This episode contains affiliate links. Learn what that means hereHave you ever tried to take a picture through your microscope with your smartphone? If you have, you know how much hassle it is to consistently take good, sharp microscope pictures. So maybe, annoyed with how cumbersome it is and how much time it takes, you have already looked for a microscope phone adapter? I know I have, and the one I got from Amazon quickly ended up in my drawer and never saw the light of day again. The pictures were no better than with the handheld phone and it took forever to mount that thing.Disappointed with my Amazon experience I gave up on finding a microscope phone adapter, thinking a proper microscope camera was the only way to go. Then I saw a comment by Skoped Micro on one of my Instagram posts. This microscope phone adapter looked different, and it even featured a dedicated app to take pictures.This is how I met Cade Wilson, a practicing veterinary surgeon from Oklahoma, who developed this unique microscope phone adapter together with the outdoor company Phone Scope, originally modifying it from a phone adapter designed for a hunting spotting scope. Fast forward 5 years and this microscope phone adapter kit consisting of a Custom Phone Case and Microscope Eyepiece Adapter is ready for purchase and anyone who wishes to do digital pathology can do it through their phone with ease, without having to disrupt their workflow or having to spend thousands of dollars for a slide scanner. Listen to the full episode to learn more about how Skoped Micro brings digital pathology to everyone, including veterinary practices, universities, and all other microscope users. So if you need to start doing digital pathology, telepathology, teleteaching, or just want to take beautiful pictures through your microscope for your Instagram or other social media feed, without breaking the bank, look no more!Digital Pathology Place is a proud affiliate of Skoped Micro and you can purchase the digital pathology kit for your phone (consisting of Custom Phone Case and Microscope Eyepiece Adapter) through our affiliate link: Buy the digital pathology kit for your phone hereThank you! This episode’s resources:Skoped Micro website with all the necessary resources and YouTube videos (affiliate link)Dr. Cade Wilson’s Veterinary Hospital in Ardmore, OK

Digital Pathology Podcast

Microscope phone adapter that can bring digital pathology to everyone w/ Cade Wilson, Skoped Micro

How many times did you get annoyed when using non-intuitive digital pathology software? Have you already given up on digital pathology and image analysis or are you still looking for something powerful but easy to use? Today’s guest, Tuomas Ropponen, the chief technology officer at Aiforia, is talking about the creation of a pathology image analysis platform whose core principles are “easy to use” and “accessible” - Aiforia. 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.  Aiforia began as a teaching and telepathology platform for sharing whole slide images. But as soon as deep learning passed the reality check and started outperforming classical computer vision methods, Aiforia’s team knew that they had to incorporate this method into their platform. It would change the way pathology was done.  The decision about using supervised deep learning as the method of choice was based on the desire to supervise the teaching of the AI in a similar way as we supervise the teaching of students. Only by supervising and curating the inputs can we be sure that we get quality output. Teaching AI in a supervised manner happens through annotations, and annotations are a natural way that pathologists communicate. Pathologists have always marked areas of interest on the glass and later digital slides. It has always been the way to show others what is important on the tissue. Adapting annotations for supervised deep learning as a way of showing AI what is important was a natural progression of how pathologists work.  Another core value at Aiforia is the close collaboration between pathologists and computer scientists. Those two groups currently work very closely together but fostering this open relationship and honest communication required a few iterations and a deeper understanding of each other’s ways of working.  For computer scientists, it was surprising that the pathology scoring and grading system often could not be directly reproduced by image analysis algorithms. For pathologists it was surprising that the algorithm results did not match their visual estimates from glass slides. The two groups had to sit together and start dissecting the pathology problems into smaller components as well as translating them into quantifiable tasks.  Suddenly it became clear to everyone that the Ki67 quantification in the tumor consists of first detecting the tumor epithelium and later identifying and counting the Ki67 positive and Ki67 negative cells within the epithelium. When pathologists’ fatty liver scores of 70 or 80% were nowhere close to the absolute pixel area of fatty vacuoles in the liver tissue of max 20% it became evident that pathologists were subconsciously normalizing their scores and spreading them on a 0-100% scale. Coming together and analyzing the discrepancies as a team revealed that pathologists’ scores or estimates are often an imprecise and inconsistent benchmark to measure against. Everyone went back to the drawing board (or drawing tablet) and provided a more objective ground truth – annotations.  This close collaboration of pathologists and computer scientists as well as involvement of user experience designers helps drive innovation at Aiforia while maintaining accessibility and ease of use. To learn more about AI for image analysis visit Aiforia’s website or even better, let the team show you what this whole thing is about - book a demo.  

