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Total humeral replacement is a complex surgery that requires many challenges to overcome such as the weight of the implant material and the shoulder function due to extensive resection of the rotator cuff. Improvements in implants material that is lighter than usual can lead to higher surgery success rates. We present a patient who was diagnosed with osteosarcoma of the right humerus. The patient received 2 cycles of MAP chemotherapy (included: doxorubicin, cisplatin, and methotrexate) before surgery. He underwent radical resection of osteosarcoma and total humerus replacement with a modified total humeral material. The purpose of this improvement was to reduce the implant’s weight and to improve postoperative recovery. Six months after the surgery, the weight-bearing ability of the patient’s shoulder within a wide range of movement has restored; the shoulder, elbow, and hand can move in a controlled way. Despite the short postoperative follow-up time, the improvement in the modified technique has brought many positive results. Total humerus replacement, which combines the reverse shoulder prosthesis, elbow prosthesis, and polyetheretherketone, is an appropriate solution for patients with osteosarcoma of the humerus instead of custom-made humerus megaprosthesis.

The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.

With the emergence of deep learning, computer vision has witnessed extensive advancement and has seen immense applications in multiple domains. Specifically, image captioning has become an attractive focal direction for most machine learning experts, which includes the prerequisite of object identification, location, and semantic understanding. In this paper, semantic segmentation and image captioning are comprehensively investigated based on traditional and state-of-the-art methodologies. In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature extraction methods. First, each domain’s preliminaries and concept are described, and then semantic segmentation is discussed alongside its relevant features, available datasets, and evaluation criteria. Also, the semantic information capturing of objects and their attributes is presented in relation to their annotation generation. Finally, analysis of the existing methods, their contributions, and relevance are highlighted, informing the importance of these methods and illuminating a possible research continuation for the application of semantic image segmentation and image captioning approaches.