RESUMO
Introduction: Since mandibular third molars are frequently impacted, third molar extractions are among the most common procedures performed by oral surgeons (35.9-58.7% of all surgical procedures). Inferior alveolar nerve (IAN) injury is major postoperative complication in 0.81-22% of the cases leading to a permanent injury in 1-4% of the cases. Prior studies have proven that coronectomy, a procedure that involves the removal of the crown and coronal one-third of the roots of the third molar with intentional retention of the two-third apical roots to protect the IAN, can thus prove to be a viable alternative in such cases of close proximity to the IAN. Aim: This study was conducted in India to determine the knowledge, attitudes, and practices of oral and maxillofacial surgeons regarding coronectomy and its role in the prevention of IAN injury. Methodology: The questionnaire entitled: "Coronectomy: A Knowledge, Attitude and Practice (KAP) Survey among Oral and Maxillofacial Surgeons" was sent to 120 oral and maxillofacial surgeons. Five questions in each domain, i.e., knowledge, attitude, and practice were designed to know the level of awareness, acceptance, and current status of the performance of coronectomy among oral and maxillofacial surgeons (OMFS). Teeth with acute infection and mobile teeth were excluded from the consideration of coronectomy procedures. Results: Out of the 120 questionnaires sent, 50 responses were obtained, thus producing a response rate of 41.6%. The male-to-female ratio in the study was 34:16. 52% of the surgeons had performed up to 5 coronectomies during their entire practice, while 16 % had never even attempted the procedure. Only 42% of the respondents preferred coronectomy, but most of the surgeons were in support of practice-oriented continuing.
RESUMO
BACKGROUND: Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS: A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS: Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION: Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.