Clinical Research Directory

Mineral Trioxide Aggregate (MTA) vs Gutta-Percha for Non-Surgical Endodontic Retreatment in Teeth With Apical Lesions

The main objective of this study is to compare the healing rate of periapical lesions after non-surgical endodontic retreatment in teeth obturated with gutta-percha versus those obturated with mineral trioxide aggregate (MTA), with a 12-month follow-up. Secondary objectives include evaluating differences between groups in healing time, postoperative pain, and tooth discoloration.

Evaluation of Revitalization Success Using i-PRF in Non-Surgical Root Canal Retreatment

The goal of this randomized controlled clinical trial is to determine whether regenerative endodontic treatment can improve healing outcomes in teeth with apical periodontitis following failed root canal treatment. The study includes adult patients who require non-surgical root canal retreatment due to persistent periapical lesions. The main questions it aims to answer are: Does regenerative endodontic treatment improve periapical healing compared with conventional non-surgical root canal retreatment? Does regenerative treatment increase long-term treatment success and tooth survival? Researchers will compare conventional non-surgical root canal retreatment with regenerative endodontic treatment using injectable platelet-rich fibrin (i-PRF) to see if the regenerative approach results in better clinical and radiographic outcomes. Participants will: Be randomly assigned to receive either conventional retreatment or regenerative endodontic treatment. Undergo clinical and radiographic evaluations over a two-year follow-up period. Be assessed for pain, sensitivity, pulp vitality, hard tissue formation, and periapical healing.

Survival and Success of Two Different Filling Techniques

This randomized clinical study analysed the clinical use of a new bioceramic premixed CaSi-containing sealer in association with a warm carrier-based technique or a traditional epoxy resin based sealer. Methodology: Healthy patients requiring root canal treatments were enrolled. Periapical X-rays were taken preoperatively, after root canal filling and after 1, 6, and 12 and 24 months. Two evaluators assessed the Periapical Index (PAI) and the sealer extrusion. The healing rate and survival rate were also evaluated. Barnard test was used to assess the relationship of each potential prognostic factor with periapical index (PAI) at 12-month follow-up. The significance level was set at 0.05.

Clinical Efficacy of AH Plus Bioceramic Sealer

The goal of this clinical trial is to assess the efficacy and outcome of sealer-based obturation (SBO) using a new calcium silicate sealer in comparison with warm vertical compaction (WVC) using a resin-based sealer, which is the current gold standard.

Calcium Silicate Sealer-based Obturation in Root Canal Retreatment

This study will evaluate the outcome of calcium silicate sealer-based obturations in root canal retreatment.

Role of GTR on Healing Outcome & Quality of Life in Combined Endo Perio Lesions With Communication

The recovery of periapical defects linked to periodontal communication frequently yields less satisfactory results. Numerous studies and case reports have highlighted the notable success achieved through the application of guided tissue regeneration techniques. More recently, autologous platelet concentrate plug \& placental membrane have emerged as a treatment option for addressing such lesions. Nevertheless, there has been no study using PRF-Medium with ACM membrane on the healing of aforesaid lesion \& quality of life assessment.

Postoperative Pain in Photodynamic Therapy Using Curcumin As a Photosensitizer

In endodontics, various irrigation solutions are used to achieve antibacterial efficacy within the root canal. The antibacterial effect of curcumin-containing root canal solutions, when combined with photodynamic therapy, has been demonstrated in several studies. However, no research has specifically examined their effect on postoperative pain. The aim of this study is to investigate the impact of using a curcumin solution in combination with photodynamic therapy as the final irrigation solution on postoperative pain.

Detection of Periapical Lesions on Dental Panoramic Radiographs Based on Artificial Intelligence

Dental periapical damages can have various reasons and is reflected by a radiolucent lesion on complementary imaging: angulated retro-alveolar (RA) radiographs, dental panoramic radiographs, and three-dimensional imaging such as computed tomography (CT) or cone-beam computed tomography (CBCT). For the radiographic detection of these deep periodontal lesions, the dental panoramic represents a first approach commonly performed with relatively low radiation. The investigation can be followed by retroalveolar radiology imaging that are more localized and more precise. However, using these techniques, the detection rates of these lesions are low (20% and 36% respectively), it is necessary to use three-dimensional tomographic investigation to be more discriminating (69%). The gold standard imaging for detection of these lesions is CBCT followed by retroalveolar radiography (\~2x less sensitive than CBCT) and panoramic radiography (\~2x less sensitive than RA). Although not a full-thickness radiograph, the dental panoramic has the advantage of being more commonly performed while being less radiating than CBCT and giving a global view of the dental arches on a single image. The detection of periapical lesions is done after a clinical assessment and a visual appreciation of the complementary examinations. The aim of this project is to improve the detection of periapical lesions, by developing an algorithm able to identify them on a panoramic dental radiograph. This algorithm is based on a deep learning system trained with reference data including panoramic dental imaging and CBCT with an acquisition interval of less than 3 months. The model is based on a previous work, will improve the quality of the initial data (using CBCT), using innovative artificial intelligence algorithms (transfer learning).