An easy AI tool for pathology image analysis. How Aiforia empowers pathologists and scientists with supervised deep learning w/ Tuomas Ropponen, Aiforia

With more than 130K total downloads and over 500 citations in scientific literature QuPath is arguably the most popular open-source software for quantitative pathology and bioimage analysis. Today’s podcast guest, Pete Bankhead, the author of QuPath, is taking us behind the scenes of his software creation.Even though Pete is now a senior lecturer in digital pathology at the University of Edinburgh, his digital pathology career actually started by accident. With an undergraduate degree in theology and a master’s in computer science, he started working on bioimage analysis during his PhD in biomedical sciences. He began using open-source software for image analysis, which was an excellent and very efficient way to work with static bioimages. So, during his post doc work he tried to apply open-source software to pathology whole slide images (WSI), unfortunately without success…The pathology WSI were just too big - it was not even possible to efficiently open them with any openly available software. So he started his own software development – first by creating his own plugins for already available open-source programs, such as ImageJ. It sort of worked but not really… There was no way to coordinate the development and bring all his plugins together, so he started developing his own pathology WSI viewer. That worked, and in the process, he realized that building software tools himself gave him a lot more freedom to solve problems in a way tailored to the specific challenges of digital pathology. He dove deeper into the project and created what we now know as QuPath – the open-source software for digital pathology image analysis. During its development, the software evolved from a Ki67 quantification tool to a machine learning-powered, versatile image analysis software.Listen to the full episode to learn Who and what influenced Pete, why is QuPath open-source,why Pete fought for QuPath to stay open-source, and what are the most important features of QuPath. This episode’s resources:The real background story to QuPath on TwitterDigitizing a photo album with QuPathQuPath docs QuPath annotation tweetorial  QuPath YouTube channel QuPath user forum (also for other software) QuPath immunofluorescence multiplex support StarDist cell detection algorithm (deep learning-based) QuPath 2020 workshop “From Samples to Knowledge”Cite this paper if you are using QuPath for researchMost popular free open-source software programs for image analysis of pathology slides HistoQC, an open-source way to control the quality of pathology images 

QuPath - open source quantitative pathology not only for pathologists w/ Pete Bankhead, University of Edinburgh

This episode is brought to you by VisiopharmCancer immunotherapy, aka immuno-oncology, is tapping into the power of our own immune system to fight cancer. There are multiple processes and immune cell populations involved in tumor immunology and digital pathology and whole slide imaging has allowed scientists to leverage the power of tissue image analysis to detect and quantify them.Today’s podcast guest, Prof. Elfriede Noessner will walk us through the complexities of the immune system, explain how it is being influenced to cure cancer and what role tissue image analysis plays in the process. In the course of her research she studied the following processes relevant for immuno-oncology together with the cells responsible for them:·       Killing: T-cells·       Removing the debris of the tumor: macrophages·       Antigen presentation: dendritic cells·       Antibody production: B-cells·       Immune response regulation: regulatory T cells and checkpoints: CTLA4 and PD1/PDL1Killing is crucial for destroying the tumor, but without removing the debris of the killed cells the surrounding tissue will suffer. Antigen presentation enables the immune system to see the enemy. If we do not have antigen presentation, the T-cells responsible for the killing process are blind.Antibody production is an upcoming research area of immuno-oncology however the processes are not well understood yet. Regulation of the communication – stopping the immune response, prevents overshooting. In the body’s fight against the tumor, this regulation is coming too early, it stops the T-cells before killing all the tumor cells. This is detrimental in tumor oncology. The stopping proteins are called checkpoints (e.g., CTLA4, PD1/PDL1) and to keep the T-cell attack going on, they need to be inhibited. Seeing is believing, so visualizing the cells and checkpoints has a great convincing effect for the scientific community, but it is also crucial for evaluating the effectiveness of the immune system. It makes it possible to see if the immune cells are in the right location and in the right number. A great example of this proving that the numbers of T-cells in the tumor influence the patient prognosis more than the classical pathology grading is the Immunoscore® of colon cancer. Quantification with image analysis of the numbers and locations of T-cells in colon cancer patients changed the way colon cancer therapy was approached. This was a breakthrough proving that T-cells can matter. However, they do not work all the time and the task of the scientists is to figure out how to activate them. We need to understand what regulates the T-cells – one aspect is the checkpoints the other part is the regulatory cells. If the regulatory cells are close to the T-killer cells, the killer cells are inhibited. This can be only evaluated in an image – the proximity of the different cell types matters, and image analysis is the only way to accurately determine this information. The immune system is a complex entity, but in order to fight cancer, we need to understand and learn to influence it. Digital pathology and image analysis have become indispensable tools in this mission.  

