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Objective: This study aimed to determine the prevalence and distribution of non-syndrome-related oligodontia in Turkish-children in Isparta and its surroundings.Materials and Method: The records of patients who were admitted to the Department of Pedodontics, Faculty of Dentistry of Süleyman Demirel University between 1999-2018, and diagnosed with oligodontia were searched. One-hundred-twelve individuals between the ages of 6 and 18 who did not have any systemic disorders, and missing ≥6 teeth except the third molars were included in the study. The distribution of detected tooth agenesis according to missing teeth, gender, and jaws and tooth agenesis patterns were examined. The frequency of tooth agenesis was given in numbers-percentages, and the Mann-Whitney U test was applied to the distribution of gender and missing teeth. The frequency analysis was calculated using Tooth Agenesis Code (TAC) values to determine tooth agenesis patterns.Results: The incidence of non-syndrome-related oligodontia in Turkish-children in Isparta and its surroundings was found to be 0.08%. The number of missing teeth per individual was found to be 9.05 and the most commonly missing teeth were observed as lower/upper second premolars. When the tooth agenesis was compared between genders and jaws, the differences were not statistically significant. Commonly observed tooth agenesis patterns were a combination of premolars and lateral incisor.Conclusion: According to the results, in Isparta and its surroundings, the presence of oligodontia was detected in 8 out of 10,000 children. Along with the early diagnosis, alternative treatment can be applied considering the severity and distribution of oligodontia., Amaç: Bu çalışmanın amacı, Isparta ve çevresinde yaşayan Türk çocuklarında oligodontinin prevalansı ve dağılımını belirlemektir.Gereç ve Yöntem: Süleyman Demirel Üniversitesi Diş Hekimliği Fakültesi, Pedodonti Anabilim Dalına 1999-2018 yılları arasında başvuran ve oligodonti tanısı konulmuş hasta kayıtları tarandı. Herhangi bir sistemik rahatsızlığı olmayan, üçüncü büyük azı dişleri hariç 6 ve daha fazla dişi eksik olan 6-18 yaş aralığında 112 birey çalışmaya dahil edildi. Saptanan diş eksikliklerinin, eksik diş sayılarına, dişlere, cinsiyete ve çenelere göre dağılımı ve diş eksikliği paternleri incelendi. Diş eksikliği görülme sıklığı sayı ve yüzde olarak tablolanıp, cinsiyet ve eksik dişlerin çenelere göre dağılımında Mann-Whitney U testi uygulandı. Diş eksikliği paternlerini belirlemek için Tooth Agenesis Code/Diş Eksikliği Kodu (TAC) değerleri verilerine frekans analizi yapıldı.Bulgular: Çalışmamızda Isparta ve çevresinde yaşayan Türk çocuklarında, sendroma bağlı olmayan oligodontinin görülme sıklığı %0.08 oranında saptandı. Birey başına düşen konjenital eksik diş sayısı 9.05 olarak bulundu ve eksikliği en sık görülen dişler, alt ve üst 2. küçük azı dişleri olarak gözlendi. Diş eksikliği cinsiyetler ve çeneler arasında karşılaştırıldığında aradaki farklar istatistiksel olarak anlamlı bulunmadı. Yaygın izlenen eksik diş paternleri, üst çenede küçük azı ve lateral kesici kombinasyonu iken, alt çenede küçük azı dişleri ve lateral kesici dişlerin kombinasyonu şeklinde oldu.Sonuç: Elde edilen sonuçlar dahilinde, Isparta ve çevresinde her on bin çocuktan 8’inde oligodonti varlığı tespit edildi. Oligodontinin erken teşhisi ile birlikte, şiddeti ve dağılımı göz önüne alınarak uygulanabilecek alternatif tedaviler önemli olabilir.

Amelogenesis imperfecta (AI) is one of the developmental enamel defects. In patients with AI, as well as enamel defects, some dental anomalies are encountered and may affect tooth development. For children whose enamel structure is not normal, whether an accurate result can be obtained by dental age determination methods is not clear. The aims of this study are to determine the dental age for the assessment of tooth development in patients with AI by 3 different methods, to decide a method that will present the most accurate result to the chronological age, and to build a basis in terms of postmortem identification. The records of patients who were referred to the Department of Pediatric Dentistry between the years of 1999 and 2018, and diagnosed with AI were reviewed. At the age of 6-15 years, 58 patients (33 males-25 females) (Group I) with AI were determined. A total of 116 healthy individuals (66 males-50 females) (Group II) who were age and gender matched with the AI group were selected as the control group. Chronological age was calculated, and Nolla's, Demirjian's and Haavikko's methods were used to estimate dental age. Chronological and dental ages were compared according to the group and gender. The accuracy of three methods was also evaluated. There was no statistical significant differences for the tooth development in terms of group and gender. Dental age was over-estimated by Demirjian's method and under-estimated by Nolla's and Haavikko's methods. Haavikko's and Nolla's methods were more accurate in the dental age estimation. AI did not present an impact on the dental development. By using particularly Haavikko's method, the possible margin of error in evaluations of dental development can be reduced. Similar studies comprising other defects affecting the tooth structure and morphology are also necessary.

Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare computing. A distributed end-to-end pervasive healthcare system utilizing neural network computations for diagnosing illnesses was developed. This work presents the initial results for a simple client (patient's PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture. The computations of neural network operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services.