Medical devices are many and various, ranging from tongue spatulas to implantable or invasive devices and imaging machines; their lifetimes are short, between 18 months and 5 years, due to incessant incremental innovation; and they are operator-dependent: in general, the clinical user performs a fitting procedure (hip implant or pacemaker), a therapeutic procedure using a non-implantable invasive device (arrhythmic site ablation probe, angioplasty balloon, extension spondyloplasty system, etc.) or follow-up of an active implanted device (long-term follow-up of an implanted cardiac defibrillator or of a deep brain stimulator in Parkinson's patients). A round-table held during the XXVIII(th) Giens Workshops meeting focused on the methodology of scientific evaluation of medical devices and the associated procedures with a view to their pricing and financing by the French National Health Insurance system. The working hypothesis was that the available data-set was sufficient for and compatible with scientific evaluation with clinical benefit. Post-registration studies, although contributing to the continuity of assessment, were not dealt with. Moreover, the focus was restricted to devices used in health establishments, where the association between devices and technical medical procedures is optimally representative. An update of the multiple regulatory protocols governing medical devices and procedures is provided. Issues more specifically related to procedures as such, to non-implantable devices and to innovative devices are then dealt with, and the proposals and discussion points raised at the round-table for each of these three areas are presented.
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http://dx.doi.org/10.2515/therapie/2013036 | DOI Listing |
J Clin Orthod
November 2024
Division of Orthodontics, Department of Craniofacial Sciences; Division of Orthodontics, University of Connecticut School of Dental Medicine, Farmington, CT.
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
Am J Sports Med
January 2025
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, New York, USA.
BMC Oral Health
January 2025
4th Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China.
Background: The stability of soft and hard tissues surrounding the implant is not only a matter of aesthetics, but also affects the long-term stability of the implant. The present study was to explore the influence of buccal mucosa width/height (W/H) ratio, emergence profile and buccal bone width on peri-implant soft and hard tissue changes in the posterior region.
Methods: Fifty-eight posterior implant restoration cases were recruited in this study.
BMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
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