Immuno-oncology 101 w/ Elfirede Nößner, HelmholzZentrum, Visiopharm advisor

Are you looking for a whole slide scanner for your digital pathology projects? In this podcast episode Doug Stapleton, the Service Manager of the digital slide scanner division of Hamamatsu, is guiding us through this process.  He is listing 10 questions you need to ask before purchasing a whole slide scanner tailored to your digital pathology needs.Question 1: What is your budget and throughput?Question 2: What is your intended use now and in the future? Brightfield, immunofluorescence or both?Question 3: What is the cross-organizational demand for scanning services?Question 4: How much space do you need in the lab for your scanner?Question 5: What size of pathology slides do you want to scan? Standard size or non-standard?Question 6: What magnification will you be scanning at? 20x, 40x or other?Question 7: Is it just the whole slide scanner or does it come with additional equipment?Question 9: How easy is it to scan the slides? How many times do you need to click?Question 10: How does the whole slide scanner integrate with other systems in your lab?Bonus Question 11: Are any extras included in the package?              Is there a special functionality that you are interested in, such as: -          telepathology capabilities-          virtual slide conferences-          slide annotations -          or cloud storage options?Hamamatsu offers all of these functions which makes them a potential one-stop-shop for all your digital pathology needs. Answer the questions above and you will gain clarity on what you really need. Equipped with this information you can start “scanner shopping.”Just remember, no matter which scanner you choose, always dry and clean your slides before scanning!Listen to the full episode to gain more insights or read the blog post based on this podcast episode. This episode’s resources:Hamamatsu digital pathology slide scannersDoug Stapleton LinkedIn profileHow to choose a whole slide scanner for digital pathology – blog post mini guide - coming soon!How to choose a whole slide scanner for digital pathology – downloadable questionnaire - coming soon!

How to choose a digital slide scanner w/ Doug Stapleton, Hamamatsu

Have you ever wondered if the pathology turn-around time could be faster and came to the conclusion that the only way to achieve that is to skip the tissue sectioning and staining? Now it is possible, with optical scanning microscopy by Instapath. Today’s guest, David Tulman, is the chief clinical officer of Instapath, a small startup using optical scanning microscopy to image fresh tissue without fixing and staining it. Working as a clinical trial manager exposed David to different areas of medicine, so he decided to dive deeper and get a Ph.D. However, pipetting for 12 hours a day for 5 years of the program to do basic research did not sound attractive…so he found a different program – a Ph.D. in bioinnovation. As a result of this program not only did he get his Ph.D. degree but also co-founded a biomedical startup company – Instapath, whose mission is to deliver pathology diagnoses to patients faster – while they are still on the operating table.To achieve that, Instapath uses optical scanning microscopy. This cutting-edge technology can scan a piece of fresh, un-sliced tissue and virtually generate an image resembling a 5 um thick H&E stained section. This can be achieved by staining the tissue with fluorescent dies, one of them being eosin itself, which happens to be fluorescent. The dies are water-based and enable the generation of a high-resolution pseudo H&E image. Instapath focuses on the speed of the pathology evaluation. For an 18-gauge biopsy the time from tissue removal from the body, through fluorescent staining, image processing, and upload to the image viewer is between two and three minutes. This is amazing compared to the traditional process that takes several days, or even frozen sections that should take around  20 minutes. Current applications of Instapath’s technology and system include: -          sample screening for biobanking,  -          alternative to fluorescent and confocal microscopy,And in the near future, following the results of ongoing validation studies the company thinks there is a good chance that -          optical scanning microscopy could replace frozen section evaluation.    To learn more about Instapath visit: https://www.instapathbio.com/Ps. David was a great guest of this episode but he is also a podcast host himself. Together with Giovanni Lujan, they co-host the “Beyond the Scope” podcast by Digital Pathology Association.   This episode’s resources:Publications about optical scanning microscopy co-authored by the Instapath team:       https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5553202/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592466/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082869/Beyond the Scope podcasthttps://digitalpathologyassociation.org/dpa-podcast-beyond-the-scopeDigital pathology crash course:https://www.subscribepage.com/digital_pathology_crash_course 

