Autologous breast reconstruction in low- and middle-income countries (LMICs): a systematic review of current practices and challenges.

INTRODUCTION: Efforts to improve global healthcare persist, yet LMICs face challenges accessing surgical care, especially breast reconstruction amidst rising breast cancer cases. This review evaluates the present state and challenges of autologous breast reconstruction in low- and middle-income countries (LMICs). EVIDENCE ACQUISITION: Utilizing the PRISMA guidelines and the Cochrane Collaboration's standards, databases such as EMBASE, MEDLINE, Cochrane, PubMed, and Google Scholar were examined for studies on breast reconstruction in LMICs (based on the World Bank's 2022-2023 definitions) up to August 2022. Articles and case reports focusing on autologous reconstruction following breast cancer surgery in these regions were incorporated. EVIDENCE SYNTHESIS: From an initial 288 articles, 19 met the criteria after thorough assessment. These articles documented 4899 patient cases from LMICs, with the breakdown being: 11 on LD flaps, nine on TRAM flaps, eight on DIEP flaps, two on TDAP flaps, and one on TMG flap. Flap necrosis emerged as the prevalent complication in four studies. CONCLUSIONS: While autologous breast reconstruction presents superior aesthetic benefits without notable long-term economic setbacks, its adoption in LMICs is limited. This is partly due to the domination of implant-based methods among patients and surgeons, selected due to convenience. The scarcity of concrete evidence and standardized metrics in LMICs clouds the understanding of this procedure. Despite its advantages, awareness is low, necessitating more training and awareness campaigns. Uniform reporting, quality data, and financial analysis can provide a comprehensive LMIC understanding, aiding future research.

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review.

INTRODUCTION: Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth. EVIDENCE ACQUISITION: A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases. EVIDENCE SYNTHESIS: Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD). CONCLUSIONS: The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.