The aim of this systematic review (SR) was to assess whether tooth mobility (TM) increases the risk of tooth extraction/loss. The protocol was registered in PROSPERO database (CRD42023485425). The focused PECO questions were as follows: (1) \"In patients with periodontitis, undergoing periodontal treatment, are teeth affected by mobility at higher risk of being extracted/lost compared to non-mobile teeth, with a minimum follow-up of 10 years?\" and (2) \"In these patients, does varying degrees of tooth mobility increase the risk of tooth extraction/loss, with a minimum follow-up of 10 years?\". Results were reported according to PRISMA statement. Electronic and manual searches were conducted to identify longitudinal studies. The different assessments of tooth mobility were pooled into three groups: TM0: Undetectable tooth mobility, TM1: Horizontal/Mesio-distal mobility ≤1 mm, TM2: Horizontal/Mesio-distal mobility >1 mm or vertical tooth mobility. Tooth loss was the primary outcome. Various meta-analyses were conducted, including subgroup analyses considering different follow-up lengths and the timing of TM assessment, along with sensitivity analyses. A trial sequential analysis was also performed. Eleven studies were included (1883 patients). The mean follow-up range was 10-25 years. The weighted total of included teeth, based on the sample size, was 18 918, with a total of 1604 (8.47%) extracted/lost teeth. The overall rate of tooth extraction/loss increased with increasing mobility: TM0 was associated with a 5.85% rate (866/14822), TM1 with the 11.8% (384/3255), TM2 with the 40.3% (339/841). Mobile teeth (TM1/TM2) were at an increased risk for tooth extraction/loss, compared to TM0 (HR: 2.85; [95% CI 1.88-4.32]; p < .00001). TM1 had a higher risk than TM0 (HR: 1.96; [95% CI 1.09-3.53]; p < .00001). TM2 had a higher risk than TM1 (HR: 2.85; [95% CI 2.19-3.70]; p < .00001) and TM0 (HR: 7.12; [95% CI 3.27-15.51]; p < .00001). The results of the tests for subgroup differences were not significant. Sensitivity meta-analyses yielded consistent results with other meta-analyses. Within the limits of the quality of the studies included in the meta-analyses, mobile teeth were at higher risk of being extracted/lost in the long-term and higher degrees of TM significantly influenced clinicians\' decision to extract a tooth. However, most teeth can be retained in the long-term and thus TM should not be considered a reason for extraction or a risk factor for tooth loss, regardless of the degree of TM.

The aim of this case series was to evaluate the necrosis of teeth adjacent to the site of mandibulotomy or mandibulectomy in a cohort of patients suffering from head and neck cancers.
Fourteen patients who underwent segmental mandibulectomy or paramedian mandibulotomy for oral, oropharynx or major salivary gland cancer and a total of 23 teeth were included in this case series. Twelve patients underwent adjuvant head and neck radiotherapy. Cold sensitivity pulp testing and/or electric pulp testing were performed on teeth at the margin of mandibulectomy and on teeth adjacent to mandibulotomy after surgery. A \"positive\" response was considered the healthy state, and \"negative\" was considered the diseased state of the tooth.
The 10 patients who underwent mandibulotomy had 12 teeth with a negative response. The 4 patients treated by mandibulectomy had two positive and three negative responses to cold and electric pulp tests. Fifteen out of 23 teeth (65.2%) showed a negative response to sensitivity testing.
Tooth necrosis seems to be a common event after mandibulectomy and mandibulotomy.
To avoid post-surgery complications, performing root canal therapy before surgery on the teeth adjacent to the surgical site could be an appropriate strategy.

The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1-Hopeless, 5-Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

The aim of this study was to determine the educational methods and tools used to teach tooth prognosis and treatment complexity determination in U.S. predoctoral dental programs. In 2018, an online survey was emailed to the academic deans of all 66 accredited U.S. dental schools. Of these, 42 schools responded (63.6%), and 36 schools completed the entire survey (54.5%). The methods reported for teaching tooth prognosis and case complexity determination varied widely among the participating schools. Among the respondents, 25% reported using the American Association of Endodontists\' Endodontic Case Difficulty Assessment, while 10% reported having no specific method for teaching prognosis. The most common method for teaching overall treatment complexity was the Prosthodontic Diagnostic Index, which was used by 24% of the respondents. However, another 24% reported that their school did not have a specific method for teaching treatment complexity. Large percentages of the respondents reported that students sometimes or often made wrong tooth prognosis and case complexity determination (90% and 92%, respectively). The most prominent feedback provided by the respondents based on their experience was the importance of faculty standardization, the understanding of students\' inexperience, and the need for an interdisciplinary approach. The majority of these respondents reported that their schools had specific methods of teaching prognosis and case complexity determination. However, there was a wide range of teaching practices related to the contents and levels of evidence.