Clinical Research Directory

Evaluation of a 3D Printed Space Maintainer Versus the Conventional Band and Loop

The goal of this study is to evaluate whether a new 3D-printed dental device can effectively maintain space after premature loss of primary teeth in children, compared to the traditional stainless-steel device. It will also assess safety and comfort. The main questions it aims to answer are: 1. What is the clinical success rate of 3D printed space maintainers compared to traditional metal band and loop space maintainers over a 12-month period? 2. How do 3D printed space maintainers compare in maintaining space and preventing rotation of abutment teeth? 3. What are the differences in gingival health outcomes between the two types of space maintainers? Participants (13 children aged 6-9 years) will: Receive both devices in a "split-mouth" design (one on each side of the mouth), totaling 26 treated tooth spaces (13 per group). Attend checkups every 3 months for a year to monitor the devices' performance.

Space Changes After Premature Loss of Lower First Primary Molars in Children Using Digital Scanning

This prospective split-mouth clinical study aims to evaluate space changes in the mandibular dental arch following premature extraction of the lower first primary molar in children aged 3 to 5.5 years. Using digital 3D models, changes in the D space and mandibular arch perimeter will be measured at baseline and 6 months post-extraction. The goal is to determine whether space maintainers are necessary in the primary dentition stage.

Clinical Evaluation of 3-D Printed Partial Denture Space Maintainer

Primary dentition is the most suitable space maintainer for permanent teeth. However, as infants often lose their primary teeth too soon, using a space maintainer is the most efficient, long-lasting, and economical way to prevent malocclusions and function impairment in the future. Regarding personalized appliances, space maintainers in digital pediatric dentistry have made significant progress. Digital technology decreases the manual manufacturing stages that take a long time and increases the reliability and durability of digital fabrication procedures.

TORNADO-Omics Techniques and Neural Networks for the Development of Predictive Risk Models

The goal of this observational study is to define a personalized risk model in the super healthy and homogeneous population of Italian Air Force high-performance pilots. This peculiar cohort conducts dynamic activities in an extreme environment, compared to a population of military people not involved in flight activity. The study integrates the analyses of biological samples (urine, blood, and saliva), clinical records, and occupational data collected at different time points and analyzed by omic-based approaches supported by Artificial Intelligence. Data resulting from the study will clarify many etiopathological mechanisms of diseases, allowing the creation of a model of analyses that can be extended to the civilian population and patient cohorts for the potentiation of precision and preventive medicine.