For the last few folks to dial in just a reminder.
Nice lines, and as always and I say this and we.
If there's background noise coming from a line, I will take the liberty to mute your line.
So, please be mindful of others and, as I said, already, we are going to record this community call.
Ready now.
Came on, I think it's 3 minutes past. I think it's a good time to start. So.
Yeah, is the agenda for today.
Okay, thank you. Can you hear me now?
Yes. Okay. Great. Thank you. Sorry about that. Um.
Well, welcome everyone to this last episode of imaging Informatics company. The webinar.
For 2020, and, uh, today it's our pleasure to host our colleagues from the National Institute for allergy and infectious diseases in our ID.
Uh, for the presentation of the TV, tuberculosis portal.
This presentation comes to you from.
You go to the next slide. Oh, you don't have that slide sorry? I need to share my slide.
Okay, so the presentation comes to you from the, in the office of cyber infrastructure and computation biology.
I want to thank doctor, Darrell hurt the chief for Informatics and computational by sciences branch at and his colleagues who will be presenting today's.
Presentation led by.
Doctor Alex Rosenthal.
Okay, yes, yeah Thank you. Everyone.
Yeah, I'm ready, thank you. Thank you.
Go ahead and take control. Yep. Yep.
I'm doing.
You have now the presenter rights.
Can you see it? Not yet.
Not yet. Okay. Hold on.
Okay.
We can see your desktop. Okay. Can you say thank you. Okay. Very good.
Yes, Hello, thank you for inviting us. My name is Alex.
Rosenthal I'm the chief technology officer for all of us who will be presenting today we are all from the same office as came on mentioned cyber infrastructure and competition. Biology.
So our agenda will start with me introducing the program.
And then we'll have several demonstrations and.
Some discussion on the data science research that we've been doing.
So, this Colossus program was established in.
Yeah, I'm sorry, can you put on presentation mode?
Oh, I was sorry.
No problem I was on a presentation.
Yeah, I apologize. I switched back and forth, so and I okay, so the TP portals program was established, uh, about 7 years ago.
It's centered around.
Tuberculosis cases with patient level information and includes not only imaging information.
Which would be chest X, Ray Sanchez cities, but also includes.
The rest of information to that normally comes with natural history.
Case such as diagnostics, direct sensitivity, testing microscopy, culture by chemistry, socio, economics.
We also tried to sequence a bacterial genome for every case in the database.
And we are expanding to include and whole genomics.
This system is currently getting data from 15 different countries, and I'll go more into specifics.
Soon where we've built 4 different tools.
Uh, for data collection and and and data analysis.
Most relevant tools for this group will include.
Our simple data query case browser, advance, cohort, base, analysis depot in our tool.
For image analysis, you're going to see demos for all 3 of the systems.
So this program started in about 6 years, the 1st publication that got us thinking.
About the stupid Colossus program and.
How imaging can really help with various aspects of tuberculosis diagnosis diagnosis and treatment.
Was our work that started in about 2012?
And it resulted in this publication.
Which basically demonstrated how you can look at 2, subsequent scans.
Using computer based algorithms and see how individual lesions are performing over time. So so this whole project started with.
1 of the researchers please very.
Approaching us with the following question.
He was trying to measure efficacy of the treatment of his clinical trial that he was running South Korea.
Through Pat and city scans and he was not getting consistent reads from radiologists.
So he wanted to have something more standardized.
And he approached us, so that's how we ended up with this publication. So we've.
That we are able to do a lot more in that area.
Now, where we can basically take a look at.
2 city scans, maybe 1 week apart and see the differences and individual lesions.
So, it took a while, but we've been working on it, but that brought us.
This idea of building this database, which will include images and as 1 of the highlights about other things as well as I have described already.
In about 2018.
The program started receiving recognition from.
Various sources, for example, right. I'm showing here that it was mentioned in a national action plan.
For comparing a multi track, resistant tuberculosis.
It's a plan.
It is also included in a strategic plan for data science.
It's included in strategic plan for tuberculosis research.
It it's mentioned by W.
You know, in in, in it's a major publication.
And we now have our sites that started referring to us.
As a recommendation for.
Recording tuberculosis cases, and for.
Sources of tuberculosis data that can't be analyze.
So, as I mentioned earlier, uh, we've been collecting data in 15 different countries.
And they're all listed here since it's a very global program. I, I wouldn't go for a listing called the sites, but you can see them.
Everything in blue is what are we collecting data.
I will also brought in some data from data feeds. Several data feeds, have been processed and we've been doing scientific collaborations in imaging and genomics.
A, in a number of places.
And this is what we have today in a database overall.
We have we're getting close to 7000 cases.
About 2 thirds of them are drug resistant cases.
Cannot be countries listed in some countries. We have multiple sites where we're getting data from.
Over 150 data points, uh, all this clinical data that I've mentioned, 15000 specimens.
About 3400 full genome sequences.
Where are we not only collect the, uh, uh.
Not only we facilitate the process of sequencing.
And we don't sequence ourselves just like you are a computer or computer science organization so facilitate the process of sequencing.
But we do also various scientific workout ourselves.
