Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

A comprehensive review on antimicrobial face masks: an emerging weapon in fighting pandemics.

The world has witnessed several incidents of epidemics and pandemics since the beginning of human existence. The gruesome effects of microbial threats create considerable repercussions on the healthcare systems. The continually evolving nature of causative viruses due to mutation or re-assortment sometimes makes existing medicines and vaccines inactive. As a rapid response to such outbreaks, much emphasis has been placed on personal protective equipment (PPE), especially face mask, to prevent infectious diseases from airborne pathogens. Wearing face masks in public reduce disease transmission and creates a sense of community solidarity in collectively fighting the pandemic. However, excessive use of single-use polymer-based face masks can pose a significant challenge to the environment and is increasingly evident in the ongoing COVID-19 pandemic. On the contrary, face masks with inherent antimicrobial properties can help in real-time deactivation of microorganisms enabling multiple-use and reduces secondary infections. Given the advantages, several efforts are made incorporating natural and synthetic antimicrobial agents (AMA) to produce face mask with enhanced safety, and the literature about such efforts are summarised. The review also discusses the literature concerning the current and future market potential and environmental impacts of face masks. Among the AMA tested, metal and metal-oxide based materials are more popular and relatively matured technology. However, the repeated use of such a face mask may pose a danger to the user and environment due to leaching/detachment of nanoparticles. So careful consideration is required to select AMA and their incorporation methods to reduce their leaching and environmental impacts. Also, systematic studies are required to establish short-term and long-term benefits.