Background: Thalassemia is a hereditary anemia resulting from defects in hemoglobin production. ?- thalassemia caused by decrease in the production of ?- globin chains affect multiple organs and is associated with cranio-oro-facial deformity which include prominent cheek bones and protrusive premaxilla with depression of the nasal bridge often referred to as “rodent or chip-munk face” with small mandible and Cl.II skeletal relationship. This study aimed to investigate cephalometric craniofacial parameters (skeletal) of ?- thalassemic major patients by using computed tomography and to compare findings with a group of healthy patients in the same age group. Subject, Materials and Method: The study included (40) patients with ?- thalassemic major (20 female and 20 male) with age 8-15years compared with (40) healthy controls (20 female and 20 male) with the same age, who admitted to spiral computed tomography scan unit in X-ray institute in AL-KARKH general hospital to have computed tomography scan for the brain, paranasal and for orthodontic purpose from October 2011 to June 2012. Cephalometric analysis of the selected four skeletal linear measurements and four skeletal angular measurements, by using direct analysis with software programs in a computer which is part of the computed tomography machine. Results: There was no statistically significant difference between thalassemic males and females in all selected skeletal linear and angular measurements, Thalassemic patients have a highly significant large ANB angle and cl II skeletal relationship, significant larger gonial angle, Mandibular base length (Me-Go) is significantly shorter, Retrognathic mandible (SNB) is significantly decreased, highly significant shorter in total anterior facial height (N-Me) and total posterior facial height (S-Go), and also the Ramus height is highly significant decreased. Conclusion: In thalassemic patients, the skeletal morphology is recognizable and mandible is retrognathism and they have skeletal cl. II pattern and Computed tomography is useful tool for assessment of the cranio facial measurement.
المستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة
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The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow
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