Against that data, such as a classification of lineages.
Extraction of most well known track, resistance, NIPS, et cetera.
In terms of imaging data we have about 2500 cities about 6000 X rays.
Many of those images include the ideological annotations by, by actual radiologists.
We also include expert system annotations.
From the company from India, your, it's a well known company in a field of tuberculosis.
Artificial intelligence we extensively pretty compete with various similarities, which assists us in algorithms. We're going to describe later.
In subsequent presentations, we store segmentation masks.
We'd have a lot of biochemistry data, and we are expanding into.
So, to bring with data, and there are 2 ways, you can either just enter the data by hand.
Or he can use the API, so, 7 compliant and.
Which store the data and 7 format.
And to get the data out, we have a formal process with data use agreements.
And once the agreement is signed, you can bring in images through based.
Repository that are also various so that you can use submit data.
There are several tools that can be used to look at the images.
And with itself is either a project based, which means the, uh.
Pi can explicitly share with certain collaborators.
And if you are not on the list, you cannot see it and there is also open access, patient level data.
Where after submitting data, access, request, and signing the data use agreement, you can see it.
You can download it, so we have 47 data use agreements signed.
There are free and grants, which include direct collaborations with the program.
And we have 8 hour collaborations so the data is being used by outside parties that are publications based on that. Diana it's just 1 example.
We ask, so here is, Here's how we announce the program to the world. This is our reference publication so to speak.
From 2015 from Journal, clinical microbiology and.
We ask this paper to be cited then.
Our data was used or any kind of research for us.
And this is the latest paper where we explain the technology behind our analytics tool.
Tv Depot, which we'll be showing today.
And this is our overall list of papers presentations and posters.
We've been very actively publishing as you can see here. There are 54.
Various types of checks here and, um.
To show you kind of a big picture.
I'll follow ecosystem you have a database in the middle.
And again, the data can get into his database either from the.
Or, by direct, uh, data entry.
And there are 4 different tools 1 for browsing.
The case browser 1 for advanced analytics Depot.
Tool for genomics analysis gap and so this is my challenge ecosystem. We also have a website for the program.
And that's about it for me and now you're going to hear from.
Senior members of the program team, who will walk you through various various tools that we have and for various research.
Items that we've been working on Thank you very much.
Thanks. Hi, everyone my name is Alissa long. I'm the data manager for TV portals.
And I'm sharing my screen, it should be coming up now.
Yeah, we can sue I also said the link in the chat so you're welcome to follow along the TV portals websites are publicly available.
I'm signed in as an administrator, so I'll be able to see about double the number of cases that you'll have access to. But as I mentioned, we wanted to start with.
The case browser, which essentially is just the case report form. So, it's all of the data that we collect from, over 15 countries. We have over 7000 patient cases that are in the portal so far.
So, you can click to view a particular countries, patient cases, or you can just click here to view all of them.
That'll take you to the browsing screen where we're going to have our filters on the left hand side of the page.
And then each card is going to be an individual patient case. So if you wanted to view the patient case details, you had selected this.
And if you wanted to filter on any of our particular countries, or maybe you wanted to only look at.
Uh, cases with images, you can select that modality here.
We also have a bunch of additional filters that you can select here if you'd like to use those.
For the purposes of today's demo, I'm going to go to our demo patient.
So, I can walk you through all the different pieces of the form that we collect.
Just hit case details and all of the data in this particular patient case is fake. It's use for training purposes. Um, all of the other countries enter the real data.
So, we've got a sample patient case here. You can see the type of resistance at the top, which is very important for tuberculosis. In particular. It's a multi drug resistant patient.
And then we've got common clinical and so she'll demographic data at the top listed here.
Some important factors are again, the resistance profile for TV.
The case definition, uh, we collect height and weight, so you can calculate.
And long localization and comorbidities scrolling down to the next section we record all of the different specimen samples that are collected over time.
So you can see for this patient, we have the 1st sample collected in May 2018, and you can add as many as we're collected for that patient along with this specimen type. This is important for the drug susceptibility testing tuberculosis.
Moving down to the next section, we collect microscopy and culture results so you would indicate the microscopy types and result culture type and result that indicates resistance.
And then we have drug susceptibility testing, so we have a number of different tests 1st line 2nd, line backpack and experts and then you can hover over to see what drug was tested. And whether it was resistant or sensitive.
The next domain that we collect is biochemistry results. So we have a number of lab results that are recorded for blood and urine specimen. That can be shown here.
Followed by our treatment section, you can see the different treatment regimens or list of drugs that that patient was treated with over time.
In addition to the overall outcome for TB, which are defined by.
And then, finally, at the bottom here, we have our imaging studies passage in genomics, and the timeline view of all the different data points of the case. For the sake of time. Today I'm just going to focus on the imaging section.
So, if I were a collaborator, I would click the plus button in the upper right hand side. And that allows me to upload an image.
1 of the things that we wanted to showcase is we have built in cloud services to detect potential Pi.
So, not only do we remove any that is included in the dotcom header and anonymize that.
But we also check the image itself to see if there's any in the image.
