Interventions for iatrogenic inferior alveolar and lingual nerve injury (Review)

The inferior alveolar nerve (IAN) and lingual (LN) are susceptible to iatrogenic surgical damage. Systematically review recent clinical evidence regarding IAN/LN repair methods and to develop updated guidelines for managing injury. Recent publications on IAN/LN microsurgical repair from Medline, Embase and Cochrane Library databases were screened by title/abstract. Main texts were appraised for exclusion criteria: no treatment performed or results provided, poor/lacking procedural description, cohort <3 patients. Of 366 retrieved papers, 27 were suitable for final analysis. Treatment type for injured IANs/LNs depended on injury type, injury timing, neurosensory disturbances and intra-operative findings. Best functional nerve recovery occurred after direct apposition and suturing if nerve ending gaps were <10 mm; larger gaps required nerve grafting (sural/greater auricular nerve). Timing of microneurosurgical repair after injury remains debated. Most authors recommend surgery when neurosensory deficit shows no improvement 90 days post-diagnosis. Nerve transection diagnosed intra-operatively should be repaired in situ; minor nerve injury repair can be delayed. No consensus exists regarding optimal methods and timing for IAN/LN repair. We suggest a schematic guideline for treating IAN/LN injury, based on the most current evidence. We acknowledge that additional RCTs are required to provide definitive confirmation of optimal treatment approaches.

In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

Citation: Kushnerev E, Shawcross SG, Sothirachagan S, et al. Regeneration of corneal epithelium with dental pulp stem cells using a contact lens delivery system.

No abstract