Method for recommendation in the classification of persistent periapical lesion by apical surgery

Abstract

The present investigation describes a solution to the problem posed by developing a method for recommendation in the classification of persistent periapical lesions using apical surgery. The research favors guidelines, procedures and technical tools that allow ensuring patient safety. The research proposes an innovative method to improve the classification and treatment of persistent periapical lesions using apical surgery, integrating clinical and technological approaches. The study addresses the need for accurate classification as a basis for effective therapeutic recommendations, which contributes to improving clinical outcomes and reducing postoperative complications. The method combines advanced radiographic analysis, machine learning techniques and specific clinical criteria to identify and categorize lesions according to their morphology, extension and behavior. A recommendation system was designed that evaluates patient data to suggest personalized surgical procedures. The research used a mixed approach, including an observational study in patients with persistent periapical lesions and a validation of the model through simulations and evaluations by specialists. The results show greater accuracy in lesion classification and significant improvement in surgical planning.