Does 1 example, you can't really see it and that's intentional.
In paint, I modified this image. I wrote John Smith and gray to match the background just to see if we would be able to detect it.
And how it works is I take this image, we prefer that all the countries, but since we're working with some countries that don't have access to those, um, we do accept files as well.
Okay, so now I've got the preview of my image. I select the modality.
Centered today again, cause it's a steak patient.
Selected studies, so, what this is doing is, it's, um, calling a script Atlanta calling recognition and comprehend medical to try and detect. Was there image any text included in the case?
And I realize I accidentally.
Dragged in our redacted version, so it's not going to find any, but if I had included the test image with text, then it would have detected that right here.
I apologize for picking the wrong image there.
Okay, and so now, at the bottom of the page there imaging 2nd, we can see December 7th is when it was added.
I'm going to view this imaging study and we have our images displayed. So here's the redacted version that I uploaded.
And you can see it's displayed in cornerstone.
So that's the image viewer that we have. And then if you wanted to add annotations, you can create a report using the X. Ray descriptors.
Or descriptors so, for the X Ray.
We identify the key characteristics per section.
To answer some overall information about the X Ray itself.
In check which substance have abnormalities.
Which is upper left and then you'd enter.
Annotation such as cavities infiltrate.
Nodules and long clap.
So these are the manual annotations that a radiologist would enter for our X. Ray.
And then we have a different set of annotations for our.
I have a patient pulled up from bellarusse.
But the, that I'm going to show today, so for our, you can see it's loading all the different instances here.
And we have automated lesion detection, so, for some of these key attributes, it identifies of all the different instances in the case study where it is detecting or any of these.
And then it shows you some representative key slices.
Since all the instances are now loaded, you can also interact with the image using the, and give them tools.
And then down at the bottom, we've got our list of annotations is identifying similar hatcheries as X rays, focusing on cavities and nodules lung claps.
If you switch over to the image analysis, tab, these are the results of the detective lesions.
You can download a PDF report of those when you're logged in.
And you can also view similar cases based on those lation detections.
So, to summarize, that's all of the quick view of all of the data that we're collecting during our case browser that the countries are either manually and putting, or automatically letting into the portal.
And from here, we take all of that data and send it to our other tools, which Andre is going to be demonstrating.
All right, I'm going to stop sharing and over to you. Thanks, Andrea.
Thank you all Lisa I am trying to.
Stop sharing my screen. Tell me if you see my screen.
Yep, we can see it.
I'm going to continue talking about the portals resources.
And the main question is, or was for us, when we started to build all these resources was, what are we trying to do? If on top of the excellent.
Exhibit or museum as we sometimes affections recall it, which is the case browser. How can how can we help clinicians? How can we help researchers? How can we help logistics? We wanted to.
Help them to extract the knowledge from data because we have a lot of date and we have a lot of data you already can.
See that, um, it is.
Indeed, the largest patient centric database, which is very important because we know everything.
About our patients we know what.
What pathogens in a way that done because we sequence the pathogen we know something about lungs because we have.
Either city or X, Ray, or maybe.
We know there are social economic status, and we know how they were treated. And in many cases, what was the outcome of the treatment?
That's definitely a bank, for example.
Might have more genomes than us.
But you cannot distinguish what gino's for coming what what pathogen ginos were coming from patients that were successful the 3 that are not successful with you you cannot connect the
properties of lungs.
And the genomic variations, so we take a lot of pride in the collection that that we created and we decided to build the tool.
Which I'm going to demonstrate, which is called Depot, it's our advanced analytics tool that would help to extract some knowledge and help with.
Testing some, some ideas and hypothesis that the researchers of doctors might.
We wanted to make it.
Extremely friendly for those who use it and we subdivided.
Our data to 4 different types. So therefore, therefore.
Entrances today to the project to.
To the deeper we can start anywhere so.
If, for example, we would like to start with clinical data, which is the biggest selection right now.
I'm clicking English.
And here is the here is the menu here are some graphs that show the distribution.
Of types of resistance, resistance, distribution of outcomes, distributions of ages, body, mass indexes for, for patients that we have in our database.
And on top, there is this query builder that works with common attributes.
And allows us to slice and dice.
To be part of those database to create virtual course that then.
We can compare or analyze as, as we wish to test our ideas.
Again, I, I would like to kind of go over several principles that we tried to.
Instill retry retry to always think about when we were building the 1st of all.
Here the query builder.
Should be simple enough and we chose the syntax that is compatible on general scale with the.
That everyone uses, so, let me give you an example.
For example, we are interested mostly and this is where to reflect within the database in the resistant case of tuberculosis. Why? Because.
If for sensitive tuberculosis, the rate of success of treatment is very high, it's above 90%, then for multiple drug resistant to particulars it's not that good.
It's about 60 70%, depending on the country and circumstances for eggs. They are extensively direct resistant, tuberculosis response to even less drugs in the worse. It's 40% sometimes worse.
So you see that our database reflects our interest alerts.
The resistance resistant tuberculosis and it is not.
Representing epidemiological logical.
