Infrared Spectroscopy‑Enabled Molecular Fingerprinting of Blood Samples Coupled with Machine Learning for Multiplex Disease Screening - Aziz, Hawraa Abbas Alwan; Khidr, Elham Adham Noaman; Haji, Azad Mahmoud Abdul Jabbar; Muhammad, Karar Saeed Kazim; Hazaa, Sondos Adnan Nouri (American Journal of Biomedicine and Pharmacy, 2025)
Molecular fingerprinting of blood samples, when effectively combined with advanced machine learning techniques, facilitates an innovative approach to label-free multiplex screening of a wide array of both acute and chronic diseases. Blood, as a readily accessible and abundant source of vital biochemical information, serves as an exceptionally ideal medium for implementing such comprehensive screening methods. The seamless integration of molecular fingerprinting and artificial intelligence capitalizes on intrinsic molecular signatures, thereby enabling the rapid assessment of disease presence. This supports extensive screening initiatives that are crucial in today’s healthcare landscape. Moreover, this multifaceted approach transforms complex raw spectral data into clear and quantifiable molecular profiles. These profiles not only enhance the understanding of various pathological conditions but also provide critical insights into diseases such as cardiovascular conditions, type-2 diabetes mellitus, a range of liver diseases, and different types of cancers. Importantly, this method effectively meets the pressing demand for scalable, cost-effective, and minimally invasive disease screening solutions, especially during resource-constrained scenarios, which may include pandemic outbreaks and public health emergencies. Overall, the fusion of molecular fingerprinting with machine learning holds immense potential to revolutionize disease detection and monitoring practices in the future.