Micropix launches their latest microscope – the Micropix UM23. An upright microscope designed specifically for Histopathology Consultants. It has all the features and qualities of an equivalent Nikon or Olympus microscope but at HALF the price. This whole package is available now for just £6,950 (plus VAT).
Machine learning is not a new technology, but it started to revolutionize pathology relatively recently. The ideal combination of untapped, abundant pathology data necessary to leverage machine learning and the relevance of pathology applications has drawn scientists to this field and caused an artificial intelligence (AI) explosion.
It’s quite possible that in the future we will be able to use whole block imaging in the same way that we use whole slide imaging today.
To become a histopathologist, I first went to medical school and then worked as a junior doctor in several different specialties before deciding to apply to the Histopathology training programme.
Artificial Intelligence (AI) has been proven to be a faster and more effective approach to identifying and assessing pathological characteristics in samples than previous techniques. The diagnosis process can be accelerated and strengthened by implementing AI. In addition, AI aids pathologists in making accurate diagnoses by using data to verify their findings. It can alert them when their conclusions are contrary to what is expected based on the algorithms. By combining AI with digital pathology as a validation tool, pathologists can now perform image analytics on a more significant number of slides in a shorter period. Pathologists can improve outcomes by focusing on specific regions and improving efficiency accordingly. Digital pathology is also enhancing patient engagement with the use of AI, with devices and apps providing access to electronic health records, radiology images, etc. The global AI-based digital pathology solutions market is expected to grow at a CAGR of 7.67% during the forecast period 2022-30, driven by the increasing prevalence of diseases, significance of digital pathology for the pharmaceutical industry, and approval by regulatory agencies for the use of digital pathology in primary diagnosis. New business models, such as on-demand services, will play a pivotal role in gaining access to untapped markets and increasing the widespread adoption of digital pathology over the forecast period. Moreover, with digital solutions being highly expensive, solution providers are taking advantage of this problem by setting up regional service centers that will serve the needs of diagnostic labs in the emerging markets through provision of slide scanning and image analysis services and image storage services. These are the latest findings of InsightAce Analytic (Jersey City, NJ, USA), a market research and consulting firm. With the advent of computational pathology, digital pathology will create opportunities for developing AI-based diagnostic tools that will make it possible to make more precise and reproducible diagnoses. Pattern recognition and generation of perception are essential features of AI. As a result, the integration of AI into digital pathology can provide additional morphological information and features. Digital pathology and AI development are both accelerating at a fast pace, leading to increased interest in computational pathology. AI-based solutions have already been reported to show promising results in detecting prostate cancer. The launch of new advanced devices by medical device companies is also expected to be responsible for an increase in research into the abilities of AI-based digital pathology as a diagnostic tool for cancer, which in turn will lead to the market's expansion. During the forecast period, digital pathology solutions will continue to drive the growth in market revenue due to their increasing use in drug development and biomarker identification. Nevertheless, the lack of computational analytical tools and high capital investment required to expand internationally will limit market growth. Additionally, the slow rate of technological advancement could hinder the growth of the AI-based digital pathology solutions market. North America accounts for the largest share of the global market for AI-based digital pathology solutions due to an improvement in healthcare infrastructure, rise in per capita income, and the presence of advanced research institutes and laboratories in the region. The North American market is also being fueled by the continued deployment of R&D investments, implementation of digital imaging, supportive government initiatives, and presence of prominent companies in the region. Another factor contributing to the growth of the North American market is the approval of a digital pathology-focused AI-based tool designed to detect invasive breast cancer and skin lesions for use by AI-based, digital pathology-focused companies. Related Links:InsightAce Analytic
Roche today announced the appointment of Brad Moore as president and CEO of Roche Diagnostics North America, effective January 1, 2023. Moore currently serves as senior vice president, Core Lab and Point of Care, within Roche Diagnostics North America. Moore will succeed and report to Matt Sause, who was recently appointed CEO of Roche Diagnostics globally, effective January 1, 2023. The position will continue to be based at Roche Diagnostics’ North American headquarters in Indianapolis.“Brad has a proven track record of delivering outstanding results and is a highly effective leader,” said Matt Sause, current president and CEO of Roche Diagnostics North America and incoming CEO of Roche Diagnostics. “I’m confident his experience will be a tremendous asset for the North America team, our customers and our patients.”Moore joined Roche in October 2016 as Head of North America Diabetes Care, leading commercial operations for the U.S. and Canada. In 2019, the role expanded to include the broader scope of Europe and North American commercial operations in mature markets in Europe, the U.S., Canada, Australia and New Zealand within Roche Diabetes Care. In 2020, Moore assumed his current position, leading the newly-created Core Lab and Point of Care commercial business unit.About RocheFounded in 1896 in Basel, Switzerland, as one of the first industrial manufacturers of branded medicines, Roche has grown into the world’s largest biotechnology company and the global leader in in-vitro diagnostics. The company pursues scientific excellence to discover and develop medicines and diagnostics for improving and saving the lives of people around the world. We are a pioneer in personalized healthcare and want to further transform how healthcare is delivered to have an even greater impact. To provide the best care for each person we partner with many stakeholders and combine our strengths in Diagnostics and Pharma with data insights from the clinical practice.In recognizing our endeavor to pursue a long-term perspective in all we do, Roche has been named one of the most sustainable companies in the pharmaceuticals industry by the Dow Jones Sustainability Indices for the thirteenth consecutive year. This distinction also reflects our efforts to improve access to healthcare together with local partners in every country we work.Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan.For more information, please visit www.roche.com.