Situation in any country that we work, it's a research database, but anyway, so imagine that you're a clinician and you have some idea about.
The data that you work with about potentially not the date about the patients you work with patients that are are sick, you treat them and then they come back because they have a relapse of tuberculosis. It's, it's unfortunate.
And you are trying to understand what might be the reason of the relapse of tuberculosis. So, based on.
The preliminary ideas that you might have, you are thinking that, let's look at the.
Cities that are X rays that we might have in the database to see.
Maybe they have something in common. Okay so.
I'm clicking on the left side of the menu.
And I'm I'm looking at.
Let's say multiple drug resistant tuberculosis cases.
Notice when I click on this, the corresponding query appears in query builder on top.
Okay, now I want also these bad guys, extensive resistant, tuberculosis, so, and put in another.
We return and they are connected with bull and operator or.
And put in parentheses, just in case.
I want to make sure that I'm working with cases where the image exist.
Okay, so I put another query term here.
Yeah, and let's see how many cases like that we have.
Overall, we have 903 cases of multiple drug resistant works extensively and tuberculosis.
In our database that half in the records seat, a city.
Now, remember that our idea was to compare.
Uh, the, um, um.
New cases and relapse cases. So.
They are in case definition here.
We have out of this 900 we have 2, the 55 free laps.
And 420 or 2223 new patients so.
We can make this cohort by adding 1st cohort.
Will make 2 cohorts 1 is relapse basis. Another is new cases.
Hey, this is going to be our 1st cohort through lapse cases.
Notice the time right now right now I am in clinical tab. Okay.
If I want to have a look at these 252 cases that I just select that.
That they can be looked at here in city status.
Because, as I'm doing on the fly, my slicing and dicing of the database.
It is faithful presented here in this table.
Okay, I can look at any case.
That, you know, I'm, I'm choosing.
In in my cohort, if I don't like it, I can exclude it and I can view it in the viewer and so on and so forth. So it's pretty comprehensive.
Resource not only in making virtual cohorts, but also in making sure that the virtual cohorts.
Contains only the cases that I really like as a specialist, and I can, I can fine tune it to my needs.
Anyway, so 252 cases.
Of resistant tuberculosis where we have city images.
And where we have, and the case definition, they came with relax.
We can save it, save the cohort.
I will call it.
Resistant cases resistant.
Collapse.
Okay, and I usually add the timestamp.
Because the colors that you create will be a.
Will be stored for your reuse.
Yeah, and this is great in terms of reproducibility.
So, I'm saving it.
Because, as you can see, this is copy pasted right in the list of my cohorts and I have about 600.
14 cohorts now and counting all these cohorts were created by me at different points of time.
When I was interested in 1 or another feature.
Or, you know, had 1 or another hypothesis about the data.
They're all here, I can send the corresponding query for the, for any cohort that I created it at any time to my colleagues. So they can.
Placed it in the query builder and get exactly the same cohorts as I was getting and, you know.
For their study them further, refine them.
And it is, it is good for publication for openness for reproducibility asset. So okay, that our tool.
Very friendly query builder syntax allows us to.
To do the cohort, let's change something here and remember that time.
1, to 3 Labs versus new, so in case definition.
We have new cases I will just do a short instead of clicking. I can just write here.
Okay.
This is my, my 2nd cohort for friends with 23 cases.
Again, the resistant cases where we know cities exist and case definition, you I will save them.
So it is this.
You see the demo.
December 1. okay. And now I can analyze this. I can, I can cross compare.
Everything that is known about about this patients. So.
Let me save and return because I wanted to show you.
How many various parameters attributes we have.
All that you you've seen here is is just a part of it.
That we put at your fingertips at at user's fingertips for, uh, to satisfy most of their needs. However, here is the full list.
That is always accessible for you and you can see.
But, you know, in clinical behalf planted in, in genomics, we have.
A lot of bioinformatics oriented data in city studies and X rays.
Everything everything everything, and, of course.
The great thing about that that is that no doctor.
Even the best doctor can store all this information about.
Hundreds and thousands of patients in his head.
And what we're trying to do is we are trying to help the clinical specialists.
To find common features or different features between different slices of society, different slices of TV portals to understand.
What makes the treatment successful,
or what distinguishes between patients that have certain clinical manifestations of the disease and the lungs and can we explain that with them from the point of view of pathogen?
From the point of your cost anyway.
Let's try.
Yet another thing, because I wanted to illustrate an important thing that we have here, Alex should refer to it already.
But I wanted to demonstrate how we do that. So, for example, in city studies okay.
Remember that everything that we have right now?
In this table, it's it is our our.
Our case, it's the cases that we chose from the large database, according to this query. However.
This is not the only way that we can use to create our cohorts. Okay. So.
Let me get through this, get back to.
The square 1, and instead we will.
Create another cohort based on similarity that we will define them.
Specialists might define according to the needs that currently he, or she might have.
So you can see here on the left side.
Create similar patients by click on it.
I will use the patient 375. I know this patient.
Not personally, but I know that this is patient from.
Who has to be multiple tuberculosis.
And we had at least 1 city image for her.
So, when.
The logic line is is like that.
