Automated Prediction of Extraction Difficulty and Inferior Alveolar Nerve Injury for Mandibular Third Molar Using a Deep Neural Network

Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.

Development of Radiographic Image Classification System for Weld Defect Identification using Deep Learning Technique

Weld defect identification from radiographic images is a crucial task in the industry which requires trained human experts and enough specialists for performing timely inspections. This paper proposes a deep learning based approach to identify different weld defects automatically from radiographic images. To employ this a dataset containing 200 radiographic images labelled for four types of welding defect- gas pore, cluster porosity, crack and tungsten inclusion is developed. Then a Convolutional Neural Network model is designed and trained using this database.

Calcinosis cutis in limited cutaneous systemic sclerosis

Soft tissue calcification is seen in some rheumatological diseases, including systemic sclerosis. We herein present a clinical image of calcinosis cutis of finger pulps and its characteristic radiographic image in a patient with limited cutaneous systemic sclerosis.

Improving radiographic image contrast using multi layers of histogram equalization technique

<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.</span></span>

Iodinated contrast media

Contrast media are the materials utilized in radiology to improve the visualization of certain body structures subjected to analysis in a medical image. Contrast media are, then, diagnostic drugs because they increase the visibility of organs and tissues and allow the identification of details that otherwise could not be observed. With the introduction of multidetector computed tomography the number of patients undergoing contrast studies has grown exponentially in recent years. In computed tomography the formation of the radiographic image is obtained with the use of contrast media containing iodine atoms, exploiting the different attenuation that the X-ray beam presents in crossing the anatomical districts. The Iodinated Contrast Media (ICM) are a category of numerous substances formed by even complex molecules that vary considerably in their properties, uses and toxic effects. Even though iodinated contrast agents have a good safety profile, it is necessary to understand that some patients can have severe, life-threatening allergic reactions because of the side effects of the chemicals. This paper describes the clinical pharmacology, use and adverse reactions of intravenous iodinated contrast media used in computerized tomography, offering all doctors, even non-radiologists, the opportunity for rapid updating.

Quality improvement of general anteroposterior radiographic image of vertebral body according to optimum angle of incidence

OBJECTIVE: In this study, we present an appropriate angle of incidence to reduce the distortions in images of L4 and L5 during a general anteroposterior radiograph examination. METHOD: We selected 170 patients who had normal radiological findings among those who underwent anteroposterior and lateral examination for lumbar vertebrae. An optimum angle of incidence wa suggested through the statistical analysis by measuring the lumbar lordosis angle and the intervertebral disc angle in these 170 patients. RESULT: We suggested the incident angle (10.28°) of L4 and the incident angle (23.49°) of L5. We compared the distorted area ratios when the incident angle was 0°, 10°, and 23.5° using the ATOM® phantom. The ratio for the L4 decreased from 14.90% to 12.11% and that of the L5 decreased from 15.25% to 13.72% after applying the angle of incidence. We determined the incident angle (9.34°) of L4 and (21.26°) of L5 below 30° of LLA. Thus, we determined the incident angle (11.21°) of L4 and (25.73°) of L5 above 30° of LLA. CONCLUSION: When you apply the optimum angle of incidence, the distortion of image was minimized and an image between the joints adjacent to the anteroposterior vertebral image with an accurate structure was obtained. As a result, we were able to improve the quality of the image and enhance diagnostic information.