Virtual H&E. How Instapath uses optical sectioning microscopy to accelerate pathology diagnosis w/ David Tulman, Instapath

This episode is brought to you by Visiopharm.In this third and last episode of the multiplex mini-series with Regan Baird from Visiopharm we look at the considerations when choosing an image analysis software for phenotyping.The two main points to consider when choosing phenotyping image analysis software are segmentation assistance and data visualization. Segmentation assistance:Before different markers are attributed to different cells in the tissue and cell phenotypes are determined, cell boundaries need to be delineated. The automatic delineation of these boundaries by image analysis software is called cell segmentation. Cells in tissue slides can have different shapes and sizes, which depend on the plane of sectioning, heterogeneity of the investigated tissue, and the disease stage. This makes the task of segmentation challenging. Unlike in single-cell confocal microscopy images, where the cell borders are very well-demarcated, in tissue they often need to be estimated. A separate segmentation (e.g., membrane) marker can help significantly, but a perfect cell segmentation is not attainable.To best estimate the cell boundaries, rule-based classical computer vision approaches or artificial intelligence (AI) – powered approaches can be used. In rule-based approaches, we are working with well-defined features on which the segmentation is based, but we need to make concessions. The AI-powered models are only as good as the examples we train the models on. To combine the advantages of both, Visiopharm offers an AI-based nuclear segmentation as the starting point and a rule-based and marker-based second step to obtain the most reliable cell segmentation for phenotyping. Data visualization:The adequate visualization and handling of the obtained data depend on the software used. To understand and interpret the multidimensional multiplex and phenotyping data we need to interpret graphs, plots, two-dimensional reduction plots, and other data visualizations for all the images in multiplex studies. In order to evaluate how well the phenotyping has performed and to export meaningful results, the correct visualization tools need to be used.If you need assistance or have questions about multiplexing and phenotyping visit the Visiopharm’s website and contact the Visiopharm team. This episode’s resources:Multiplexing mini-series Part 1: Introduction to multiplex for tissue image analysis (part 1) w/ Regan Baird, VisiopharmMultiplexing mini-series Part 2: How to make sense of multiplex data with phenotyping? (part 2) w/ Regan Baird

What to consider when choosing an image analysis solution for phenotyping? (part 3) w/ Regan Baird, Visiopharm

This episode is brought to you by Visiopharm.Multiplex tissue staining can generate large amounts of data to help identify distinct information about particular cells in tissue. Immuno-oncology is a field where it is common practice to use multiplexing, in particular for cell phenotyping in tissue.Phenotyping is the ability to classify every individual cell in the tissue based on the biomarker panel used. The panels are designed to identify cells of different lineages as well as cell activation states within each lineage, which is of utmost importance for the personalized therapeutic approaches in oncology. Although multiplex data can be visualized manually, e.g., by switching on and off different fluorescence channels, its interpretation requires computational assistance. If the multiplex assay only contains a few markers, the rules for detecting potential phenotypes can be designed manually, but as the number of markers increases the number of potential phenotypes increases exponentially. In order to sort through the cellular phenotypes in higher-plexes, machine learning-based auto clustering has been implemented. This method is based on the way cells are characterized in flow cytometry and has been adapted to automatically identify phenotypes of cells in tissue images. The adequate visualization and handling of the generated data depend on the software used. In the next episode, we will be talking about the considerations when choosing an image analysis software program for phenotyping. To learn more visit Visiopharm’s websiteThis episode’s resources:Multiplexing mini-series Part 1: Introduction to multiplex for tissue image analysis (part 1) w/ Regan Baird, Visiopharm

