Proceedings of The 11th International Conference on Research in Engineering, Science & Technology
Year: 2021
DOI: https://www.doi.org/10.33422/11th.restconf.2021.03.35
Proposal of Automation of Local Dental Anesthetics Application Process Supported by Prediction Algorithms Based on Fuzzy Logic
Thiago Antunes da Silva Barbosa, Polyana Vanessa Gonçalves, Milena Carvalho Tourino Ribeiro, Laila Gabriela de Figueiredo Costa, Daniele Daiany da Silva Gomes, Vinicius Borges de Oliveira, João Pedro Ferreira Silva, Gabriel Lavalle Garrido, Luiz Melk de Carvalho, Diva de Souza e Silva Rodrigues, Flávio Henrique Batista de Souza
ABSTRACT:
Local anesthetics in dental procedures, whether simple or invasive, are found on the pharmacological market in different associations. However, its use must follow the standards requirements and the individual systemic conditions of the patient, otherwise severe complications or death may occur. Studies have demonstrated a lack of the necessary knowledge from dental surgeons and undergraduates, regarding the applicability of these drugs. Thus, this research demonstrates a proposal for an automation process, to assist in the decision making of these trained (or in training) professionals. The solution consists of a structure to collect anamnesis data, via a reception totem, from the waiting patient. With the data inserted, a sequence of rules based on Fuzzy logic, analyzes by inference with the 5 main anesthetics of the Brazilian protocol. Such inference shows the professional, according to the dosages, whether or not to treat the case. The parameters considered by the Fuzzy logic were: Age of the patient; if the patient is pregnant; hypertension; diabetes; asthma; duration of the procedure; and weight (in kg). The attributed results consider a combination of drugs (Lidocaine, Epinephrine, Articaine, Mepivacaine, Prilocaine, Felipressin) or the option of not treating. In addition to optimizing the waiting time in medium to large clinics, it is possible to minimize the risks of incorrect administration.
Keywords: Fuzzy; Anesthetics; Totems; Medical Errors; Health Education.