ESTUDO DE CASO DE UMA PACIENTE COM TRANSTORNO DO ESPECTRO AUTISTA, COM BASE NO PRONTUÁRIO ODONTOLÓGICO DA FAESA

Autism Spectrum Disorders (ASD) are complex disorders that involve a patient's neurological development. They are characterized by changes in social interaction, language and motricity, as well as stereotypical and repetitive behaviors. Patients may also present hypersensitivity to loud noises and bright lights. To answer specific anamnesis for patients with ASD, also taking into account patients' information that don't fit in Kanner's classic description. The chosen method was the Descriptive Case Study model, established through filling a specific anamnesis directed at a patient with ASD. The Odontological Medical Record of the clinic at FAESA was the basis for the data collection. It was not possible to answer many of the questions in the anamnesis from the data available on the medical records. Most of the questions were answered through previous contact with the patient. Other questions could not be answered by any means. A specific anamnesis for ASD patients should be included during treatment at FAESA's clinics for Patients with Special Needs. Treatment of patients with autism is still a great challenge for dental surgeons, since it requires knowledge of the problem and specific, objective techniques to cause the least possible trauma.

EPIDEMIOLOGICAL ANALYSIS OF PATIENTS SERVED IN A PUBLIC DENTAL SERVICE FOR PATIENTS WITH SPECIAL NEEDS IN BELÉM

Objective: In view of the scarcity of research aimed at patients with special needs in our region, this study aims to identify the pathologies, such as age, gender, place of origin and prevalence of caries and periodontal disease in patients enrolled in a public dental service in Belém aimed at the care of people with special needs. Method: Retrospective cross-sectional study, where the data collected in the medical records of the patients enrolled in SIDOPE-UFPA were analyzed. Results: At the end of the data collection, 219 medical records of patients eligible to participate in the study were divided into groups, such as Behavioral Disorder (27.85%), Non-Progressive Brain Encephalopathy (10.5%), Syndromes and malformations (8.7%), Intellective deficit (7.76%), among others. The prevalence of caries was 71.23% and Periodontal Disease was 23.7%; (61.6%) and Belém (62.5%) were the most prevalent place of origin. Conclusion: According to the research, most patients still need treatment, due to the high caries index found, and mainly preventive measures so that these numbers can be reduced in the future. It was also observed the complexity and variety of diseases in the patients enrolled, and the incidence of rare syndromes that should and should be studied more specifically, not only by dentistry, but also by professionals from other areas, emphasizing the importance of care multiprofessional approach to these patients.

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

Dental and Periodontal Care at the Bedside Using a Portable Dental Unit in Hospitalized Special Needs Patients: The Experience of an Italian Pediatric Hospital

Patients with special needs (SNPs) include individuals who are disabled due to physical limitations, medical complications, developmental problems, and cognitive impairments. SNPs may be at an increased risk of oral diseases throughout their lifetime. These patients have difficulties in accessing traditional dental studios or clinics. Moreover, orodental problems may cause local and generalized infections, leading to worrisome complications when not properly treated. In this paper, we describe the preliminary experience of treating dental problems in a series of nine hospitalized patients with special needs. This innovative protocol at the Bambino Gesù Children’s Hospital (Rome, Italy) provides an introduction to a portable dental unit in order to perform oral care for hospitalized patients at the bedside. A multidisciplinary team composed of pediatric dentists, dental hygienists, nursing staff, and the patient’s case manager was involved in the operative protocol. The SNPs described were affected by congenital heart or oncohematological diseases and neurodisabilities, and they were all hospitalized for different reasons: Open heart surgery, chemotherapy, organ transplantation, and rehabilitation. The oral evaluation was mandatory for ruling out or treating problems that could cause complications. Dental extractions, caries and fracture fillings, sealing, and oral hygiene procedures were performed at the bedside of the patients in the reference unit of their pediatric hospital. The results of this protocol confirm the feasibility of dental procedures at patients’ bedside with portable dental units, encourage implementation of their use, and may represent an actionable model for oral care management in hospitalized SNPs.