You are in a case browser.
In case browser, you can browse things and.
This is patient 375.
Something caught your interest, like, in.
Dark susceptibility, or how the regimen of treatment was was done and so on. So you're you are curious whether.
You can create a cohort.
Either based on a similar.
Clinical and social economic teachers, or.
Based on similar radiological features that can be extracted from the data.
Both of this patient and that what we can do.
In in ideological world, we can say, okay.
We are interested in size.
That has been so that has been detected during the 1st visit. Therefore, the work 1st, here.
Okay, and we wanted to find similar patients with affected.
Okay, so out of the full list.
Features we left just 2, and we say, find us the similar patients we define similarity by the presence.
Of the features that are of interest for us and.
There was just 2 features, but we can make 3, 4 and so, on here, we have the similarity score. 100%. If both of these features are present both in, in our case. And in, in the case that was.
Taken from the database, we can.
Further look at, at every case, we can exclude them or include them in in the cohort. But if.
If we select all cases, then.
Essentially, we create the cohort of 511.
The cases we can proceed with that.
And this is another way of creating a corporate so the query builder.
Uh, has this.
Slightly ugly, but very informative lines and lines of codes and that lists all these patients that were just.
Chosen from the database based on our similarity.
Terms and we have this 511 people as a cohort that again we can, uh, we can save it.
For further we use, we can then compare this cohort with other cohorts and so on and so forth.
See me a little bit.
In the clinical clinical tab, we have this great similar patient cohort so I can say that for patient 3, 7, 5 again.
Okay, we will, we don't care about the country. We don't care about the employment.
We care about the H, and let's say we care about gender.
So, we define similarity based on these 3 parameters. We can find similar patients.
And then.
When this virtual cohorts are done, we can save them.
Uh, either as Excel file.
Or we can.
Or we can ask to be portal staff.
To give us the access to images and and so on and so forth.
Anyway, so.
How much time do I have because in I'm not sure.
How much time is left for me yeah, thank you. Yeah, you're out almost out of time. Yeah, I'm almost out of time. Okay, let me show you 1 thing. That is, uh, about the comparison.
But, uh.
The good thing the good thing is that even even though I want to be able to show you everything, that is.
At least of publications about the, about the depot.
Sorry this yeah.
Very quickly I wanted to mention that there are.
2 publications 1 is very fresh. This number 1.
Alice is the 1st author, and it will explain all the technical incident and nuts and bolts that went into the building go deeper and.
Number 4 here is our ideological, so to speak paper about the deeper where we explain all new answers about.
How to build the queries what kind of hypothesis you can check and so on and so forth. So we.
Invite you to explore the deeper and read the publications to learn much more than I was able to demonstrate. But 1 minute that is left hopeful that you will allow me.
She was still cohorts. Okay. And this is.
A huge list of values.
Of course that I have created, so I.
Is here.
1, what I doing here is we are taking 2 cohorts that we just created.
And we're asking deeper what is actually a different.
Existing news this until apps what is actually different.
By in the social economic in general ideological features.
Between patients that belong to this due to cohorts so.
And deeper compares it all again it goes through this huge lists of attributes and compares these 2 groups.
1 is our relapse cases and now there is new cases for transit, 23 versus 252.
It it compares them, it does this contrast analysis and here are the ideological features that are.
Different between these 2 groups between these 2 cohorts. Okay.
So, we can see that we have 12345 6, highly significant.
Parameters that distinguish between these 2 groups, and then significance kind of deteriorates. Okay. And, uh, the rest is not that significantly different. However.
Out of this significant parameters we can try and get further hypothesis about what happened why.
These poor patients had access to this and how.
It could be a managed differently and prevent this from happening anyway. Um.
Sorry, I'm out of time I really wanted to show more, but I am always open for additional questions.
We invite you to use the deeper and other tools that and.
Thank you for your attention.
Okay, so hope you can see. Okay awesome.
So this is it imaging scientists to buy Informatics and computational by sciences branch and you may know me from my past,
which was a develop and I'm a strong supporter open source software that image guidance surgery tool kit.
I'm involved in the insight toolkit and we are the developers of at bcb.
So, an, or are specific for our specific.
Here, I'm a conducting research.
And the domain of image analysis on in TV portals, we have both chest X rays, and currently, our focus has been a on a chest X rays.
And the 1st thing that we did was develop a classifier for TB, not TB and which brings up my pet peeve.
And the complimentary fast, it TV is not normal or healthy. I've seen so many papers where it's the classes. If you don't have TB, apparently you're healthy.
I'm sorry, I have a lot of other diseases, but, uh, probably not TB. So read critically.
If you're looking at these things, we trained a deep neural network, conditional neural network, dense net, 121.
And we tried a variety of networks. The main conclusion was the more data that we get, including and generating a synthetic chest X rays, using generative, adversarial networks.
The more we increase that the better our performance.
We are evaluate and our goal is again a general.
And we evaluate our models against a, and.
Tb screening program that provided us with a 300 about 9300 images out of which 300 RTB and 9000 are not TB and we're able to get to about a
point.
7 9 use.
