RESUMEN
Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.
RESUMEN
BACKGROUND: This study was done to study the anthropometry of nonarthritic Asian knees; to determine the differences in morphology between knees of different ethnicities and to compare the knee anthropometry values with sizes of available knee implants. METHODS: Magnetic resonance imaging scans of 100 nonarthritic Indian knees were analyzed. Anteroposterior (AP) length, mediolateral (ML) length, and aspect ratio of the distal femur and proximal tibia, patellar length, and patellar tendon length were measured. These values were compared with values of other ethnicities from literature. The values were also compared with sizes of available knee implants and evaluated for mismatch. RESULTS: All the parameters of female knees were significantly smaller than those of male knees (p < 0.05). The distal femur of Indian knees resembled that of Chinese knees with similar AP and ML lengths and aspect ratio. The distal femur of Indian knees had a significantly smaller AP, ML, and aspect ratio than those of Hispanic knees did. In comparison to Caucasian distal femur, Indian knees had smaller AP and ML lengths and larger aspect ratio. In terms of the proximal tibia, the Indian knees were smaller than Chinese (only ML), Caucasian (AP and ML) and Hispanic (AP and ML) knees. On comparison with implant sizes, there was a mismatch between the distal femur morphology and the dimensions of all implants. For a given AP length, the ML dimensions of all implants were smaller than the measured ML length of the knee. However, the tibial components of all the studied implants correlated well with the tibial morphology. CONCLUSIONS: Distinct anthropometric differences exist between knees of different ethnicities. The knees of females were smaller than the knees of males. In Indian knees, the ML-AP aspect ratio of the distal femur was higher than that of the currently available femoral components. These results suggest the need for race-specific knee implants.