How to make sense of multiplex data with phenotyping? (part 2) w/ Regan Baird, Visiopharm

Founded in 2011 by this episode’s guest, Lorcan Sherry, along with co-founder John Waller, OracleBio entered the digital pathology market with a unique value proposition – to be a contract research organization for tissue image analysis and help other organizations in their biomarker discovery work. Fast forward 10 years and they evolved from a small service provider into a Good Clinical Practice (GCP) compliant organization supporting clinical trials, but still within the same paradigm of providing tissue image analysis services.Originally using one software package (Definiens), they diversified into others such as HALO and Visiopharm to be able to utilize the best tool for the job as well as match what their clients might be using for their internal image analysis projects. The transition to supporting clinical trials was a bit bumpy as Definiens abruptly discontinued the software license that OracleBio’s business was based on, but the organization quickly pivoted and secured a diverse image analysis toolbox which helped them stay in business. Currently, as image analysis methods are advancing and deep learning plays an important role in solving computer vision problems applied to pathology, OracleBio keeps expanding the toolbox incorporating not only ready-to-use software packages but also programming capabilities. This variety of tools allows them to address a wide range of projects in the most efficient way.For a CRO specializing in tissue image analysis, it is critically important to provide adequate quality control of the image analysis results. This process has been incorporated into the operations from the very beginning. Each project starts with the evaluation of the image quality – are they good enough for image analysis? Is there enough tissue? What about the tissue processing artifacts and the quality of staining? Only images that passed the QC criteria are used for algorithm development. In the next step, pathologists annotate the regions relevant for analysis (e.g., the tumor mass vs. the non-neoplastic tissue present on the slide) and later they provide region and cell annotations as ground truth for comparison with algorithm markups and correlation calculations.Apart from the annotations, pathologists provide educational sessions for the image analysis scientists to increase their knowledge about the problems which are being addressed with image analysis with the various projects. Although OracleBio is supporting projects along the whole drug development pipeline, their recent focus has been on supporting immune-oncology clinical trials, which lead the company on the path to GCP compliance. This was a big effort for the company and went far beyond image analysis quality control and software validation. This change affected the way work is done across the entire company and positioned OracleBio to bring in the image analysis capabilities to support clinical trials. While histologic techniques evolved from simple brightfield chromogenic single marker IHC stains to immunofluorescence-based multiplex, which are difficult to evaluate visually, the image analysis tools for this more complex imaging data lagged in terms of regulatory compliance. OracleBio’s decision to commit to GCP compliance definitely closed this gap. Even though the company’s focus shifted to the clinical part of drug development, OracleBio continues to serve their smaller biotech clients throughout their whole R&D process not only by providing high-quality tissue image analysis but also by partnering with other service providers, such as histology labs, to facilitate their customers’ drug discovery and development journey. To learn more about OracleBio visit https://oraclebio.com/.

OracleBio's path to GCP compliant image analysis clinical trail support w/ Lorcan Sherry, OracleBio

This episode is brought to you by Visiopharm.After experimenting with multidimensional, multimarker, and multicolor single-cell imaging modalities during his postdoc at Beth Israel Deaconess Medical Center in Boston, looking at 2D images of tissue stained just with hematoxylin and eosin (H&E) seemed to him a bit simplistic…and then he was tasked with doing tissue image analysis (IA). When relying just on H&E, IA can be a very challenging task. So, to both simplify it and extract more information from the tissue, multiplex staining can be implemented.  In this three-part episode miniseries Regan Baird, Ph.D., scientific sales manager at Visiopharm introduces us to the concepts of multiplexing and cell phenotyping as well as to image analysis approaches relevant for multiplex data analysis.Multiplexing in the context of life sciences is referred to as taking multiple measurements at the same time on the same specimen. With tissue slides the easiest method of multiplexing is immunohistochemistry (IHC) based virtual multiplexing where consecutive sections of tissue are stained with a single IHC marker and later each slide is imaged and co-registered to simulate the presence of several IHC markers in the tissue of interest. More complicated, but more precise methods allowing for visualizing cellular colocalization of biomarkers include multicolor bright field IHC (visualizing up to five biomarkers per tissue but colocalizing reliably a maximum of only two biomarkers per cell), immunofluorescence (IF) potentially with spectral unmixing, to increase the number of biomarkers per tissue section as well as per cell to nine, and imaging mass cytometry where instead of chromogens or fluorophores heavy metals are used, which increases the number of biomarkers up to 60 in a single section of tissue.All these multiplex modalities have their advantages and disadvantages, and the choice of the appropriate method should be guided by the design of the experiment as well as scientific and/ or diagnostic questions we want to address.For example, currently a widely used application of IF multiplexing is phenotyping cells in the tissue. This not only allows for the characterization of single cells but also lets us interrogate and investigate spatial relationships between different cell populations giving us information about the interactome of different cells and the environment in which they live. To learn more about phenotyping join us for the next episode of the Multiplexing Miniseries next week.This episode’s resources:Multiplexing mini-series Part 2: How to make sense of multiplex data with phenotyping? (part 2) w/ Regan BairdVisiopharmTop 20 Pathology podcasts you must follow in 2021 