And and commercial products exist out there, and we compare ourselves to a commercial product that this group currently uses, which has an of point 8 1.
so we're pretty happy given that relatively. We have few images to train on, but we're.
And so we have our 2 models. 1 is the dense 121. the other is we did a 5 Volt cross validation and created an ensemble out of that.
And that has a slightly lower on that data specific data set. It has a higher on other data sets. But again, the importance for us was to.
Evaluate our method for our network for generalizability and it generalizes decently.
And across these domains without data without the need for, and.
Domain adaptation, so that's that we are also looking at a chest X Ray similarity content based for content based image retrieval.
We get our original and chest X, Ray, use unit to segment out the lungs.
In that, and we use more of a traditional approach, not a deep learning approach. We use engineered features and do some metric learning to a.
And what would be a correct distance between corresponding images and.
In the beginning, I said the goal we have research collaborations and the goal in the end is services. So.
Research to service this research,
these pieces of research will become services analysis,
portal and TV not TB should be there and next month,
and the chest X,
Ray similarity and in January or February 2021,
then you'll be able to upload your image and get the closest the most similar images,
or upload your images and get a probability of TB or not TV.
We have collaborations with various groups.
1 of the groups is a biomedical and Genesis group from the National Academy of science and, and they're working on deep neural networks for CP segmentation and.
For the whole long, and then within the long various, eh, lesions and and and.
Clinical findings with the goal in the end to make this as a service on radio comics portal to.
We also have a collaboration with a group from and.
And that's with her being party party and so here the W***, they analyze images and extract specific findings from a.
And we have a service that is almost online, eh, that does various comparisons and allows you to do content based image retrieval. If you have your own that you want to bring into TV portals and you're interested in seeing.
What are what was the diagnosis for similar patients? What were the frequent regimens and so forth? And you can select 1 of these 4 different comparison metrics.
You upload your data is segmented and then their comparison is done.
And again, research to service, which is our theme at the segmentation is expected to be somewhere in 2021, incorporated into the analysis portal.
And CP similarity is going to be, I believe next month.
So, beyond these services beyond these collaborations that are close to becoming services.
We also have an ongoing collaboration with doctors, Stefan, Yegor from the National library of medicine.
This is with respect to classification of patient images as drug, sensitive, or drug resistant from the chest X.
And clinical data, or plus the clinical data the unique aspect here is the combination of images plus clinical data that TV portals is 1 of the few.
A data collection image collections that has this additional information that we want to take advantage of. And this is ongoing.
We have initiated a.
Collaboration with a doctor less Folio from the edge clinical center for evaluating the utility agreement with the, the based content based image retrieval.
Again, and we retrieve their images, they are defined based on our definition of similarity. But does it make sense for a clinician? Is there a clinical benefit?
Because if there's no clinical benefit, then what does the, what do these and what does this similarity act in? So, this is just at the beginning stages and possibly, you.
And with that, I think I hand it over to and I gave.
For his portion of the presentation.
All right I'm going to share.
Let me know when you all can see the screen, I can see.
Okay, great. So, I'm going to try to go quickly. I'm rounding up the group almost like a relay race. So I apologize as I go quickly. Can follow up with us with questions just to sort of rehab.
You've probably seen before that. This is a growing resource, but I want to mention.
So, you can see it sort of graft out that really, it's kind of growing very quickly, the past 3 years or so, in terms of the number of cases, these are actually publicly available, but you can download once you go through a or a data request.
And the interest by the community is going up, so the requests, the agreements are going up, it's really unique because all of this data associated with the images.
Is put into the context of the timeline of the case and you've been shown that before, I think really good presentations and I'm a data scientist. I'm doing all my work in our and Python. We're really interested in hearing from the community.
So, we do have an API sort of geared towards data scientists where you can get this metadata and the context of the images in development and coming soon. So, if this is really interest, please go to the website, fill out the agreements and start to look at the data and let us know what you think.
So, I'm just going to briefly represent some of the data science work.
Colleagues in the group,
for example,
some of the deep learning work that does if had mentioned that they presented this last year at the world long conference,
where they saw that there were various impacts of using different image intensity Pre processing on the deep learning classifiers predict TB,
not to be quite interesting and that sort of highlights because all this information is available to the resource that you can do this.
It was mentioned before that this company cured at dot a.
I'll adjust features,
so actual radiologist from the sites saying,
is this a cavity or is there not a cavity in various images other members and the team,
including Andre and others have analyzed and compared the automated to the radiologist annotations and then.
Just to sort of, round up quickly, so we I know we're right at the hour.
This is unpublished data, but we've been looking at some of these annotation features associated with images, such as the radiologist annotations, and the impact these have on risk of treatment outcomes. So, we're finding that.
You know, things that have been reported previously, like cafeteria disease, these are associated with poor outcomes and hence at the quality of the data that's being collected. So I kind of went through very quickly.
But if you have questions related to this, do feel free to follow up, and I'll pause there to hand back to that.
The meeting organizers and thanks so much for letting us present on the work that we're dealing.
Great Thank you so much. That was a very interesting and thorough presentation by quite a team.
