And our final paper for the afternoon will be presented by Dr. Haboub, who will discuss the neurosurgery ancestry tree. Thank you for having me. My name is Ghais Haboub. I'm a fifth-year neurosurgery resident at the Cleveland Clinic. I've been working on this project since I was a second-year, which we were calling the Neurosurgery Ancestry Tree. So it's been 100 years since Harvey Cushing became a neurosurgeon at Johns Hopkins Hospital. And since then, the neurosurgery history expanded exponentially. History can be stored in the form of web pages, publications, or even human memories. Throughout history, though, several historical events and data were lost. For example, the FIFA World Cup trophy, the initial one, was lost, and so are some other examples here. The point here is we make a lot of data, but history tends to get lost. Graphs tend to store data in a condensed format with less uncertainties. For example, this is Napoleon. This is a graph showing Napoleon's failed attempt to invade Russia. And you can see the size of the army shrinking while they're traveling. You can basically describe the graph in several paragraphs, or you can just look at it. So the goals of our study was to preserve neurosurgeons' training history, create a visualization tool to store neurosurgery ancestry data, and provide these visualizations and analytical tools to everybody. Initially, when we decided to start the project, we only wanted to look at the chairman, just to make the project more reasonable. We looked at the geographical location where they were practicing, who they trained and trained under, and we collected data from several sources, including emailing departments, talking to people in publications, and so on. As far as storing data, you can store it in unstructured format, but it tends to get difficult to parse the data. You can store it in a relational database, so it's difficult to get relational data, actually, from relational databases. Or you can even store it as a JavaScript object notation, but it's still not the best tool. So what should we do? So basically, we decided to convert it to a graph. So here, for example, we have Dr. Benzel as a node, and Dr. Larson as another node, and you can make a connection between them through an edge. You can convert this into relational edges or connections. And so we collect data on 162 chairmen, and here, basically, this graph, so you can upload all these data into the server, and it will automatically create the graph. Blue represents all the connections, and when you hover over the name, it will convert to green and purple, represent trained and trained under, respectively. This is a zoomed-in version of the tree, and you can see Charles Locke, our first chairman at the Cleveland Clinic, trained under two people outside the view here, which are Harvey Cushing and Howard Navsekar. The important two points here is we still are trying to figure out the best way to update the database and where to host the server, and I think these tend to be very important if you want to maintain this project, keep it alive. So in the current format, the project is more in two dimensions, but if you want to add more people, you'll probably have to convert it into three dimensions, and that's probably where virtual reality can come in, and you can see here a flying tree, but the point is you can easily convert it to a three-dimension, which will add capability, add much more data to it. For visualization, for storage, you can add as many as you want, but to be able to see it in a meaningful way, virtual reality can be a very good tool. So imagine if you have an entry department, you have a virtual reality headset, you can just put it on and you can visualize a tree at any point. And as far as, we could have used some of the graph analysis tools, including social media network analysis, for example, degree centrality and between centrality, but these tend to be not very clear of utilization here for this project. But our goal eventually, not only look at Sherman, look at all neurosurgeons, but it will be a challenging task to make all these connections. Eventually with the data more complete, we can look at sub-graph analysis, for example, you can look at all Ohio trained neurosurgeons, and you can basically see their distribution throughout the world, and so on. And this is a link for the website, we made it available to the public for convenience. And so these tools, to the best of my knowledge, have never been used to create an ancestry tree, so we as a neurosurgery society, we can be the first to adapt these technologies. Some references, and thank you for having me.

neurosurgery residency

neurosurgery ancestry tree

visualization tool

virtual reality technology

technology applications