One in nine women in the developed world will be diagnosed with breast cancer at some point in her life. The prevalence of breast cancer is increasing, an effect caused in part by the modern lifestyle and increased life spans. Thankfully, treatments are becoming more efficient and more personalized. However, what isn’t increasing – and is in fact decreasing – is the number of pathologists, or the doctors whose specialization is examining body tissues to provide the specific diagnosis necessary for personalized medicine. Now, a team of researchers have made it their quest to turn computers into effective pathologists’ assistants, simplifying and improving the human doctor’s work. The specific task that researchers at the Technion – Israel Institute of Technology (Haifa, Israel) have set out to achieve lies within the realm of immunotherapy. Immunotherapy has been gaining prominence in recent years as an effective, sometimes even game-changing, treatment for several types of cancer. The basis of this form of therapy is encouraging the body’s own immune system to attack the tumor. However, such therapy needs to be personalized as the correct medication must be administered to the patients who stand to benefit from it based on the specific characteristics of the tumor. Multiple natural mechanisms prevent our immune systems from attacking our own bodies. These mechanisms are often exploited by cancer tumors to evade the immune system. One such mechanism is related to the PD-L1 protein – some tumors display it, and it acts as a sort of password by erroneously convincing the immune system that the cancer should not be attacked. Specific immunotherapy for PD-L1 can persuade the immune system to ignore this particular password, but of course would only be effective when the tumor expresses the PD-L1. It is a pathologist’s task to determine whether a patient’s tumor expresses PD-L1. Expensive chemical markers are used to stain a biopsy taken from the tumor in order to obtain the answer. The process is non-trivial, time-consuming, and at times inconsistent. The Technion researchers took a different approach. In recent years, it has become an FDA-approved practice for biopsies to be scanned so they can be used for digital pathological analysis. The researchers decided to see if a neural network could use these scans to make the diagnosis without requiring additional processes. Neural networks are trained in a manner similar to how children learn: they are presented with multiple tagged examples. A child is shown many dogs and various other things, and from these examples forms an idea of what “dog” is. The neural network developed by the Technion researchers was presented with digital biopsy images from 3,376 patients that were tagged as either expressing or not expressing PD-L1. After preliminary validation, it was asked to determine whether additional clinical trial biopsy images from 275 patients were positive or negative for PD-L1. It performed better than expected: for 70% of the patients, it was able to confidently and correctly determine the answer. For the remaining 30% of the patients, the program could not find the visual patterns that would enable it to decide one way or the other. Interestingly, in the cases where the artificial intelligence (AI) disagreed with the human pathologist’s determination, a second test proved the AI to be right. “This is a momentous achievement,” explained Professor Ron Kimmel from the Henry and Marilyn Taub Faculty of Computer Science at the Technion. “The variations that the computer found – they are not distinguishable to the human eye. Cells arrange themselves differently if they present PD-L1 or not, but the differences are so small that even a trained pathologist can’t confidently identify them. Now our neural network can.” “We expect AI to become a powerful tool in doctors’ hands,” added Prof. Kimmel. “AI can assist in making or verifying a diagnosis, it can help match the treatment to the individual patient, it can offer a prognosis. I do not think it can, or should, replace the human doctor. But it can make some elements of doctors’ work simpler, faster, and more precise.” Related Links:Technion
Aiforia stands for artificial intelligence (AI) for image analysis (IA) and it combines cloud-based access with supervised deep learning for pathology image analysis. This is where pathologists and computer scientists collaborate closely to create tools that empower pathologists and give them access to the state-of-the-art image analysis methods.
The flagship Pannoramic 1000 RX joins a range known for its “unprecedented capacity, superior image quality and flexibility”, and is the only one in the UK with water immersion.
What we have to learn from day one when we design these AI applications, is that pathology has to come with us. We cannot just design a network as computer scientists and then go to the pathologists just when we need to validate it. The pathologist has to be with us from the start.
