Loss of orbital content can cause functional impairment, disfigurement of the face, and psychological distress. Rehabilitation of an orbital defect is a complex task, and if reconstruction by plastic surgery is not possible or not desired by the patient, the defect can be rehabilitated by an orbital prosthesis. The prosthetic rehabilitation in such cases depends on the precisely retained, user-friendly removable maxillofacial prosthesis. Many times, making an impression of the orbital area with an accurate record of surface details can be a difficult procedure. The critical areas are making a facial moulage, mold preparation, and attaching the retention device, particularly when eyeglass frames are used. This case focuses on these hindrance factors. A simple basket was used for the impression tray to obtain the facial moulage. A putty mold was used, and attachment of the prosthesis to a retention device was accomplished with positional distance. This method proves to be an economical and simple way of making an orbital prosthesis.

Aim: Minimally invasive method for retrieving displaced objects like implants from the maxillary antrum with minimal complications.Background: Minimal invasive endoscopic surgery has been developed for various indications in the craniomaxillofacial area.

Age Related Macular Degeneration (ARMD) is a type of eye disease which normally have an effect on the central vision of a person. This Disease might sometimes lead to permanent vision loss for some people. It affects the people over the age of 50. So, basically there are 2 different types of ARMD i.e., Dry and Wet. Dry ARMD will generate a tiny amount of protein deposits called drusen, whereas Wet ARMD occurs whenever any abnormal blood vessel is developed under the retina, so sometimes this blood vessels might leak blood fluid, this type of ARMD is very severe and can even lead to permanent central vision loss. Therefore, it is necessary for early detection of the disease. Generative Data Augmentation for ARMD Classification is deep learning based which uses Convolutional Neural Network (CNN) model for generating images to accurately identify the disease. Deep Learning Diagnostic models require expertly graded images from extensive data sets obtained in large scale clinical trials which may not exist. Therefore, (Generative Adversarial Networks) GAN-based generative data augmentation method called Style GAN is used for generating the images. Generative deep learning techniques is used to synthesize new large datasets of artificial retinal images from different stages of ARMD using the images from the already existing datasets. The performance of ARMD diagnostic DCNNs will be trained on the combination of both real and synthetic datasets. Images obtained by using GAN appear to be realistic, and increase the accuracy of the model. It then continues with classifying the retinal images into one of the three classes i.e., dry, wet or normal using CNN model. It also compares the accuracy against the model with traditional augmentation techniques, towards improving the performance of real-world ARMD classification tasks.