Forensic Breakthrough: KMC Mangaluru Unveils AI-Powered Bloodstain Differentiation for Crime Scenes

In a significant stride for forensic science, Kasturba Medical College (KMC), Mangaluru, a constituent college of Manipal Academy of Higher Education (MAHE), has unveiled a pioneering application designed to accurately differentiate between menstrual and non-menstrual bloodstains found at crime scenes.

This innovative tool, developed by a dedicated team of researchers, promises to revolutionize how forensic experts approach crucial evidence, particularly in sensitive cases.

The distinction between types of bloodstains is often paramount in criminal investigations. Misinterpretation can lead to severe consequences, especially in sexual assault cases where the presence of menstrual blood might be misinterpreted as evidence of assault when it is not, or vice-versa.

This new application addresses a critical gap, offering clarity and precision that was previously difficult to achieve with conventional methods.

Led by Dr. B. Suresh Kumar, Professor and Head of Forensic Medicine, the research team includes Dr. Arjun R. V. (Assistant Professor, Forensic Medicine), Dr.

Ajith V. Kumar (Associate Professor, Medical Biochemistry), and Dr. Shivashankara A. R. (Professor, Medical Biochemistry). Their collaborative effort has culminated in a method that combines the power of UV-Vis spectroscopy with advanced machine learning (ML) models.

The methodology involves obtaining spectral data from blood samples using UV-Vis spectroscopy, a technique that measures the absorption of ultraviolet and visible light.

This data is then fed into various machine learning algorithms, including Support Vector Machine, Random Forest, K-Nearest Neighbours, and Logistic Regression. The synergy between these technologies allows for the robust and accurate classification of bloodstains.

Remarkably, the study, which has been published in the prestigious Journal of Forensic and Legal Medicine, reported an impressive accuracy of 99.8% and high specificity in distinguishing menstrual bloodstains.

What makes this advancement even more impactful is its non-invasive and non-destructive nature, meaning the original evidence remains intact for further analysis. Furthermore, the application provides rapid results, significantly speeding up the investigative process.

The implications of this breakthrough are far-reaching.

Beyond sexual assault cases, this tool can assist in determining the timing of events at a crime scene, helping investigators piece together a more accurate timeline. By providing an objective and scientifically rigorous method for bloodstain analysis, KMC Mangaluru's innovation stands to enhance the reliability of forensic evidence, thereby contributing significantly to the fair and timely delivery of justice.