Inspiration
Many students and peers in our community struggle with mental health disorders. The treatments they have access to, however, do not effectively alleviate their symptoms, nor target the root of the disorder. These ineffective solutions leave patients in a state where they face difficulties in close relationships, their ability to learn is impacted, and they may even face physical manifestations of health problems. Despite these consequences, only 13.4% of young adults in North Carolina who require treatment from mental health professionals actually improve. In fact– North Carolinians are over 7x more likely to be forced to seek mental health care outside of their current healthcare provider when compared to primary health care. These statistics are staggering, and indicate that a serious change must occur in the current approach towards mental health treatment in North Carolina.
After having been exposed to this information, we were inspired to create Focal Point, a website that targets the diagnosis and treatment of mental health disorders which are prevalent in our communities today. Focal Point strives to provide access to customized diagnosis reports and treatment plans that will benefit the lives of individuals across the globe. We hope that this website will take the next step towards curing mental health disorders, and improve the lives of people in our own communities that struggle with maintaining a positive outlook on life.
What it does
Focal Point provides a unique and cutting edge solution to a major obstacle in access to mental health treatment. Most treatment plans today are designed based upon a clinical diagnosis which an individual receives, not the symptoms that are specific to them. Mental health disorders have heterogeneous presentations, and while some symptoms may align with the diagnostic criteria mentioned in the Diagnostic and Statistical Manual (DSM), treatment for mental health disorders do not alleviate symptoms outside of strict clinical criteria.
Therefore, our app utilizes a unique data processing algorithm that measures the prevalence symptoms users exhibit to develop a unique treatment plan specific to each individual! The symptoms measured on our diagnostic test are not specific to one disorder– they include common effects of diverse mental health issues. In addition, our state-of-the-art diagnostic algorithm combines neuroimaging scans which users upload, and based upon aberrant connectivity detected on those scans through synchronous processing, a treatment plan is developed. We currently have a database of treatments that specifically alleviate certain symptoms, such as medications, types of therapy, and day-to-day changes which a user can implement. This application will not only allow users to seek access to treatment specific to them, but additionally encourage them to make changes in their lifestyle which will additionally mitigate the symptoms of mental health disorders.
How we built it
Our website was constructed using a flask framework that combines Python, HTML, and CSS to develop the functionality and User Interface. For certain elements of the website’ UI– such as the navigation bar, Bootstrap frameworks were utilized. We additionally combined Werkzeug and NumPy to develop a novel image recognition algorithm that is able to detect the presence of aberrant braun connectivity in neuroimaging scans. Finally, our state-of-the-art diagnostic test uses a FlaskForm to collect user data and recommend a customized treatment plan.
Challenges we ran into
The biggest challenge that we faced while developing the algorithms behind our website include integrating NumPy to build an image recognition software. By collaborating with one another, as well as receiving guidance from technical advisors, we were able to overcome this challenge through iterating over pixels in an image and then applying conditionals to each pixel. By working through this challenge, we were able to understand the significance of collaboration in software development. Combining unique perspectives allowed us to integrate a much more efficient algorithm when compared to solutions we would have developed if we worked individually.
Accomplishments that we're proud of
Throughout the course of this hackathon experience, we found that we’ve grown both as programmers and as individuals. We’ve been able to build and develop our programming skills by brainstorming technical ideas, collaborating with one another, and by researching cutting-edge features to make our software more efficient. This hackathon gave us our first insight into machine learning through understanding the NumPy module, and we gained a significantly deeper understanding of flask. We are additionally very proud of the fact that we were constantly willing to learn from one another and be flexible with integrating new ideas as we gained more information. Therefore, we feel that the growth that we’ve undergone, as a result, is our biggest accomplishment from this experience.
What we learned
Throughout this experience, we learned more about developing a flask application, as well as image recognition utilizing Python (through the NumPy Library). We gained a better insight into synchronous and asynchronous processing, as well as how these could be applied to our website. Through collaborating and capitalizing on one another’s strengths, we learned the importance of working on a team and brainstorming ideas with one another. Additionally, we found that we were able to gain perspective on mental health disorders, the factors that affect the treatment of these disorders, and an understanding of the struggles faced by individuals with mental health disorders. We hope to carry forth the knowledge we learned through this hackathon to make a lasting contribution to the mental health community.
What's next for Focal Point
Focal Point is currently looking into expanding the image recognition software to not only identify the presence of aberrant brain connectivity, but additionally locate which region of the brain is impacted by comparing a user’s uploaded Neuroimaging scan to an anatomical template mask. As different regions of the brain are impacted across a variety of mental health disorders, Focal Point will be able to identify which symptoms a user may experience based on which brain networks are impacted, and recommend treatment through combining scans and the diagnostic test. We are additionally planning to increase the amount of treatment plans which Focal Point will be able to identify by adding a larger bank of possible symptoms, and working with clinical psychiatrists to integrate cutting-edge treatment options such as deep brain stimulation and transcranial magnetic stimulation.
Log in or sign up for Devpost to join the conversation.