India’s race, religion, and caste are quite diverse. Even within the same nation, regional variations exist in the ABO blood type and the Rh system. The current research examined the relationship between diseases and the ABO blood type among Nagaland’s Chakhesang ethnic communities. This research considered the population of sick people with ABO blood types. One hundred persons, including men and women from the Chakhesang tribe, served as research respondents. The Chakhesang Naga tribe was selected for this study because of the documented higher prevalence of hypertension and diabetes mellitus within this group compared to the broader regional population. The study also aimed to explore a possible association between these health conditions and blood type A. The ABD antisera typing Kit’s standard methodology was followed for blood group testing. S2 ABO software was used to compute the Hardy-Weinberg model, and the chi-square test was used to compare the results. In this research, we discovered that blood type A was more likely to develop hypertension and diabetes than blood types B and O (blood type A, X² = 16.3, P = 0.00∗; blood type B, X² = 18, P = 0.00∗; blood type O, X² = 0.085, P = 0.87). This might imply that blood type A may be genetically predisposed to diabetes and hypertension more than other blood types. Our research shows that, compared to healthy individuals, the prevalence of hypertension and diabetes was much higher in the general population. The Chakhesang Naga tribe has the highest prevalence of blood type B, while those with blood type A are the most afflicted and sensitive to hypertension and diabetes. A key limitation of the study is that the findings are based on a specific population and may not be generalizable. Larger and more diverse cohorts are needed to evaluate their broader applicability.

Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology.