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Interview with Dr. Mozayeni about his project using Aiforia’s AI software to study the novel coronavirus

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Where do you work and what do you do there?

I am a physician/researcher in Bethesda, Maryland, where I am a director of the Foundation for the Study of Inflammatory Disease (FSID), Laboratory Director of a high complexity clinical testing laboratory (T Lab Inc.), Chief Medical Office oF Galaxy Diagnostics (GalaxyDx.com), and medical director of a private medical practice (Translational Medicine Group). We are located in the heart of a major biotech region that includes the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and more. I first came to this area to work at the NIH.

Why did you apply to aiForward?

FSID is positioned to apply aiForward analysis to thousands of high resolution microscope images of human blood and tissue samples contain specific pathogens (or, for example, the flourescing tags of bacterial or viral RNA). FSID has access to large sets of unique images generated by T Lab for such analysis. To date the visual analysis of such images has been up to teams of expert histopathologists, microbiologists, and microscopists to interpret. We see the potential to train the aiForward system to analyze such images with extreme precision and great efficiency. AiForward training thus far will be used as background training to be able to discern patterns of COVID-related disease in microsocope images of blood and skin tissues.

Tell us a bit about your research / work.

My work, in general, is focusead on how inflammation may start and persist to cause acute and chronic diseases. More often than not, these diseases result from persisting infection. My work with FSID is to oversee the foundation’s efforts to explore and illuminate the mechanisms of inflammation, how the human immune system becomes dysregulated or overwhelmed, and what we can learn from large sets of clinical data about the patterns of response to treatment of inflammatory diseases. In the clinical lab, we have developed advanced microscope imaging tools and other techniques to detect pathogens that cause inflammatory diseases. In my medical practice I focus on treating patients with chronic, complex and often dibilitating inflammatory illnesses. This work informs the development of diagnostic tests that can be used to guide medical care for inflammatory diseases. Many of my patients with chornic conditiosn now also have COVID-19. And many with COVID-19 are developing persisting chronic inflammatory illness. Thus, it has become exceedingly urgent for me and others to be able to determine if and how COVID-19 becomes a chronic disease. In patients with other chronic inflammatory diseases, their response to COVID helps us learn more about the pathophysiology of COVID-19, and our understanding of COVID is informed by our prior experience with chronic inflammatory conditions.

Tell us a bit about your proposed project.

As Covid-19 was emerging, FSID laid out a program to analyze high resolution images of blood and skin samples stained for Covid-19 RNA (and/or inflammatory markers) collected from hundreds of patients, and to put those results in the context of other testing, clinical and medical history data collected about those individuals in order to gain better understanding of the mechanisms and impact of this disease. When we became aware that aiForward might be available to generate extraordinary image anaysis and high effeciencies, we reached out.

How is this significant to advancing our understanding of coronavirus?

Finding Covid-19 and related inflammatory responses anywhere in the body will contribute to the understandng of this virus. Using AI to analyze images of either the novel coronavirus RNA in blood or any other tissues, as well as potentially colocating such viral RNA along with markers for immune response would be a very sigificant contribution to our understanding of the viurs’ behavior, pathogenicity and potential treatment interventions.

Is there something novel about your proposed research project?

We see COVID-19 as an inflammatory systemic disease of sudden onset that exerts much of its damage in the vascular system with the formation of clots that impair blood flow. We are focused on the study of COVID in the vascular system and blood components. We are uncovering evidence of Covid-19 RNA in the blood, skin, vasculature, lymphatic system and other tissues. And in the context of patients’ clinical histories and other test results, these findings may reveal important patterns of immune response and significant opportunities for treatment.

How will AI help advance this?

Using AI in this process is novel because AI can detect differences that are not obvious to humans. AI has the potential to increase the throughput of specimens to augment the human technologist’s visual analysis of thousands of microscope slides while reducing the inherant variabilities in the process. So, although effeciencies are extremely important, it is the potential ability to detect subtle changes that are not otherwise seen by humans, and ultimately, allow for a big stepwise increase in the sensitivity and specificity.

How are you finding working with Aiforia so far?

The Aiforia team is extraordinary. Not only are they experts who have built an amazing technology, but they are great teachers and are passionate about helping their clients succeed. We are thrilled to be working with them.

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Interview with consultant histopathologist Jan von der Thüsen MD PhD

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“The solution offered by Aiforia for AI in pathology is extremely user-friendly and while it relies heavily on correct annotation by disease specialists, it assumes very little prior knowledge of AI – a perfect combination for me. “

What do you do and what is your research focus?

I am a thoracic pathologist and my research interests include both neoplastic and non-neoplastic diseases of the thorax, including lung cancer, mesothelioma, thymoma, interstitial lung disease and lung and heart transplantation.

Why did you apply to the aiForward program?

The solution offered by Aiforia for AI in pathology is extremely user-friendly and while it relies heavily on correct annotation by disease specialists, it assumes very little prior knowledge of AI – a perfect combination for me. Also, the online support offered by knowledgeable Aiforia staff is extremely helpful, including very useful instruction videos.

Had you thought much about using AI in your research before applying to aiForward?

No, while we had been considering entering the field for a while, participating in an AiForward program has really kick-started our AI research.

Tell us a little bit about your aiForward project.

Classification of rejection in solid organ transplantation is subject to interobserver variation. In an effort to reduce the variability and to improve correlation with outcome, as well as a point to potentially useful subclassifications of current categories, we have started an aiForward program to score kidney and heart biopsies according to current guidelines.

What are your expectations for the program?

To develop an algorithm which reproduces current transplant rejection scoring systems, as well as (hopefully) provides a more accurate correlation with clinical outcome.

Applications to the aiForward program are accepted on a continuous basis.

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Artificial intelligence for Parkinson’s disease

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AI can automate it and make it much easier and more reproducible.”

Ilmari Parkkinen is on a mission to decipher Parkinson’s disease (PD). The neurodegenerative disorder predominantly affects neurons that produce dopamine. Current treatments alleviate the symptoms caused by the loss of these neurons. Ilmari’s research is focused on elucidating why these particular neurons degenerate and ultimately to discover disease-modifying treatments to either halt or reverse the progression of the disease. 

Preclinical PD studies often involve numerous quantitative analyses, such as cell counting. This is mostly done manually with labor intensive methods like stereology. Hours and days are spent counting cells with this traditional method, often involving multiple scientists. This, like for many other neuroscientists, was a big hurdle for Ilmari and his lab. 

Neuron counting by AI

“AI can automate it and make it much easier and more reproducible,” explained Ilmari of why he decided to apply to the aiForward program. “Because Aiforia offered a chance to create a custom algorithm just for this purpose, we decided to go for it and use it in our models,” he added.”

Aiforia Create allows Ilmari to develop his own AI model not just to count a huge amount of cells but to also identify and quantify the specific cells he needs to analyze for his Parkinson’s disease study. A customized algorithm was needed and Aiforia Create enabled the development of this. “Using AI for your research requires a good amount of expertise and/or a good platform to get started,” he described.

Ilmari has finished developing his algorithm and is now excited to start running the analysis, he recently told us: “Mainly we are expecting to do exciting and robust work that could lay the basis for our future studies to study the mechanisms underlying neurodegeneration. Also, at the end of the day, hopefully we will find a way to make the lives of neuropathologists and researchers doing preclinical studies on PD easier. The great thing is that we already have some promising preliminary data.”

Applications to the aiForward program are accepted on a continuous basis.

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Interview with PhD student Polina Stepanova

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“The AiForward program can give the opportunity to do image analysis faster and more unbiased.”

What do you do and what is your research focus?

I am a PhD student at the Voutilainen Lab at the University of Helsinki’s Institute of Biotechnology. One of the interests of our lab is the identification of suitable drug treatment for Huntington´s disease (HD). HD is a fatal inherited neurodegenerative disease. I am focusing on the research of the effect of a promising protein on HD at the preclinical level. Our aim is to reach clinical level and use it in the treatment for HD patients.

 

Why did you apply to the aiForward program?

The AiForward program can give the opportunity to do image analysis faster and more unbiased.

Had you thought much about using AI in your research before applying to aiForward?

Yes, I had thought using AI in my research due to manual counting of the cells is time-consuming and quite often can be biased. To eliminate these issues it is necessary to use several researchers.

 

Tell us a little bit about your aiForward project.

My Aiforward project is based on the analysis of huntingtin inclusion, which can be found in in vivo models and in HD cases. There are several types of inclusions and the aiForward program can help to analyze them and separate based on the type of the aggregates. Nowadays there is no most convenient method to count these inclusions, however most of them are based on manual counting.

What are your expectations for the program?

Our expectations are that Aiforward can make our analysis faster and more accurate!

Applications to the aiForward program are accepted on a continuous basis.

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Interview with pathologists Helen Remotti MD and Ladan Fazlollahi MD

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“I believe there is a great need for AI-based image analysis techniques in pathology research, and eventually clinical diagnosis, as we need to move from subjective analysis of tissue components to more robust and objective quantitate methods.”

What do you do and what is your research focus?

We are academic pathologists with broad research interests involving liver/gastrointestinal/pancreatic benign and neoplastic diseases. Fatty liver disease has become an important area to study as it has become the leading cause of liver disease in United States and worldwide, and among the top reasons for liver transplantation. Here at Columbia University Medical Center, we receive a large number of liver biopsies from patients with alcoholic and non-alcoholic fatty liver disease. Therefore, we do have the resources and the opportunity to advance our study methods and understanding of this disease.

