We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




New Technique Combines ML with SWIR Fluorescence Imaging for Precise Surgical Tumor Removal

By HospiMedica International staff writers
Posted on 30 Mar 2023
Print article
Image: Machine learning combined with multispectral infrared imaging can guide cancer surgery (Photo courtesy of Pexels)
Image: Machine learning combined with multispectral infrared imaging can guide cancer surgery (Photo courtesy of Pexels)

Surgical tumor removal remains among the common procedures in cancer treatment, with approximately 45% of cancer patients undergoing this procedure at some point. Recent advances in imaging and biochemical technologies have improved a surgeon's ability to distinguish between tumors and healthy tissue. One such technique that enables this distinction is "fluorescence-guided surgery" (FGS). A new study proposes a method for classifying healthy and tumor cells using an intensity-independent approach. This method combines machine learning with short-wave infrared (SWIR) fluorescence imaging to precisely detect the boundaries of tumors.

FGS involves staining the patient's tissue with a dye that emits infrared light when irradiated with a special light source. The dye selectively binds to the surface of tumor cells, enabling the detection of the location and extent of the tumor based on the emitted lightwaves. However, most FGS-based methods rely on the absolute intensity of the infrared emissions to differentiate pixels corresponding to tumors. This approach is problematic since intensity is influenced by lighting conditions, camera setup, dye quantity, and staining duration. Therefore, intensity-based classification can lead to inaccurate interpretation.

The new technique developed by researchers at the University College London (London, UK) involves capturing multispectral SWIR images of the dyed tissue, rather than relying solely on measuring the total intensity over one wavelength. To achieve this, the team sequentially placed six different wavelength frequency (color) filters in front of their SWIR optical system and registered six measurements for each pixel. By doing this, the researchers were able to create spectral profiles for each type of pixel, including background, healthy tissue, and tumor. Subsequently, they trained seven machine learning models to accurately identify these spectral profiles in multispectral SWIR images.

The research team conducted in vivo training and validation of the models using SWIR images of an aggressive type of neuroblastoma in a lab model. They also evaluated various normalization techniques to make pixel classification independent of absolute intensity and dependent only on the pixel's spectral profile. The study involved testing seven machine learning models, with the top-performing model achieving a remarkable per-pixel classification accuracy of 97.5%. Specifically, the accuracies for tumor, healthy, and background pixels were 97.1%, 93.5%, and 99.2%, respectively.

In addition, the model's results were found to be highly robust against variations in imaging conditions due to the normalization of the spectral profiles. This is desirable for clinical applications because testing of new imaging technologies is typically done in ideal conditions that are not reflective of the real-world clinical setting. Based on their findings, the research team is optimistic about the potential of this methodology. They believe that conducting a pilot study to implement it in human patients could lead to significant advancements in the field of FGS.

Multispectral FGS has the potential to go beyond the current study's scope. It can be used to remove unwanted reflections and surgical or background lights from images, as well as offer noninvasive ways of measuring lipid content and oxygen saturation. Multispectral systems also allow for the simultaneous use of multiple fluorescent dyes with different emission characteristics since the signals from each dye can be untangled from the total measurements based on their spectral profiles. This multiple dye approach can target multiple aspects of a disease, providing surgeons with even more information. Future studies will undoubtedly explore the full potential of multispectral FGS, unlocking doors to more effective surgical procedures for treating cancer and other illnesses.

Related Links:
University College London

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
12-Channel ECG
CM1200B
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
LED Phototherapy System
Bililed Mini+

Print article

Channels

Critical Care

view channel
Image: The permeable wearable electronics developed for long-term biosignal monitoring (Photo courtesy of CityUHK)

Super Permeable Wearable Electronics Enable Long-Term Biosignal Monitoring

Wearable electronics have become integral to enhancing health and fitness by offering continuous tracking of physiological signals over extended periods. This monitoring is crucial for understanding an... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.