AI-Boosted Raman Spectroscopy for Surgical Precision Tools

Published Date : 19/07/2025 

A groundbreaking method merges artificial intelligence with Raman spectroscopy to accurately differentiate between tissue types, such as bone, fat, and muscle, during surgical procedures. 

A new method combines artificial intelligence (AI) with a laser-based technique called Raman spectroscopy to distinguish between tissue types like bone, fat, and muscle commonly encountered in orthopedic and neurosurgical procedures. By identifying the unique biological signatures that guide AI tissue classification, it lays the foundation for smart surgical tools with the added feature of biomolecular-level precision.

The basic science article, published in Lasers in Surgery and Medicine (LSM), the official journal of the American Society for Laser Medicine and Surgery, Inc. (ASLMS), was selected as the August 2025 Editor’s Choice. This innovative approach aims to empower surgeons with better decision-making tools during orthopedic and neurosurgical procedures by revealing the unique biomolecular signatures of commonly encountered tissues—something traditional techniques often miss.

“We conducted this study to empower surgeons with better decision-making tools during orthopedic and neurosurgical procedures by revealing the unique biomolecular signatures of commonly encountered tissues—something traditional techniques often miss,” said Dr. Soha Yousuf, a postdoctoral fellow at the Laboratory for Advanced Bio-Photonics and Imaging (LAB-PI) at New York University Abu Dhabi.

The authors aimed to support real-time, informed surgical decision-making by identifying key Raman biomarkers that distinguish each tissue type and enhance the transparency of the machine learning models driving those decisions. Together, the models developed delivered high classification performance and interpretable outputs that offer molecular-level insights.

“By integrating Raman spectroscopy with interpretable machine learning, we not only identified key tissue biomarkers but also illuminated how these features guide classification decisions,” she continued. “This represents a significant step toward building smart, transparent technologies that can enable safer, more precise, and trustworthy real-time surgical guidance.”

Dr. Soha Yousuf is a postdoctoral associate at the Laboratory for Advanced Bio-Photonics and Imaging (LAB-PI) at New York University Abu Dhabi. Her research focuses on optical sensing technologies, mainly Raman spectroscopy, in combination with machine learning for tissue classification applications. She also conducts projects to advance Surface-Enhanced Raman Spectroscopy (SERS) platforms for biomedical applications. She received her PhD in Electrical and Computer Engineering from Khalifa University, United Arab Emirates.

This innovative approach has the potential to revolutionize surgical procedures by providing surgeons with real-time, precise, and trustworthy information about the tissues they are operating on. The integration of Raman spectroscopy and AI not only enhances the accuracy of tissue differentiation but also improves the overall safety and effectiveness of surgical interventions. 

Frequently Asked Questions (FAQS): 

Q: What is Raman spectroscopy?

A: Raman spectroscopy is a non-destructive analytical technique used to identify and characterize materials based on their unique molecular vibrations. It involves shining a laser on a sample and analyzing the scattered light to determine the material's chemical composition.

Q: How does AI enhance Raman spectroscopy in surgical tools?

A: AI enhances Raman spectroscopy by processing the data collected from the laser interactions to identify specific tissue types. Machine learning algorithms can classify tissues with high accuracy and provide real-time feedback to surgeons, improving decision-making during procedures.

Q: What are the benefits of using AI and Raman spectroscopy in surgery?

A: The benefits include improved accuracy in tissue differentiation, enhanced surgical precision, reduced risk of errors, and better patient outcomes. Surgeons can make more informed decisions in real-time, leading to safer and more effective surgical procedures.

Q: Who conducted the study on AI and Raman spectroscopy in surgery?

A: The study was conducted by Dr. Soha Yousuf and her team at the Laboratory for Advanced Bio-Photonics and Imaging (LAB-PI) at New York University Abu Dhabi. Dr. Yousuf is a postdoctoral fellow specializing in optical sensing technologies and machine learning.

Q: What is the future potential of this technology?

A: The future potential of this technology is significant. It can be integrated into various surgical tools to provide real-time, precise guidance during operations. This can lead to more successful surgeries, reduced recovery times, and improved patient care. 

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