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Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest. [ABSTRACT FROM AUTHOR]

Micro-expression (ME) recognition has attracted numerous interests within the computer vision circle in different contexts particularly, localization, magnification, and recognition. Challenges in these areas remain relevant due to the nature of ME's split-second transition with minute intensity levels. In this paper, a comprehensive state-of-the-art analysis of ME recognition and detection challenges are provided. Contemporary solutions are categorized into low-level, mid-level, and high-level solutions with a review of their characteristics and performances. This paper also provides possible extensions to basic methods, highlight, and predict emerging trends. A thorough analysis of mainstream ME datasets is also provided by elucidating each of their advantages and limitations. This survey gives readers an understanding of ME recognition and an appreciation of future research direction in ME recognition systems. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is a zoonotic betacoronavirus that was first reported at the dawn of 2019 in Wuhan, China and has since spread globally, causing an ongoing pandemic. Anthroponotic transmission was reported early, with confirmed infections reported in 26 species to date, including dogs and cats. However, there is a paucity of reports on the transmission of SARS-CoV-2 to companion animals, and thus, we aimed to estimate the seroprevalence of SARS-CoV-2 in dogs and cats in Sarawak, Malaysia. From August 2022 to 2023, we screened plasma samples of 172 companion animals in Sarawak, Malaysia, using a species-independent surrogate virus neutralization test. Our findings revealed the presence of neutralizing antibodies of SARS-CoV-2 in 24.5% (27/110) of dogs and 24.2% (15/62) of cats. To the best of our knowledge, this is the first report of the seroprevalence of SARS-CoV-2 in companion animals in Malaysia. Our findings emphasize the need for pet owners to distance themselves from their pets when unwell, and a strategy must be in place to monitor SARS-CoV-2 in companion animals to assess the potential impact of the virus on companion animals. [ABSTRACT FROM AUTHOR]