This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we assessed Diagnocat's performance in detecting PARLs in non-root-filled molars and compared its diagnostic performance between preoperative and postoperative scans. This retrospective study analyzed preoperative and postoperative CBCT scans of 134 molars (327 roots). PARLs detected by Diagnocat were compared with assessments independently performed by two experienced endodontists, serving as the reference standard. Diagnostic performance was assessed at both tooth and root levels using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the receiver operating characteristic curve (AUC-ROC). In preoperative scans of non-root-filled molars, Diagnocat demonstrated high sensitivity (teeth: 93.9 %, roots: 86.2 %), moderate specificity (teeth: 65.2 %, roots: 79.9 %), accuracy (teeth: 79.1 %, roots: 82.6 %), PPV (teeth: 71.8 %, roots: 75.8 %), NPV (teeth: 91.8 %, roots: 88.8 %), and F1 score (teeth: 81.3 %, roots: 80.7 %) for PARL detection. The AUC was 0.76 at the tooth level and 0.79 at the root level. Postoperative scans showed significantly lower PPV (teeth: 54.2 %; roots: 46.9 %) and F1 scores (teeth: 67.2 %; roots: 59.2 %). Diagnocat shows promise in detecting PARLs in CBCT scans of non-root-filled molars, demonstrating high sensitivity but moderate specificity, highlighting the need for human oversight to prevent overdiagnosis. However, diagnostic performance declined significantly in postoperative scans of root-filled molars. Further research is needed to optimize the platform's performance and support its integration into clinical practice. AI-based platforms such as Diagnocat can assist clinicians in detecting PARLs in CBCT scans, enhancing diagnostic efficiency and supporting decision-making. However, human expertise remains essential to minimize the risk of overdiagnosis and avoid unnecessary treatment.

Dental resin composites are commonly used in the restorative management of teeth via adhesive bonding, which has evolved significantly over the past few decades. Although current self-etch bonding systems decrease the number of clinical steps, the acidic functional monomers employed exhibit a limited extent of demineralization of enamel in comparison to phosphoric acid etchants, and the resultant superficial ionic interactions are prone to hydrolysis. This study evaluates the etching of primers constituted with bis[2-(methacryloyloxy) ethyl] phosphate (BMEP) of dental hard tissue, interfacial characteristics, and inhibition of endogenous enzymes. We examine the incorporation of 2 concentrations of BMEP in the formulation of experimental primers used with a hydrophobic adhesive to constitute a 2-step self-etching bonding system and compare to a commercial 10–methacryloyloxydecyl dihydrogen phosphate (10-MDP)–containing system. The interaction of the primer with enamel and dentine was characterized using scanning electron, confocal laser scanning, and Raman microscopy while the polymerization reaction between the BMEP primers and hydroxyapatite was evaluated by Fourier-transform infrared spectroscopy. The inhibitory effect against matrix metalloproteinase (MMP) enzymes of these primers was studied and percentage of inhibition analyzed using 1-way analysis of variance and Tukey’s post hoc test (P < 0.05). Results of the scanning electron microscopy micrographs demonstrated potent etching of both enamel and dentine with the formation of longer resin tags with BMEP primers compared to the 10-MDP–based system. The BMEP polymerized on interaction with pure hydroxyapatite in the dark, while the 10-MDP primer exhibited the formation of salts. Furthermore, BMEP primers were able to inhibit MMP activity in a dose-dependent manner. BMEP could be used as a self-etching primer on enamel and dentine, and the high degree of polymerization in the presence of hydroxyapatite can contribute to an increased quality of the resin polymer network, prompting resistance to gelatinolytic and collagenolytic degradation.

Aim: Accurate detection of periapical radiolucent lesions (PARLs) is crucial for endodontic diagnosis. While cone beam computed tomography (CBCT) is considered the radiographic gold standard for detecting PARLs in non‐root filled teeth, its use is often limited by cost and radiation exposure. Artificial Intelligence (AI)‐based radiographic analysis has shown the potential to enhance the diagnostic capability of periapical (PA) radiographs, but its accuracy, compared with CBCT, needs further evaluation. The aim of this paper is to assess the diagnostic accuracy of Diagnocat, a commercial AI‐driven platform in detecting PARLs on PA radiographs of teeth diagnosed with irreversible pulpitis or pulp necrosis and scheduled for primary root canal treatment, using CBCT as the reference standard, and to compare Diagnocat's performance with that of experienced clinicians. Methodology: This retrospective diagnostic accuracy study analysed 339 teeth (796 roots). PA radiographs were independently assessed by two experienced, calibrated endodontists and by Diagnocat. CBCT scans, serving as the reference standard, were evaluated by two different endodontists, blinded to the PA radiograph results. Sensitivity, specificity, accuracy and area under the receiver‐operating characteristic curve (AUC‐ROC) were calculated for Diagnocat and clinicians at both tooth and root levels. Results: CBCT identified PARLs in 121 (35.7%) teeth and 240 (30.2%) roots. Diagnocat displayed a high level of correlation with clinicians in determining lesion status, with an agreement of 89%. Clinicians demonstrated significantly higher accuracy at the tooth level (86.1% vs. 78.5%, p < .001) and greater sensitivity (65.3% vs. 47.9%, p < .001) than Diagnocat, while specificity was comparable (97.7% vs. 95.4%, p = .3). Similar trends were observed at the root level. AUC‐ROC values were higher for clinicians than Diagnocat at both tooth (0.81 vs. 0.72) and root (0.77 vs. 0.68) levels, although these differences were not statistically significant. Conclusion: While Diagnocat exhibited high agreement with clinicians in detecting PARLs on PA radiographs, clinicians demonstrated superior accuracy and sensitivity overall. Notably, Diagnocat performed comparably to experienced clinicians in cases without PARLs, highlighting its potential utility for reliably ruling out disease. However, further refinement is required before it can reliably complement clinical judgment in endodontic practice.

The need for controlling bacteria and pain during root canal therapy is undeniable. This clinical trial aimed to assess whether there is a difference in colony-forming unit (CFU) reduction after instrumentation and post-endodontic pain after root canal treatment (RCT) using a traditional endodontic cavity (TEC) versus a conservative endodontic cavity (CEC). This clinical study was conducted on 89 patients designated for a single-visit RCT. Patients were allocated randomly (TEC n = 45 and CEC n = 44). The access opening was gained accordingly in each group by a single operator. A pre-instrumentation sample of root canal dentin was collected using an endodontic file; the second sample was collected similarly, right after shaping and cleaning the root canal. The CFU was calculated based on the samples collected. The pain level was recorded preoperatively and at 1, 7, and 21 days postoperatively utilizing a visual analog scale (VAS). There were no statistically significant differences in the CFU reduction between the TEC and CEC groups (p > 0.05). Additionally, there were no statistically significant differences found in postoperative pain levels between the TEC and CEC at 1, 7, and 21 days (p > 0.05). Despite the limitations of this study, both the CEC and TEC demonstrate a decrease in bacteria within the root canals and alleviate postoperative pain with no difference between them.