Introduction to multiplex for tissue image analysis (part 1) w/ Regan Baird, Visiopharm

As a computer scientist, he knew that to make a real impact with image analysis there were only two areas: military and life sciences. Sylvain Berlemont, the founder of Keen Eye chose life sciences and never looked back. He started consulting for the industry during his biomedical image analysis research work in academia and he quickly saw that regardless of the applications, the questions asked and the problems to be solved were very similar. Patterns started to emerge and the next logical step was to form a service company and offer solutions to those problems.The service company later turned into a product company and today Keen Eye is a software as a service (SaaS) company leveraging artificial intelligence to design customized deep learning image analysis and computational pathology solutions to support the drug development process. KeenEye&apos;s SaaS allows the customers to access powerful computing resources and a user-friendly platform from their own PC. The platform hosts the algorithms and enables their deployment in an easy and scalable way.As KeenEye does not believe in designing "off the shelf" products for the complex image analysis problems of life sciences, the design of the algorithms happens in a customized way and a very close collaboration of pathologists and computer scientists is a key component of the process. Through such collaboration as well as the development of efficient processes and use of transfer learning, the time required to develop high-quality deep learning models was reduced from several months to a few weeks. To learn more, visit Keen Eye&apos;s website.

Accelerating custom design of AI image analysis algorithms for drug development using transfer learning w/ Sylvain Berlemont, Keen Eye

Smartphone, smartwatch, smart TV...internet of things (IoT) and artificial intelligence of things (AIoT) is ubiquitous. But did it already make it into any of the digital pathology devices? Oh yes! There is a smart whole slide scanner out there. In this episode, I am talking with Don Van Dyke, the chief business officer of Bionovation Biotech. Bionovation holds a patent to a potentially revolutionary scanning technology powered by AI. Due to the ability of the scanner to predict the 3D architecture of the scanned tissue in the Z-axis the device is able to dynamically adjust camera focus exactly to the surface of the specimen and scan it ca. 70x faster than the classical whole slide scanners. Not only does it make the scanning faster, but eliminates the necessity of Z-stacking when scanning smears and cytology specimen. What is more, it is now possible to obtain high magnification images (80x and 100x) fast too, which practically removes most of the digital pathology hurdles for hematopathology and cytopathology. To learn more about Bionovation offer visit: http://www.bionovationimc.com/

A new generation of whole slide scanners: faster, smarter, and more flexible w/ Don Van Dyke, Bionovation

