SO. Registered computed tomography and magnetic resonance imaging images are used for gross tumour delineation. The produced RTStruct set is exported as a stereolitography (STL) file and merged with a 3D model of the dental status. Based on these merged files, a personalized and hollow digital SO design is created, and 3D printed. Due to the proper fit of the prefabricated SO, a soft silicone lining material can be used during surgery to adapt the prosthesis to the oronasal defect, instead of putty materials that are not suitable for this purpose. An STL file of this final SO is created during surgery, based on a scan of the relined SO. The digital workflow results in a SO weight reduction, an increased fit, an up-to-date digital SO copy, and overall easier clinical handling.
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http://dx.doi.org/10.1016/j.ijom.2018.03.015 | DOI Listing |
Int J Gynecol Cancer
January 2025
Subdirección de Investigación Básica, Instituto Nacional de Cancerología, Tlalpan, Mexico City, Mexico. Electronic address:
Objective: Our retrospective study aimed to investigate the role of computed tomography (CT) using both the tomographic Fagotti index and the Sugarbaker peritoneal cancer index (PCI) in predicting the feasibility of optimal interval debulking surgery in epithelial ovarian cancer.
Methods: Patients with advanced ovarian cancer treated in our institution who were eligible for interval debulking surgery were identified and included in the study. A retrospective image collection was operated, and CT scan evaluations were conducted by 2 independent radiologists to establish both scores (Fagotti index and Sugarbaker PCI).
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Language Intelligence and Information Retrieval (LIIR) Lab, Department of Computer Science, KU Leuven, Leuven, Belgium.
The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text.
View Article and Find Full Text PDFImaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-µm sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples.
View Article and Find Full Text PDFDeformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations.
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