Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

A large international collaboration, led by A/Prof Xiu Ying Wang and Prof Manuel Graeber of the University of Sydney, has developed an innovative, advanced artificial intelligence (AI) application, PathoFusion, that could be used for the examination of routine tissue samples in order to identify indications of cancer. The research melds contributions from computer scientists, neuropathologists, neuosurgeons, medical oncologists and medical imaging scientists. ANSTO's Prof Richard Banati, a Professor of Medical Radiation Sciences/Medical Imaging, who studies the brain's innate immune system using advanced medical imaging techniques, is a co-author on the paper published in the journal, Cancers. "The idea behind PathoFusion was to create a novel advanced deep learning model to recognize malignant features and immune response markers, independent of human intervention, and map them simultaneously in a digital image," explained Banati. Scientists specifically designed a bifocal deep learning framework which is analogous to how a microscopist works in histopathology image analysis.

A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].

Artificial Intelligence (AI) based on a combination of deep-learning algorithms and laser-imaging technology can be utilised to examine brain tissue and detect a brain tumour in near real-time according to a study published in Nature Medicine Journal on Monday. This recent AI technique can be a game-changer in intra-operative brain tumour diagnostics according to reports. The method is a combination of "Raman histology (SRH), a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion," the study said. The AI method is also much faster. The neural networks have been trained using over 2.5 million SRH images to identify brain tumours using brain tissue in under 150 seconds, according to the report.

Analyzing biopsied tissue samples for signs of malignant growth is a crucial part of cancer diagnosis. But many places around the country suffer from a lack of neuropathologists, which prevents these fundamental procedures from happening in a timely manner. Recent studies have warned of a "pathologist gap" which may grow through 2030. Scientists have demonstrated an A.I. not only capable of completing such procedures in less than three minutes, but can do so more accurately than a human. Of the 15.2 million people around the world diagnosed annually with some form of cancer, nearly 80 percent will undergo biopsy surgery to remove and test a piece of the tumor. In the case of brain tumors, which this new study focused on, the testing process can take up to 30 minutes (if there's a neuropathologist on hand to conduct the test) and requires time and labor-intensive freezing, thawing and chemical staining of samples in order to make a diagnosis.

Expert human pathologists typically require around 30 minutes to diagnose brain tumors from tissue samples extracted during surgery. A new artificially intelligent system can do it in less than 150 seconds--and it does so more accurately than its human counterparts. New research published today in Nature Medicine describes a novel diagnostic technique that leverages the power of artificial intelligence with an advanced optical imaging technique. The system can perform rapid and accurate diagnoses of brain tumors in practically real time, while the patient is still on the operating table. In tests, the AI made diagnoses that were slightly more accurate than those made by human pathologists and in a fraction of the time.

A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors, a new study finds. Published in Nature Medicine on January 6, the study examined the diagnostic accuracy of brain tumor image classification through machine learning, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation. The imaging technique, stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images. The microscopic images are then processed and analyzed with artificial intelligence, and in under two and a half minutes, surgeons are able to see a predicted brain tumor diagnosis.

Published in Nature Medicine on January 6, the study examined the diagnostic accuracy of brain tumor image classification through machine learning, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation. The imaging technique, stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images. The microscopic images are then processed and analyzed with artificial intelligence, and in under two and a half minutes, surgeons are able to see a predicted brain tumor diagnosis. Using the same technology, after the resection, they are able to accurately detect and remove otherwise undetectable tumor.

Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another, and do it very quickly. The findings, published May 15 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist. The tool allows them to analyze thousands of times more data and ask new questions that would not be possible with the limited data processing capabilities of even the most highly trained human experts. "We still need the pathologist," said Brittany N. Dugger, PhD, an assistant professor in the UC Davis Department of Pathology and Laboratory Medicine at UC Davis and lead author of the study.

The new AI algorithm was able to efficiently automate classifying amyloid plaques and blood vessel abnormalities in postmortem brains of Alzheimer's patients. Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue, delivering a proof of concept for a machine-learning approach capable of automating a key component of Alzheimer's research. Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another -- and do it very quickly. The findings, published May 15, 2019 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist.