Forensic medicine has increasingly integrated advanced imaging technologies to improve the accuracy and efficiency of investigations. Techniques such as virtual autopsy, multi-detector computed tomography (MDCT), and artificial intelligence (AI)-driven imaging have revolutionized the identification of injuries and causes of death. Despite these advancements, the field faces operational, ethical, and legal challenges that hinder widespread adoption. Objectives: This systematic review aimed to explore the role of emerging imaging technologies in forensic medicine, identify key challenges in their implementation, and provide insights into optimizing their use in forensic practice, with particular attention to cultural, ethical, and interdisciplinary aspects. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed across six databases, including PubMed, Scopus, Web of Science, and others, focusing on studies discussing imaging technologies in forensic contexts. A total of 10 studies were included after applying eligibility criteria. The data were synthesized using narrative synthesis and thematic analysis. Results: Four key themes emerged: (1) advancements in AI and imaging technologies, (2) operational and financial barriers, (3) ethical and legal considerations, and (4) interdisciplinary collaboration and training. Emerging imaging modalities enhance diagnostic precision and facilitate non-invasive examinations, offering culturally sensitive alternatives to traditional autopsies. However, high costs, algorithmic biases, data security risks, and the lack of standardized forensic imaging protocols present significant challenges. The potential for cross-cultural and international forensic collaborations through AI-enabled imaging was also identified as a promising future direction. Conclusions: Advanced imaging technologies hold transformative potential in forensic medicine. Addressing financial, ethical, and operational challenges through interdisciplinary collaboration, standardized guidelines, and culturally sensitive practices is crucial for maximizing their utility and global acceptance

In recent years, deep learning (DL) has become indispensable in medical image segmentation (MIS), proven by numerous studies showcasing its effectiveness. This paper presents two significant original contributions and conducts a comprehensive thematic evaluation of DL approaches in MIS. Diverging from traditional surveys that categorize DL literature into multiple groups and analyze individual works within each category, we adopt a multi-level classification approach. This method organizes existing literature from a broad overview to finer details. Furthermore, to address the inherent challenges of this field, we introduce an innovative method called L-UNet. The L-UNet method strategically utilizes filters with reduced parameters to construct a U-shaped convolutional neural network. This not only demonstrates impressive performance but also alleviates complexities associated with architectural parameters. Extensive evaluation across multiple datasets, including lung image segmentation, PH2 skin cancer segmentation, liver image segmentation, Chest X-ray segmentation, and COVID-19 chest X-ray segmentation, highlights the exceptional performance of L-UNet. Notably, experimental results showcase competitive accuracies, achieving figures such as 99.15% for lung image segmentation, 99.53% for liver image segmentation, 95.45% for PH2 skin cancer segmentation, and 98.99% for Chest X-ray segmentation. Moreover, for COVID-19 chest X-ray segmentation, a commendable accuracy of 93.45% is observed. The simplification of L-UNet renders it a highly practical choice for deployment in resource-limited environments or real-world scenarios necessitating faster performance and enhanced efficiency.

Sprouting boosts bioactive compounds in Brassicaceae, including glucosinolates, amino acids, and anthocyanins. This study examined gamma radiation seed priming effects on growth, metabolism, and bioactive accumulation in Nasturtium officinale, Eruca sativa, and Raphanus raphanistrum sprouts. Seeds of the three species were primed with gamma radiation (0.1 kGy/h) and grown under controlled conditions. Biomass was measured; metabolic profiling quantified glucosinolates, amino acids, flavonoids, and anthocyanins. Enzymatic activities for glucosinolate and flavonoid biosynthesis were assessed. Antioxidant potential was assessed using FRAP and DPPH assays; antimicrobial activity was tested against Escherichia coli and Enterococcus faecalis. Microbial counts (APCs and Coliforms) were measured. Gamma radiation increased biomass by 48%, 36%, and 71% in N. officinale, E. sativa, and R. raphanistrum, respectively. Glucosinolates rose, especially glucoerucin (up to 227% in E. sativa) and glucoraphenin (up to 60%), linked to higher precursor amino acids (leucine, tryptophan, methionine), glutathione, and activities of glutathione S-transferase and sulfotransferase. Myrosinase activity increased, boosting sulforaphane. Flavonoids surged: quercetin (48 -191%), kaempferol (75 -172%), anthocyanins (42 -60%), with elevated PAL, CHS, 4CL, and C4H activities. Antioxidant and antimicrobial (40 -77%) potentials improved, though APCs and coliforms rose. Gamma radiation priming enhances growth, secondary metabolite accumulation, antioxidant potential, and antimicrobial activity in Brassicaceae sprouts. It offers a promising method to improve the nutritional and functional qualities of edible sprouts, aiding food safety and health.