Review of recent advances in non-invasive hemoglobin estimation

Abstract

Hemoglobin is essential for diagnosing conditions like anemia and respiratory issues. Traditionally, the assessment of hemoglobin necessitates invasive techniques that involve blood draws, which can induce discomfort and present possible complications for patients. Recent advancements in non-invasive technologies have light-emitting diode (LED) to the development of smartphone applications and machine learning algorithms that allow real-time hemoglobin level estimation, eliminating the need for blood sampling. This not only improves patient comfort but also enhances access to ongoing health monitoring. This review aims to delve into the newest developments in smartphone-oriented strategies for hemoglobin estimation, highlighting their importance within contemporary healthcare practices and the potential implications they might have for more expansive clinical applications. Technological advancements have combined smartphones and artificial intelligence (AI) for non-invasive hemoglobin estimation, offering a promising alternative to traditional methods. These solutions optimize data collection and analysis processes, enhance diagnoses' accuracy, and facilitate timely medical interventions. Advancements in technology have revolutionized medical diagnostics, particularly in estimating hemoglobin levels non-invasively. AI methodologies have demonstrated significant results in accurately forecasting hemoglobin concentrations through a variety of analytical strategies. Future research should focus on the best configurations for these networks and the physiological concepts underpinning spectral data interpretations.