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Objective To investigate the relationships between eruption status, gender, social class, grade of operator, anaesthetic modality and nerve damage during third molar surgery. Design Two centre prospective longitudinal study. Setting The department of oral and maxillofacial surgery, University Hospital Birmingham NHS Trust and oral surgery outpatient clinics at Birmingham Dental Hospital. Subjects A total of 391 patients had surgical removal of lower third molars. Sensory disturbance was recorded at one week post operatively. Patients with altered sensation were followed up at one month, three months and six months following surgery. Results 614 lower third molars in 391 patients were removed. Fortysix procedures (7.5%) were associated with altered sensation at one week with three procedures (0.49%) showing persistent symptoms at six months. Of these 46 nerve injuries, 26 (4.23%) involved the lingual nerve and 20 (3.25%) the inferior dental nerve (IDN). All three persistent sensations were IDN related. A logistic regression model found that the use of a lingual retractor χ 2 =11.559, p=0.003 was more significant than eruption status χ 2 =12.935, p=0.007. There was no significant relationship between anaesthetic modality, age, social class, sex and seniority of operator. Conclusions There was no link between the choices of local or general anaesthesia and nerve damage during lower third molar removal when difficulty of surgery was taken into account.Third molar surgery may be associated with iatrogenic nerve damage. The incidence of nerve damage following the removal of third molars is well documented. Lingual nerve injury ranges from 0.5%-22% and inferior dental nerve (IDN) injury between 0.5%-7% with a mean of 4%. 1-5 Several factors have been associated with an increased risk of nerve damage; the removal of unerupted teeth, 6 lingual retraction using a narrow instrument

This study clearly demonstrates the majority of the facial trauma in the older people can be treated conservatively unless the patients complain of functional problems.

Character recognition from handwritten images has received greater attention in research community of pattern recognition due to vast applications and ambiguity in learning methods. Primarily, two steps including character recognition and feature extraction are required based on some classification algorithm for handwritten digit recognition. Former schemes exhibit lack of high accuracy and low computational speed for handwritten digit recognition process. The aim of the proposed endeavor was to make the path toward digitalization clearer by providing high accuracy and faster computational for recognizing the handwritten digits. The present research employed convolutional neural network as classifier, MNIST as dataset with suitable parameters for training and testing and DL4J framework for hand written digit recognition. The aforementioned system successfully imparts accuracy up to 99.21% which is higher than formerly proposed schemes. In addition, the proposed system reduces computational time significantly for training and testing due to which algorithm becomes efficient.