A clinical Raman spectroscopy imaging system and safety requirements for <i>in situ</i> intraoperative tissue characterization

Daoust, François; Tavera, Hugo; Dallaire, F.; Orsini, Patrick; Savard, Keven; Bismuth, J.; Mckoy, Philippe; Veilleux, Israël; Petrecca, Kevin; Leblond, Frédéric

doi:10.1039/d2an01946a

Cited by 7 publications

(8 citation statements)

References 40 publications

(44 reference statements)

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“…Nonetheless, comparable studies have demonstrated effective results through algorithmic processing ( 62 ). A secondary limitation lies in the non-application of the model machine in vivo due to potential challenges the probe might introduce to surgical sterility requirements ( 63 ). Furthermore, the portable fiber-optic Raman spectrometer has yet to be fully integrated, thus impeding true portability.…”

Section: Discussionmentioning

confidence: 99%

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Li,

Sun

et al. 2023

Front. Oncol.

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IntroductionOral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer.MethodsToward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses.ResultsThe developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination.DiscussionThus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

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Section: Discussionmentioning

confidence: 99%

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Li,

Sun

et al. 2023

Front. Oncol.

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“…This could be achieved multiple ways, including, for example, using a spectrophotometer with increased light sensitivity (e.g., HT rather than EH spectrometer from EmVision LLC) and a more powerful laser, within the maximum permissible exposure limits to ensure minimal tissue damage from increased temperature (e.g., <43°C). 50 Other more stringent safety standards as set by ANSI Z136.3 Laser Safety in Healthcare for skin and ocular exposition could be addressed through user guidelines and using safety goggles.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

et al. 2023

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Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages. Aim:The purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine.Approach: A portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train-and test on independent hold-out data subsets-a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model. Results:The five-class model achieved >96% accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches. Conclusions:We provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.

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“…The scientific community has recognized that Raman spectroscopy faces several problems, including weak signals, long acquisition times, fluorescence interference in biological samples, time-consuming data processing, and high cost. [236] However, significant advances have been made over the past few decades with continuous advances in instrumentation technology and machine learning to address these challenges. To overcome the signal weakness and improve the SNR, complementary techniques such as SERS, RRS, and SRS have been widely used, which provide strong support for Raman spectroscopy.…”

Section: Discussionmentioning

confidence: 99%

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Wang,

Fang,

Wang

et al. 2023

Advanced Science

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Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

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A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization

Abstract: Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system...

Cited by 7 publications

References 40 publications

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

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