Medical tests and procedures can get reimbursed. The basis of the reimbursement are the Current Procedural Terminology (CPT) codes developed by the American Medical Association (AMA). But how can such a code be obtained for digital pathology which is so much more complex than a group of tests or procedures that could be reimbursed on a fee-for-service basis?According to Esther Abels, Visiopharm’s Chief Clinical and Regulatory Officer, to align the digital pathology reimbursement with its value the fee-for-service paradigm needs to shift to a value-based reimbursement strategy. To determine the real value of digital pathology for patient care we need to -          Articulate the services provided and define their added value and uniqueness in patient care (e.g. risk assessment, improvements in responses to therapy, delay in disease progression),-          Gather data relevant to support the claimed added value (e.g. cost-effectiveness data),-          Ensure that the reimbursed fee is based on a combination of technology use and physician involvement,-          And identify the key values relevant for the decision-making stakeholders.Limited work has been done in this area so far, but if we look into the existing care decision-making and treatment patterns and analyze the claims for existing codes in the payers’ databases, we will be able to identify key datasets where digital pathology could make a difference and use this information to start applying for new CPT codes more aligned with digital pathology value. To analyze what steps would need to be taken to prove to the payers that a digital pathology test deserves reimbursement, let us take a tangible example of the Visiopharm’s AI-assisted metastasis detection in Lymph nodes application. This application has a technical, artificial intelligence-based screening component and a pathologist’s reviewing component. Currently in order to assess the presence or absence of cancer metastasis in lymph nodes several (even up to 60) lymph node sections need to be visually evaluated by a pathologist. Finding a metastasis in one of those slides is sufficient to make the diagnosis, but regardless all the other slides need to be reviewed as well. One of the benefits of the AI application would be to save the pathologist’s time, but reducing cost is not the only added value of such an application. The value proposition lies in adding value to patient care. In this case, using a computer algorithm would increase consistency and precision increasing the overall quality of the slide review. AI-aided slide review for metastasis would result in faster turn around not only for the cases where it was used but also for other cases, as the time for visual review could now be used for evaluation of other cases or spending more time on more complex cases again increasing the quality of patient care. Faster diagnosis means faster access to treatment, which often means shorter treatment times.Every time we are able to point out and overcome limitations in the current standard of care with digital pathology applications we have both a legitimate reason to get reimbursed for its use and an incentive to fight for it if we want to make the patients’ lives better. This episode&apos;s resources:“Aligning reimbursement for digital pathology with its value” 

Reimbursement for digital pathology in the clinic – how does that work? w/ Esther Abels, Visiopharm

This episode is brought to you by VisiopharmArtificial Intelligence is starting to cross from pathology research into pathology clinical practice. With several AI-based algorithms approved for clinical use in Europe and many more in the making, it is clear that rather sooner than later it will be an integral part of practicing pathology.  Does everyone practicing pathology have to keep up with this new trend? Those who wish not to and are close to retirement probably not, but everyone else probably yes. AI will become one of the pathologist's everyday tools and the use of this tool should be taught throughout the entire process of medical formation from medical student through to practicing pathologists through continuous professional education. AI is not scary, but it is a new technology we need to adopt, similar to how immunohistochemistry (IHC) was adopted. IHC entered the pathology practice only in the 1980s and today most practicing pathologists are using this method as an integral part of their diagnostic workflow. It was brand new not so long ago and the pathologist community had to figure out this new method and leverage it to better serve patients. An analogous situation is happening now with AI. Unlike some may fear, the primary benefit of AI is not necessarily to make the diagnosis and replace pathologists. The first thing that AI does is help pathologists manage workflow. It may sound unambitious but triaging cases that are safe/ normal, and allowing the pathologist to focus on cases that are more urgent or more high risk already benefits the profession tremendously and improves patient care. Pathologists should keep up with AI both to leverage its power to help pathology as well as leverage the collective pathology knowledge in different aspects of the discipline for the development of reliable AI tools designed to help pathologists. This episode's resources:Prof. Dr. David Harrison researcher profileiCAIRDVisiopharm 

Why and how should pathologists keep up with AI? w/ David Harrison, University of St. Andrews

If you are working with immunohistochemistry (IHC) you know how challenging it can sometimes be to optimize all the steps in the process to obtain a high-quality stain. It often takes testing different antibodies, antibody concentrations, antigen retrieval methods, and incubation times. What if there was a way to produce an IHC stain virtually, without antibodies or even the need to step into the lab?Today&apos;s episode&apos;s guest is Victor Dillard, the commercial operation director of Owkin. Owkin is a company leveraging artificial intelligence and machine learning for medical image analysis and its offering includes virtual immunohistochemistry staining.  We talk about how it was developed, how it works, and how it can be deployed at interested institutions. To learn more about Owkin visit https://owkin.com/This episodes resources: Deep learning-based classification of mesothelioma improves prediction of patient outcome 

Virtual Immunohistochemistry. How Owkin uses artificial intelligence to generate IHC stains without antibodies w/ Victor Dillard

Aleksandra Zuraw from Digital Pathology Consulting Blog discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

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Digital Pathology Podcast

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