Why did you apply to the aiForward program?

As pathologists, we quantitate the degree of fat content in our liver biopsies as mild (5-33%), moderate (>33-66%) and severe (>66%) by estimating the percentage of fat under microscope. We also use the routine H&E slide and a trichrome-stained slide to assess the stage of fibrosis (Stage 1 to 4). There is some degree of inter-observer variation in estimating the percent fat and fibrosis stage. The data derived from this type of pathology analysis is used to direct patient care and even enrollment in clinical trials. Now with AI-based imaging techniques available, we are interested to see if these techniques could help us better estimate the fat content, differentiate the small fat-droplets from large fat-droplets and fibrosis stage in liver biopsies. In addition, AI based imaging can be used for quantitating other biomarkers.

Had you thought much about using AI in your research before applying to aiForward?

Yes. we have previously worked on multiple collaborative projects focused on pancreatic, liver, and colorectal carcinomas. We used AI-based image analysis techniques to evaluate and compare the immune cell density in tumor and stroma. I believe there is a great need for AI-based image analysis techniques in pathology research, and eventually clinical diagnosis, as we need to move from subjective analysis of tissue components to more robust and objective quantitate methods.

Tell us a little bit about your aiForward project.

We have a cohort of liver biopsies from patients with non-alcoholic steatohepatitis (NASH) and a cohort of liver biopsies from control patients (liver donors) with correlative Fibroscan liver stiffness measurements. FibroScan is a non-invasive ultrasound-based imaging technique used by hepatologists to indirectly assess the fat content and elasticity (corresponding to fibrosis) of the liver parenchyma. The aim of our study is to use the AI-image analysis platform developed by Aiforia for quantitative evaluation of fat and fibrosis in liver biopsies, and to correlate these findings with parameters obtained by routine pathology assessment and FibroScan.

What are your expectations for the program?

We would like to have quantitative data on percentage of fat and fibrosis in our cohort of liver biopsies within a short timeframe (less than 6 months). Quantitation of additional biomarkers is planned for future studies.

Applications to the aiForward program are accepted on a continuous basis.

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Interview with PhD student Ilmari Parkkinen

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All in all, we are very fortuitous to have been chosen for the program and look forward to making the most out of it.

What do you do and what is your research focus?

I am a PhD student, and I study Parkinson’s disease (PD), which is a debilitating age-associated neurodegenerative disease. The aim of my research is to elucidate why dopamine neurons degenerate in the disease and find potential new ways to treat it. Particularly, I am taking a closer look into the endoplasmic reticulum (ER) of these neurons with advanced imaging techniques and trying to find drug targets or compounds targeting the dopamine neuron ER. The ultimate goal of the research is to find disease-modifying (either halting or reversing) treatments for PD as the current ones only alleviate symptoms.

Why did you apply to the aiForward program?

With regards to preclinical PD studies, which our lab does, there is a lot of various morphometric analyses involved, especially cell counting. Cell counting is mostly done manually with laborious methods such as stereology, but AI can automate it and make it much easier and more reproducible. We wanted to create a faster and easier way to count cells and proteinaceous inclusions from cells, like Lewy Bodies which are found in PD patient’s brains. Because Aiforia offered a chance to create a custom algorithm just for this purpose, we decided to go for it and use it in our models.

Had you thought much about using AI in your research before applying to aiForward?

Yes, we had a few ideas, but it always came down to execution as using AI for your research requires a good amount of expertise and/or a good platform to get started. Since we had neither readily available, a collaboration was the way to go. We eventually found Aiforia, which has both, the expertise and the platform, and offers a hands down and convenient way to apply AI to imaging-related research needs.

Tell us a little bit about your aiForward project.

We are developing a CNN-based algorithm to count Lewy Bodies (LB) and Lewy Neurites (LN) from histological samples, in our case mainly brain sections. Ideally it would identify and locate different neurons (like the dopamine neurons which degenerate in PD) and the LBs and LNs, count how many there are, in which cells they are and also give different parametrics, such as size and distribution, for them.  

What are your expectations for the program?

Mainly we are expecting to do exciting and robust work that could lay the basis for our future studies to study the mechanisms underlying neurodegeneration. Also, at the end of the day, hopefully we will find a way to make the lives of neuropathologists and researchers doing preclinical studies on PD easier. The great thing is that we already have some promising preliminary data. However, it is still in its early phases to say how it compares to other counting methods or identification done by professional neuropathologists. All in all, we are very fortuitous to have been chosen for the program and look forward to making the most out of it.

Applications to the aiForward program are accepted on a continuous basis.

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