Oh, there's an active chat conversation going on. We can't go through all that, but I want to open the floor. If there's any questions we have time for for a few.
And please mute yourself.
That was a great presentation and it's really good to see all that. I was curious about 1 of the things we've been debating on the imaging data. Commons is versioning for cohorts. Do you store your query strings together with a version or do you ever change the versions of the data and TV portals?
You handle that.
Yeah, so we're in the process of doing this, right? We would like this is Alex. Um.
We would like to be able to include, uh.
Queries in publications, and just allow people to run them.
Hope that the same results will come back. We'll also would like to create some public queries and allow.
Interested people to run those and get predictable results back. So.
I think the way we would like to do this is to store with date when the query was run a.
Um, with the query, and, uh, if you want to get the same results, you should be able to do it and if you want to refresh with data, you should be able to do that as well.
That will basically require us to time stamp our data.
And we are kind of working on architecture to make it happen. We've had good designs, but got pulled in a top priorities, but the end result, we are hoping. Yeah. So, it's just like, you know.
Always happens, so what it is actually 1 of the key goals that we are trying to reach for 40 per disability. So that's essentially it. So basically timestamp the data.
Make the date available in addition to query and get the same results when you run it as of that date.
Thank you. Yeah, it's a tricky client so I understand it takes a while. It's not actually but yeah, but the.
Yeah, we've done similar things with file types of data. Cool.
Are there questions.
So, I just put a question in the chat.
Okay, I ask if users can contribute analysis results to the program or to the portal.
And the answer is that if they join the program okay, membership is important.
So understand, this portal is not cloud based, but using a copies.
Program to refine the I answered this question,
Kevin and and the basis was the following so that our data that are Pre computes as Alex pointed out and the results by themselves.
I'm not sure how to store them. So, for example, I would not know how to store the result that would.
Uh, let's say state that the large cavities are related to certain types of tuberculosis. The, um.
The proper format for that with the publication. Uh, the, the findings are immediately obvious if you reproduce the.
Cohort comparison based on the cohort.
That that are stored, but, um, it's it's a tricky question because yes, we would like to.
Not to dance from the same from the same square every time. But how to.
How to allow for storage of results and then how to capitalize on that and augment that it's a, it's a very good question.
And I'm afraid that I currently don't have a good answer for maybe other members of the team can contribute.
Okay, thank you. Yeah what would like to do is to derive some variables right? So, if we can somehow generate additional data points.
Out of the data we already have rights. For example, I mentioned generating cleanish data out of bacterial genome strings like this. And then essentially you get.
A more interesting data set out of it. Right? You just get augmented data set and that's how we've been. We've been viewing this in terms of additional data generation, which.
That's come out to the analysis, but general analysis results just like Andre said, we are, we're not sure what to do with them. It really depends on what the results are.
Yep,
a great point,
Alex I,
I really appreciate your bringing this point,
because depending on collaborations we can say,
okay,
you found this very interesting dependence between 2 or 3 or 5 features and the.
The coefficient, or if you wish the combined, the combined feature that we can make out of this 5 is very indicative of severity of disease or or anything else.
We'll, we'll Pre compute it and we will put it as a new variable. So everyone can then.
Capitalize on the discovery that you did, and use this new variable for their own research and clinical practice. That is completely possible. So that is a great point. I was thinking.
Yeah, but also they're interested in collaborating, right? Uh, with, uh.
Yeah, so that's where of this derived data can come from really comes out of the algorithms with with.
This program can grow very rapidly if our collaborators are able to produce with derived variables.
Such as a severity score, for example, right? Or volume yeah. Volume of abnormalities. So, for certain type that are just so many imaging variables that can be generated.
Are are there any thoughts or plans to extend this to go with.
You know, well, the struggle of expanding it to other diseases, you've probably noticed that in my.
Leading slide I had a.
Which is not to the closest microbacterium. So, and we are actually going to repurpose with software for the tests are similar treatments are somewhat similar. So.
It really depends. It took us years to understand tuberculosis.
Uh, so many of us actually can answer probably medicine related questions at this point. After all these discussions we've had.
It depends if we just it just want to have a general purpose slicing and dicing.
Type application.
Sure, if we if we think that.
These the same types of descriptors threat are used for particular courses can be used for call it. They make sense. Right? Sample general.
Descriptors types of cavities and volumes and in, in which sex and all that kind of it really depends. We prefer not to do it just for clinical data.
If there is, uh, at least imaging data that we can bring in very similar.
I don't know if what we can do with vital data honestly, but this is what we've been trying to do, right? Have the clinical data, have the imaging data and we have some kind of a pathogen data and together.
That's where we program really shines doing just 1 domain at a time. It's just not as interesting.
And Alex, remember, we are started tracking it as a comobidity.
So, we're going to see tap in around no, I see. I believe already to add it as a quote that that that's I can do. Yeah, it's it's just a.
I think I think it's more about repurposing all the software we have for corporate. So I'm, I'm not sure about that.
I, if I can add just 1 minute of my own comment on that, I'm probably more enthusiastic than Alex. Maybe, because I don't see that many obstacles because for.
