Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy

Orringer, Daniel A.; Pandian, Balaji; Niknafs, Yashar S.; Hollon, Todd; Boyle, Julianne; Lewis, Spencer; Garrard, Mia R.; Hervey-Jumper, Shawn L.; Garton, Hugh; Maher, Cormac O.; Heth, Jason; Sagher, Oren; Wilkinson, David; Snuderl, Matija; Venneti, Sriram; Ramkissoon, Shakti; McFadden, Kathryn; Fisher-Hubbard, Amanda; Lieberman, Andrew P.; Johnson, Timothy D.; Xie, X. Sunney; Trautman, J. K.; Freudiger, Christian W.; Camelo-Piragua, Sandra

doi:10.1038/s41551-016-0027

Cited by 467 publications

(455 citation statements)

References 31 publications

(35 reference statements)

Supporting

Mentioning

447

Contrasting

Order By: Relevance

“…The authors used 30 cases to show that MLP can distinguish non-diseased and diseased tissue with 100% accuracy. It can also distinguish between non-glioma and gliomas in diseased specimens with 90% accuracy, which is significant for neurosurgery [15].…”

Section: Rapid Diagnosis Of Brain Tumor Surgerymentioning

confidence: 99%

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (Preprint)

Shen¹,

Zhang²,

Jiang³

et al. 2018

Preprint

View full text Add to dashboard Cite

Artificial intelligence (AI) has been widely used in medical field, facilitating the development of medical services. This passage illustrates basic principles of AI application in medicine, summarizes some outstanding achievements of such application and discusses its merits and drawbacks. The passage is also an attempt to look into the future of AI in medical field. Keywords Artificial intelligence; disease diagnosis; treatment; deep-learning Artificial Intelligence in Disease Diagnosis and Medical TreatmentArtificial Intelligence (AI) is a field of program-based computer science that can simulate human's mental processes and intelligent activity and enable machines to solve problems with knowledge. In the information age, AI is widely used in the medical field and can promote medical development. AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials. [1] There are currently two common methods in AI. An expert system is a computer system that generates predictions under supervision and can outperform human experts in terms of decision-making. It consists of two interdependent subsystems: a knowledge base and an inference engine. While the knowledge base contains accumulated experience, the inference engine (a reasoning system) can access the current state of the knowledge base and supplement it with new knowledge. Expert systems can create critical information for the system more explicit, make maintenance easy and increase the speed of prototyping [2]. However, expert systems are limited in terms of knowledge acquisition and performance.The second method is machine learning. This is the core of AI and is a fundamental approach to making computers intelligent. Machine learning requires huge amounts of data for training. This improves their performance step by step during the process. One of the focuses underlying machine learning is parameter screening. Too many parameters can lead to inaccurate entries and calculations. Therefore, reducing the number of parameters can improve the efficiency of AI, but it may also lower its accuracy. However, one of the key objectives of AI is to outperform people via selfstudy in challenging fields without any prior knowledge.Google's DeepMind was recently updated with new version of AlphaGo Zero, which started from scratch by relying completely on itself for intensive study and selftraining without learning from any chess manual. It then defeated AlphaGo that had studied a large number of chess manuals before by 100 to 0. The study ushered in a new era of AI that it is free from human cognition and can discover new knowledge and strategies itself [3]. In China, a robot designed by iFlytek. passed the 2017 Licensing Examination of Medical Practitioners. As a result, it will assist doctors in medical diagnosis and treatment.In machine learning, neural network is an algorithm. The network acquires knowledge through many examples in med...

show abstract

Section: Rapid Diagnosis Of Brain Tumor Surgerymentioning

confidence: 99%

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (Preprint)

Shen¹,

Zhang²,

Jiang³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…41,43 The survey demonstrates a remarkable concordance (Cohen's kappa) between SRS and H&E, proving SRS as an e®ective diagnostic tool for brain tumor. This method has been used in studies of both animal models and human surgical specimens.…”

Section: Applications In Brain Tumor Detectionmentioning

confidence: 99%

“…. 41 This could be realized because H&E is a quasi-two-color imaging method with hematoxylin (blue) and eosin (pink) labels. Although SRS and H&E di®er in the detection of biomolecules, they could both provide key diagnostic features such as the morphologies of cell nucleus.…”

Section: Srs Imaging Of Human Brain Surgical Specimensmentioning

confidence: 99%

“…44,45 Broadband femtosecond OPO could also be used with either spectral slicing or spectral focusing techniques. 41,[46][47][48] Furthermore, synchronized picosecond and femtosecond sources could be applied to realize hyperspectral SRS with parallel detection. 49,50 Other types of multicolor and hyperspectral SRS could be found in previous works.…”

Section: Instrumentations Of Srs Microscopymentioning

confidence: 99%

“…2(c)]. 41 Second, simultaneous two-color SRS imaging method has been developed with dual-phase lock-in detection, allowing real-time virtual histology without the need of post processing. 57 Such a dual-phase modulation and detection method has also been applied for other imaging modalities such as transient absorption microscopy.…”

Section: Instrumentations Of Srs Microscopymentioning

confidence: 99%

See 2 more Smart Citations

Stimulated Raman scattering microscopy for rapid brain tumor histology

Yang

Chen

2017

J. Innov. Opt. Health Sci.

View full text Add to dashboard Cite

Rapid histology of brain tissues with su±cient diagnostic information has the great potential to aid neurosurgeons during operations. Stimulated Raman Scattering (SRS) microscopy is an emerging label-free imaging technique, with the intrinsic chemical resolutions to delineate brain tumors from normal tissues without the need of time-consuming tissue processing. Growing number of studies have shown SRS as a \virtual histology" tool for rapid diagnosis of various types of brain tumors. In this review, we focus on the basic principles and current developments of SRS microscopy, as well as its applications for brain tumor imaging.

show abstract

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis

et al. 2020

View full text Add to dashboard Cite

IMPORTANCE Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused. OBJECTIVES To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in

show abstract

Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy

Cited by 467 publications

References 31 publications

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (Preprint)

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (Preprint)

Stimulated Raman scattering microscopy for rapid brain tumor histology

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis

Contact Info

Product

Resources

About