- Department of Neurosurgery, Maastricht University Medical Center, The Netherlands
Pieter L. Kubben
Department of Neurosurgery, Maastricht University Medical Center, The Netherlands
DOI:10.4103/sni.sni_129_17Copyright: © 2017 Surgical Neurology International This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
How to cite this article: Pieter L. Kubben. NeuroMind: Past, present, and future. 06-Sep-2017;8:216
How to cite this URL: Pieter L. Kubben. NeuroMind: Past, present, and future. 06-Sep-2017;8:216. Available from: http://surgicalneurologyint.com/surgicalint-articles/neuromind-past-present-and-future/
This narrative report describes the underlying rationale and technical developments of NeuroMind, a mobile clinical decision support system for neurosurgery. From the perspective of a neurosurgeon – (app) developer it explains how technical progress has shaped the world's “most rated and highest rated” neurosurgical mobile application, with particular attention for operating system diversity on mobile hardware, cookbook medicine, regulatory affairs (in particular regarding software as a medical device), and new developments in the field of clinical data science, machine learning, and predictive analytics. Finally, the concept of “computational neurosurgery” is introduced as a vehicle to reach new horizons in neurosurgery.
Keywords: Clinical data science, machine learning, mhealth, mobile, predictive analytics, programmng
After introduction of the iPhone (2007) and the App Store (2008) I immediately loved the device and its refreshing approach towards mobile computing. Obviously, I wanted to have my grading scales and classification systems with me on this new device to support my “palmomental reflex.”[
The main disadvantage of NeuroMind 1.x was that it remained fully static. While each minor update contained some more classification systems, the format remained text-only and let the end user do the math. In 2011, I gave a TED talk explaining my thoughts on clinical decision support systems.[
In 2014, increased attention was drawn to regulatory affairs for mobile medical applications, in particular focusing on the question when they needed to be considered as a medical device. The FDA released its first non-binding guidance on mobile medical applications back in 2011 with an update in 2013.[
By 2015, I started losing motivation for further development of NeuroMind. A sense of “been there, done that” came up frequently and I was wondering how to continue. Meanwhile I started learning the Python programming language in the context of data science, to which I will refer later in this article. Python is relatively easy to pick up and an excellent language to start learning programming. For this reason I created the website
Having found back the joy in iOS development I decided to rebuilt NeuroMind from scratch, using Swift and XCode. The obvious side effect would be that Android users would not benefit from this update, but for two reasons I did not mind at the time. First, the download figures suggested that there were many more iOS users than Android users for NeuroMind. Second, I decided to offer some In App Purchases to cover for my development-related expenses and future investments. This was easier to do when focusing on one platform.
Early 2016, NeuroMind 3.0 for iOS was released [
NeuroMind.cc is the current version of NeuroMind, and is available on all devices and all screen sizes. All content is available free of charge. Moreover, it is also directly accessible from any modern web browser on desktop or laptop using the weblink
Three directions are important for the future of NeuroMind: content, technical, and regulatory. Regarding content, my intention is to add more decision support modules. The advantage of running Google Analytics is that I can view which content is being used most, and hence could benefit of more support and interactivity. Such content could be developed by me, but also by others who wish to contribute to NeuroMind. Current interactive content is based on simple calculations or flowcharts. Therefore, all content flow can be derived from studying the source code. In contrast to this so-called “white box” approach, further technical development may also allow for “black box” approaches in which machine learning techniques are applied for predictive modelling. This field is also referred to as (clinical) data science. The current platform would easily allow to integrate such content, that may be generated in Python or R.[
Besides an overview of NeuroMind development, underlying philosophy and practical considerations of the world's “most rated and highest rated” neurosurgical mobile application,[
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