As Alex mentioned himself, the main players are the same it's the cost.
It is the pathogen and then there are drugs so.
By by, including a lot lots and lots of methods that are absorbed right now, and, you know, a lot about virus and how it mutated.
A lot about the about many costs, we know the clinical manifestations that are visible on on CDs.
So, in principle, the analysis like that, and the database like that, I believe would fit perfectly into.
Into the ideology of the portals and TV Depot analytical tool that sits on top of it, but easier said than done. And I will.
I I agree with especially the.
Pneumonia kind of aspect of it, right? Where it has manifestation in the lungs.
That that makes this more suitable so we're excited about like I mentioned earlier we are.
Somewhat excited about bacterial many morning.
Maybe even about light aluminum on it, so, yeah, maybe he can for the, because of the nature of the, most of the complications that that could be. It would be interesting.
Okay, thank you. Well, in the interest of time, I think we'll move on, but I really want to thank you Alex and your colleagues for a great presentation.
Look forward to following your progress with the team the portals.
You will thank you. Thank you for inviting us. Yes, of course, you're welcome to stay on for the remainder of the program, which we have updates on the imaging repository.
Um, platforms I was gonna say portals, but eventually it will go to, uh, update with Justin Kirby.
Thank you.
Okay, can you'll see my screen? Yes. All right. So going back to 1 last month's meeting was starting with November. 3rd. We had a.
The piggybacking on that conversation, we did some analysis results status submitted to, um, so this was a high resolution prostate segmentations that were derived from the prostate X challenge dataset.
That was on tca. They took 66 of the 2 scans and the ground truth. Segmentations can be used for training, deep learning algorithms.
We also adopted the IBM espera software recently and,
uh,
our initial rollout of using that for facilitating high speed downloads was focused on the SP,
tech collections and the National lung screening trial.
We are in the process of transitioning a number of other mostly pathology data sets to use the Spero so that we can migrate away from the box system that we were using to manage a lot of
those.
Then we had the prostate repeatability collection, which was initially released as a restricted access data set has been transitioned to a fully open access data set.
This 1 contains multi parametric, prostate data and allows you to evaluate repeatability of memory based measurements.
And then the last 2 are both related posts. The TCI team had 2 different presentations that are this year. I just got an email this morning saying that all of the.
Content from virtual meaning is gonna be available to attendees through I think April. So if you missed either of these presentations, there's lots of time to go back and review the videos that your own leisure 1 of them was.
Called creating publicly accessible radiology imaging resources for machine learning and AI and the other was novel discoveries on the cancer imaging archive, public data sets.
So, the, the latter was focused on emphasizing some of the most interesting discoveries that came out of work, based on data sets over the past year.
And then the 1st, 1 was a lot of discussion about practical considerations on issues that you might run into and trying to build resources.
That's all I have.
Thank you, Justin. I hope my audio is okay and you can hear me.
Yes, okay. I had some audio trouble in between so.
Apologies I want to give a very brief update on the imaging data comments.
As most of, you know, the minimal viable product was released at the end of October.
And we had a special additions imaging Informatics on November 16th.
The recording of the webinar can be found at the U. R. L. listed here.
Which is our wiki page for the imaging Informatics webinar so, yeah, we can see your slide. I'm sorry. Okay. Let me see if I can share again.
Sorry about that. Yes. Thank you. Okay. What you have not seen but you may know is up here. That's the U. R. L. for the imaging data comments.
And the 2nd, on this page.
Is the webinar wiki page that I just mentioned?
The imaging data comments team also presented at the hours in a meeting.
On December, 3rd, and as stated earlier, we have the minimal viable product out for the public.
To look at to test and comments and suggestions for new features are very, very welcome. Please submit them.
Either to discourse, which you can reach by going to the portal page and then clicking on the discourse link in the top menu.
Or you can also send an email to support at cancel.
And this will reach the team as well.
I just wanted to remind you at this point,
when you start working with the imaging data comments,
there are free Google Cloud credits available to you and how you can make use of them is spelled out as detailed in the imaging data comments user guide.
On the requesting cloud credits.
And if you have questions again, back, you can email the team using this E, mail address.
Our last topic for today is just a reminder of all these resources that are available to, you.
The dial in information and agenda of upcoming meetings can be found on the.
On the wiki page as can be the recordings.
Our next call would be January 4th, but given the holidays.
And the new and the new year, we have decided to cancel.
The January 4th call, and then our next call will be on February 1st, 2021, and join us.
We'll talk about orchestration of distributed imaging archives.
K1 back to you. Great. Thank you so much. We'll be on Justin for those updates. Okay. Uh.
Well, we had a good attendance today. Thanks to our colleagues from. We have.
To be twice as many.
Typical within these that we have number of attendees, so.
Great presentation. I want to. Thank the presenters, uh, from an ID and.
Do it in these orders with state around.
Thank you for your interest and and support of these imaging.
Informatics community Webinars and of course, what a thank MS. only ragner for managing and hosting the series.
Are we all are happy and safe holiday season see you in 2021. thanks. Everyone.
Thank you. Thank you so much. Thank you. Bye.