Contemporary exodontia

Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists

OBJECTIVES

Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability.

MATERIAL AND METHODS

Using 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS.

RESULTS

The ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589.

CONCLUSION

AI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information.

CLINICAL RELEVANCE

AI could help monitor at-risk teeth and reduce errors in indications for extractions.

A novel computationally engineered collagenase reduces the force required for tooth extraction in an ex-situ porcine jaw model

The currently employed tooth extraction methods in dentistry involve mechanical disruption of the periodontal ligament fibers, leading to inevitable trauma to the bundle bone comprising the socket walls. In our previous work, we have shown that a recombinantly expressed truncated version of clostridial collagenase G (ColG) purified from Escherichia coli efficiently reduced the force needed for tooth extraction in an ex-situ porcine jaw model, when injected into the periodontal ligament. Considering that enhanced thermostability often leads to higher enzymatic activity and to set the basis for additional rounds of optimization, we used a computational protein design approach to generate an enzyme to be more thermostable while conserving the key catalytic residues. This process generated a novel collagenase (ColG-variant) harboring sixteen mutations compared to ColG, with a nearly 4℃ increase in melting temperature. Herein, we explored the potential of ColG-variant to further decrease the physical effort required for tooth delivery using our established ex-situ porcine jaw model. An average reduction of 11% was recorded in the force applied to extract roots of mandibular split first and second premolar teeth treated with ColG-variant, relative to those treated with ColG. Our results show for the first time the potential of engineering enzyme properties for dental medicine and further contribute to minimally invasive tooth extraction.

A Minimally Invasive Technique for the Retrieval of Fractured Root Tips

Exodontia, the removal of a compromised tooth, ideally consists of the painless removal of the tooth or tooth root, with minimal trauma to the surrounding tissues, resulting in complete healing without creating postoperative prosthetic problems. Fractures of the tooth's root tip during exodontia can be common in some cases, such as in teeth with irregular root morphology or severely decayed teeth. The current article presents a technical report in which endodontic files made it possible to remove a fractured root tip from a maxillary third molar without using force.

Prescribing antibiotics by dentists in Colombia: Toward a conscientious prescription

OBJECTIVES

Inappropriate prescription of antibiotics contributes to antibiotic resistance. Therefore, the objective of this study was to determine the awareness, attitudes, and intention to practice of dentists prescribing antibiotics in Colombia in order to design a virtual learning environment on this subject.

METHODS

In a descriptive study across seven cities, 700 dentists from different Colombian cities were requested to complete a validated questionnaire containing five sections: general information, awareness on antibiotic effectiveness and antibiotic resistance, attitudes regarding prescription decision, intention to practice concerning clinical cases, and complementary information. The level of awareness, attitudes, and intention to practice was determined and Chi-square test was used to determine the existence of significant differences among cities.

RESULTS

The majority of dentists showed a medium level regarding the number of correct answers on awareness (62.4 percent) and attitudes (88.7 percent) and a high level on intention to practice (91.7 percent). Common errors within the awareness section included the meaning of the term "antibiotic resistance" (35 percent) and most dentists were not convinced that such resistance could be derived from prescription of antibiotics (51.2 percent). In the attitudes section, only 45 percent declared that they prescribe antibiotics based mainly on symptoms, and the intention to practice section showed a significant percentage of unnecessary prescription (51 percent for pacemaker users) or absence of prescription (53.9 percent for ventricular septal defect) in antibiotic prophylaxis for infectious endocarditis (IE).

CONCLUSION

The dentists interviewed should be trained and made aware of antibiotic resistance, microbiological and clinical foundations, and current antibiotic prophylaxis guidelines.