[{"innerBlocks":[],"name":"core\/freeform","postId":139,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"A new laser-based imaging system combined with artificial intelligence is able to provide accurate, real-time intraoperative diagnosis of brain tumors.That\u2019s the finding of a new study<\/a>, published on Monday in the journal Nature Medicine<\/i>, which assessed the diagnostic accuracy of brain tumor image classification using machine learning vs. pathologist interpretation of conventional histologic images.Researchers discovered that the accuracy of the AI-based diagnosis was comparable to the pathologist-based interpretation (94.6 percent and 93.9 percent, respectively).Convolutional neural networks (CNNs) \u201clearned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors,\u201d according to the study\u2019s authors. In addition to CNNs, the study leveraged stimulated Raman histology (SRH), an advanced optical imaging technique that reveals tumor infiltration in human tissue by collecting scattered laser light and illuminating essential features not typically seen in standard histologic images.\u201cWe implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images,\u201d contend the authors. \u201cThese results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.\u201dCNNs were trained using more than 2.5 million SRH images and predicted brain tumor diagnosis in the operating room in under two and a half minutes\u2014an order of magnitude faster than conventional techniques.\u201cAs surgeons, we\u2019re limited to acting on what we can see; this technology allows us to see what would otherwise be invisible, to improve speed and accuracy in the OR and reduce the risk of misdiagnosis,\u201d says senior author Daniel Orringer, MD, associate professor in the Department of Neurosurgery at NYU Langone, who helped develop SRH. \u201cWith this imaging technology, cancer operations are safer and more effective than ever before.\u201d\u201cSRH will revolutionize the field of neuropathology by improving decision-making during surgery and providing expert-level assessment in the hospitals where trained neuropathologists are not available,\u201d adds co-author Matija Snuderl, MD, associate professor in the Department of Pathology at NYU Langone.","saveContent":"A new laser-based imaging system combined with artificial intelligence is able to provide accurate, real-time intraoperative diagnosis of brain tumors.That\u2019s the finding of a new study<\/a>, published on Monday in the journal Nature Medicine<\/i>, which assessed the diagnostic accuracy of brain tumor image classification using machine learning vs. pathologist interpretation of conventional histologic images.Researchers discovered that the accuracy of the AI-based diagnosis was comparable to the pathologist-based interpretation (94.6 percent and 93.9 percent, respectively).Convolutional neural networks (CNNs) \u201clearned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors,\u201d according to the study\u2019s authors. In addition to CNNs, the study leveraged stimulated Raman histology (SRH), an advanced optical imaging technique that reveals tumor infiltration in human tissue by collecting scattered laser light and illuminating essential features not typically seen in standard histologic images.\u201cWe implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images,\u201d contend the authors. \u201cThese results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.\u201dCNNs were trained using more than 2.5 million SRH images and predicted brain tumor diagnosis in the operating room in under two and a half minutes\u2014an order of magnitude faster than conventional techniques.\u201cAs surgeons, we\u2019re limited to acting on what we can see; this technology allows us to see what would otherwise be invisible, to improve speed and accuracy in the OR and reduce the risk of misdiagnosis,\u201d says senior author Daniel Orringer, MD, associate professor in the Department of Neurosurgery at NYU Langone, who helped develop SRH. \u201cWith this imaging technology, cancer operations are safer and more effective than ever before.\u201d\u201cSRH will revolutionize the field of neuropathology by improving decision-making during surgery and providing expert-level assessment in the hospitals where trained neuropathologists are not available,\u201d adds co-author Matija Snuderl, MD, associate professor in the Department of Pathology at NYU Langone.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

AI, imaging system able to accurately identify brain tumors

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