A network established by the National Pathology Imaging Co-operative (NPIC) covering 15 hospitals and a population of six million people, makes an ambitious move to advance digital pathology in the North of England.
A new way of using artificial intelligence to predict cancer from patient data without putting personal information at risk has been developed by a team including University of Leeds medical scientists.
Tribvn Healthcare) is pleased to announce the company’s expansion into the pharmaceutical market with the appointment of Mr. Loic Dartois as Vice President Pharma.
Paige, a global leader in AI applications in pathology, today announced several additions to its Scientific Advisory Board as it enters its next phase of growth across its clinical and life sciences businesses.
For many of us, Google Maps has become an indispensable daily tool: We pop open a web browser and instantly access a powerful map where we can get directions, zoom in and pan, overlay features like traffic and terrain, and search for specific landmarks. But what if there were a tool with similar functionality to explore an entirely different kind of map? What if we had a Magic School Bus-esque ability to dive deep into the details of tissues that make up the human body?
The use of artificial intelligence (AI) to diagnose cancer and distinguish prostate cancers from benign tumors has been extensively investigated. However, existing deep learning methods for diagnosing prostate cancer are based on magnetic resonance imaging (MRI) data alone, and diagnosis relies on ground truth data determined by radiologists. Therefore, current deep learning algorithms provide limited accuracy in [...]
The recent news that digital pathology pioneer Proscia and the multi-billion-dollar healthcare giant Siemens Healthineers had inked a multi-year OEM agreement certainly raised industry eyebrows. For Siemens, the deal marks its first foray into digital pathology, with Proscia filling what is arguably a rather prominent hole in the German vendors diagnostic portfolio. For casual onlookers, it also signals growing acceptance for digital pathology in the clinical market, which has been famously slower than anticipated to adopt digitilisation since whole slide imaging (WSI) scanners first became available over twenty years ago.
Learn how to implement the HALO Prostate AI tool for prostate cancer detection and grading.
Deep Bio Presents Research Results of AI-based Cancer Diagnosis and Prognosis at the AACR Annual Meeting 2022Five abstracts in prostate and breast cancers, Deep Bio’s main research areas, are presented during the online sessions.SEOUL, SOUTH KOREA (PRWEB) APRIL 11, 2022Deep Bio, a pioneer in medical AI for pathologic cancer diagnostics, presented five abstracts in regards to cancer diagnosis and prognosis of multiple cancer types using deep learning-based algorithms at the American Association for Cancer Research (AACR) 2022 which took place both in-person and virtually in New Orleans.These studies showed notable findings when the algorithms were used for prognosis, including recurrence risk prediction and survival analysis. Particularly, a study that aimed to find out the relationship between histomorphological features of adenocarcinoma lesions and patients’ survival using a deep learning model was highlighted, drawing attendees’ attention. In the study, the deep learning-based pancreatic adenocarcinoma survival model discovered that histomorphological features show correlations with patient survival. Also, the study brought into light the possibility of applying the survival analysis to other diseases such as rectum adenocarcinoma and breast adenocarcinoma. In another study, Deep Bio’s breast cancer survival analysis model was able to distinguish between the low-risk group and the high-risk group to some extent, suggesting it can be used as a screening tool prior to the 21-gene test which classifies patients in early-stage breast cancer for whom chemotherapy can be effective.Deep Bio also shared meaningful study results in prostate cancer, its core research area. In the study, researchers sought to detect cancerous regions and grade their severity on a whole slide image (WSI) of prostate frozen sections with deep learning-based cancer diagnosis support software. The model demonstrated high performance, achieving 98%in sensitivity for detecting malignant cases and 99% sensitivity for detecting clinically significant risk cases*.Tae-Yeong Kwak, the CTO of Deep Bio, said, “Prognosis analysis of cancer is crucial in that it directly affects patients’ treatment and care plans. The studies presented imply that deep learning algorithms can be a great help to predict the prognosis of cancer more precisely. Deep bio will continue its efforts to make these technologies for real clinical settings.”“By presenting significant research results at this year’s AACR, Deep Bio not only showcased its commitment to AI-based cancer diagnosis and digital pathology but also expanded its presence globally. With the proven and tested technologies, we will lead digital pathology and AI through close partnerships with clinics, research institutes, and digital pathology platform providers,” said Sun Woo Kim, the CEO of Deep Bio.Deep Bio continues to focus on not only AI diagnostics but also R&D and presenting the results at various international conferences and events. The company also continues to build its presence in the global market through overseas digital pathology solution providers in the US, Europe, and India, as well as conducting research cooperation with Stanford Medical School, Harvard Dana-Farber Cancer Center, and other top research institutions in the US.Abstracts Presented Included (E-Posters):Breast cancer survival analysis with the size of the infiltrative cancer areaAutomated Gleason grading of digitized frozen section prostate tissue slide imagesRecurrence risk prediction based on automatic histopathologic analysis of breast cancer using whole slide imagesA deep learning based pancreatic adenocarcinoma survival prediction model applicable to adenocarcinoma of other organsMolecular mapping of prostate cancer on whole mount prostatectomy specimens using deep neural networks to quantify genotypic heterogeneity(* clinically significant risk cases: Grade group 2 or over)About Deep BioDeep Bio Inc. is an AI biotech company with in-house expertise in deep learning, pathology, life sciences, and pharmacotherapeutics. As the country’s first to obtain Korea’s MFDS (Ministry of Food and Drug Safety) approval of an AI-based cancer diagnostic support solution, Deep Bio envisions a suite of AI-based IVD SaMDs (In Vitro Diagnostics Software as a Medical Device) for diagnosis and prognosis of multiple cancers. Deep Bio is actively engaged in the research space and participating in ongoing collaborations with top US medical centers. To learn more, visit http://www.deepbio.co.kr.“DeepDx® Prostate is a clinically-validated AI for prostate core needle biopsy tissue image analysis. Whole-slide images (WSIs) of H&E-stained biopsy tissue specimens are analyzed for prostate cancer, Gleason scores, and grade groups. Extensively tested at US CLIA labs (> 500k cores in 2021), DeepDx® Prostate can alleviate the shortage of pathologists and the resultant increase in workload, while reducing diagnostic subjectivity and variability.” To learn more, visit http://www.deepbio.co.kr.Source: Cision PRWeb
Imaging technologies have become a key aspect of the pharmaceutical research & development process to understand the complexity of biological events happening within tissue. The growth of spatial-omics is giving access to transcriptomic, proteomic and metabolomic data in a single tissue slice. At AstraZeneca, we’re integrating those data with gold standard histological methods to generate a holistic understanding of drug action using Mass Spectrometry Imaging (MSI) and Imaging Mass Cytometry (IMC). The integration of both technologies is playing a pivotal role in modern drug development by providing Pharmacokinetics (PK), Pharmacodynamics (PD), as well as safety and target engagement (TE) information that are crucial for decision making. Automated image analysis has been transformative in digital pathology as it has enabled robust workflows to extract data from images in a time efficient manner. The advent of Deep Learning tools for image segmentation has allowed tissues to be analysed in greater depth and precision. The tools in the HALO® software have enabled the team to segment regions, transfer ROI annotations, phenotype cells and relate this back to the MSI data.
