article · Informatics in Medicine Unlocked
Anaemia remains a significant global health issue, primarily impacting children and pregnant women. Standard clinical diagnosis often encounters obstacles, including testing costs, shortages of medical personnel in remote locations, and patient reluctance towards invasive procedures. Machine learning approaches provide a promising alternative because they are non-invasive, accessible, and simple to operate. This systematic literature review investigates current machine learning trends for anaemia detection, evaluating prominent algorithms across factors such as performance metrics, image augmentation methods, and dataset sources and sizes. The analysis confirms that non-invasive machine learning techniques deliver timely and cost-effective detection. Overall, the findings demonstrate the feasibility and scientific validity of deploying machine and deep learning algorithms to analyse medical images for clinical anaemia diagnosis.
Anaemia affects vulnerable populations globally, yet standard diagnostic pathways can be slow, costly, and invasive. Demonstrating the reliability of machine learning algorithms on medical images supports the development of non-invasive screening tools. This evidence is vital for healthcare providers seeking affordable diagnostic options that can be deployed effectively in resource-constrained or remote environments where laboratory access and trained personnel are limited.
The findings support the development of non-invasive image-based diagnostic software for healthcare workers, particularly in under-resourced or remote clinics. However, as this study is a systematic literature review analysing existing research, datasets, and algorithms, the technology remains at an early-stage review phase rather than representing an applied, tested, or near-market product.
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Anaemia is a global public health challenge that affects children and pregnant women. Anaemia develops when the body's supply of red blood cells declines or when the structure of the cells is weakened. The clinical diagnosis of anaemia has several challenges in practice, including insufficient funding for medical tests, inadequate medical personnel and resources in remote areas, and client reluctance that results in abstinence. Several machine learning techniques for anaemia detection have been developed due to their affordability, simplicity of use, and non-invasive nature as compared to the invasive approach. We perform a systematic review and examine current trends and concepts of machine learning in healthcare services to identify viable approaches to detect anaemia. We compare the most successful machine learning algorithms currently in use regarding machine learning, evaluation metrics, image augmentation, and the origin and size of the dataset used. The result of this study is a clear indication that non-invasive methods such as the use of machine learning algorithms to detect anaemia are affordable, and provides results on time. This systematic review provide scientific evidence, with the results describing how effective machine learning could make anaemia detection feasible. Machine and deep learning algorithms are introduced and used to make a wide-ranging analysis of images, diagnosis, and clinical analysis in the disciplines of medical fields such as anaemia detection.
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DOI: 10.1016/j.imu.2023.101283
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