In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole-genome sequencing (WGS) analysis, with a specific focus on its implications in oncology. Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI’s emergence as a potent ally in processing and analyzing the voluminous genomic data generated. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the importance of ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more successful personalized and targeted treatments.

Introduction: Saphenous vein graft aneurysm (SVGA) is a rare but life-threatening complication following coronary artery bypass grafting (CABG). The authors aim to identify the potential risk factors that lead to SVGA in post-CABG patients. Methods: A systematic review of original studies, observational studies, systematic reviews, meta-analyses, case studies, and case series was conducted using PubMed, Web of Science, Scopus, EMBASE, and Google Scholar involving adult patients (>18) with SVGA after CABG using MESH terminology in a broad search strategy. All searches were performed and analyzed according to PRISMA and duplicates were removed via Rayyan. Two independent investigators extracted and assessed the data involving demographics, and baseline data related to CABG and its manifestations. Results: Out of 487 finalized articles, 14 of them matched the inclusion requirements and reported 12 cases of SVGAs following CABG. Atherosclerosis with intimal calcification was the most common risk factor followed by infection. Others included hyperlipidemia, pneumonia, and cardiac pathologies mostly related to the ventricles and valves. Conclusion: Atherosclerosis associated with intimal calcification is the most common risk factor. Patient outcomes seem to improve upon early identification and regular follow-up imaging. The exclusion criteria indicated the study’s limits, and future studies that address these constraints may be able to better understand the risk variables involved in the genesis of SVGA.

INTRODUCTION : In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole genome sequencing (WGS) analysis, with a specific focus on its implications in surgical oncology. METHODS: Scoping review of whole genomic sequencing and artificial intelligence.DISCUSSION : Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI's emergence as a potent ally in processing and analyzing the voluminous genomic data generated by these technologies. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. CONCLUSIONS : This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the imperative for ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more personalized and targeted treatments in surgical oncology and perhaps beyond.