NNPC 2018 Science Quiz Champion, Tony Kabilan Okeke, Five Others Develop AI Medical App In USA

Tony Kabilan Okeke

By Frank Oshanugor

In spite of leadership challenges besetting Nigeria as a country, Nigerian students in different parts of the world keep making impressive strides in different disciplines.

Couple of weeks ago,
20-year-old Tony Kabilan Okeke, who won the national champion trophy of the 2018, NNPC national science quiz competition, currently studying Biomedical Engineering in Drexel University, Philadelphia, Pennsylania, USA, collaborated with five other international students (four Nigerians and one Indian) to develop an Artificial Intelligence (AI) Medical Assistance App, which they named Meddibia.

Okeke’s team, named, Team Meddibia, comprises Tony Kabilan Okeke, Dishika Goel, Elochukwu Enwerem, Dalu Okonkwo, Michael Moemeke and Victor Uzo, all of whom are international students in the US.

The team’s project, Meddibia, was one of the ten AI projects that won at the Philly Codefest 2023, a contest that took place, on 12th March, in Philadelphia, Pennsylania, USA at the event of the 10th year of the software and hardware hackathon hosted by College of Computing & Informatics, Drexel University, Philadelphia.

On the 12th of March close of the 2023 Philly Codefest, held at Quorum within the University City Science Center, a group of judges made their way around the room, listening intently to each group as they presented on the project they’d developed over two days. “The judges then moved to a separate room to determine a winner, which took longer than expected, given how impressive all the projects were this year,” said Dave Raiken, Assistant Director of Operations, Events and Logistics at the event hosted by Drexel University’s College of Computing and Informatics (CCI). Codefest just celebrated its 10th year, once again welcoming students and professionals of all skill levels across the US to the weekend-long hackathon.

This year’s Comcast-sponsored event saw 46 teams create technical projects aligning with the theme, “AI Everywhere” — that is, “real-world, scalable software and hardware solutions to improve and expand artificial intelligence’s positive societal impacts.”

Participants started coding on Saturday morning March 11, 2023 and had until 6 p.m. that day to submit their idea. Then, they had until noon on Sunday, 12th March to complete their project before judging.

Presenting on the project, Meddibia, Tony Okeke on behalf of his Team members, explained as follows, starting from their inspiration for the project:

“MEDDIBIA: Your Personal Medical Assistant


The inspiration behind our latest project came from a deep desire to improve the accessibility of medical care for people living in rural areas. As developers from third-world countries ourselves, we understood all too well the struggles faced by those who lack access to proper medical facilities.
Our team wanted to create a solution that would bridge the gap between these individuals and medical professionals, so we set out to create an app that would allow users to receive medical advice and diagnoses using machine learning models.
By inputting their symptoms into the app, users can receive predictions for potential diseases and conditions, allowing them to make informed decisions about seeking medical treatment. We believe that this app has the potential to be particularly beneficial for those in rural areas who may not have easy access to medical doctors or facilities.
For us, this project was an opportunity to use our skills and expertise to make a real difference in the lives of people in our own communities and beyond. We’re excited to continue developing and improving this app and to see it make a positive impact on the world.

What it does

MEDDIBIA is a personal AI health assistant that puts greater control over health in the hands of users living in rural communities. With MEDDIBIA, users can describe their symptoms to a chat assistant and receive a likely diagnosis, along with more information about their diagnosis and symptoms. Additionally, MEDDIBIA enables users to get diagnosis for skin conditions and aberrations by simply taking a picture of the affected area. This feature is especially important for those with limited access to healthcare professionals or specialized facilities. By providing personalized care and making it easier to manage health conditions, MEDDIBIA empowers users to take control of their health and improve their quality of life.

How we built it

Our team utilized cutting-edge machine learning techniques to develop an innovative solution for identifying and diagnosing skin conditions and diseases. To accurately identify skin conditions, we experimented with various pre-trained models, including VGG16 and EfficientNet, to extract features from images from the dermnet dataset. We then trained and evaluated deep neural network classifiers, ultimately selecting a model with approximately 70% accuracy. For symptom identification, we employed GPT-3, a state-of-the-art language model, to preprocess natural language input from users into symptom labels, which served as input to our machine-learning model. This approach resulted in about 87% accuracy in predicting disease labels. To further assist users, we used GPT-3 to provide helpful descriptions of the predicted disease. Our app’s backend was built using Flask API and deployed on Heroku, while the cross-platform frontend was developed using Flutter, making our app easily accessible to users across multiple devices.

Challenges we ran into

The construction of MEDDIBIA was an interesting and challenging task. The first problem we encountered was locating suitable datasets for our machine learning algorithms. We needed to obtain a dataset with over 40 diseases and appropriately identify them using their symptoms for our disease classification algorithm. To maximize machine learning, we needed to obtain a dataset with rich photos for our skin disease detection model. The next problem was to discover effective machine learning techniques to use with our dataset to produce models. To acquire accurate findings, we needed to determine the machine learning technique that performed best with our dataset. Another difficulty we encountered was integrating our machine models to our mobile application. Creating a machine learning model is one thing, but we also needed to guarantee that our model was user-friendly and easily accessible via our application. Constructing MEDDIBIA was difficult, but we were able to overcome the obstacles that the journey posed in order to complete our project, MEDDIBIA.

Accomplishments that we’re proud of

We are proud of the knowledge we gained from this experience. Building MEDDIBIA was challenging and the process allowed us gain useful knowledge on several technologies. In addition. We are proud of being able to create such a rich project within 24 hours. In 24 hours we were able to create a project that uses machine learning to predict diseases users might have, using their symptoms as values.

What we learned

We learned how to integrate flask APIs to a Flutter application while developing MEDDIBIA. Our application is built on Flask, which connects machine learning models to a Flutter application. We discovered how to leverage openAI APIs. Our application makes advantage of openAI APIs to fine-tune user input before passing it to our model. We also learned about the multinomial naive bayes machine learning method, Flutter, and Flask.

What’s next for MEDDIBIA

Looking to the future, we plan to patent the app and suggest its usage to nearby hospitals and clinics for potential treatments and connect patients directly with healthcare providers through MEDDIBIA. These features will personalize care, streamline the process of accessing medical assistance, and provide the latest treatment options. We are excited to continue our work in revolutionizing healthcare and improving health outcomes for our users.”

Out of the 40 entries submitted to Phily Codefest 2023 at Drexel University by Comcast, Team Meddibia emerged the winner Philly Codefest 2023 Collaborative Team Award with a price money of USD5000.

It will be recalled that Tony Kabilan Okeke came to national limelight in 2018 when, at 16, he emerged the national champion in the prestigious Nigerian National Petroleum Corporation (NNPC) National Science Quiz Competition, beating over 14000 other Nigerian science student participants. He went ahead to haul 8 As (distinctions) in 2019 WAEC Exams, beside several Distinctions in 2018 IGSCE (London GCE) before he headed to the US where, at 16, he was admitted for accelerated B.Sc./M.Sc. program in Biomedical Engineering in the College in Philadelphia, Pennsylania, USA.

He has continually posted a brilliant performance in his academics since his enrolment in the University, having, in the past 4 years, earned himself inclusion on the Dean’s List of the college, a list that, metaphorically, is the exclusive club of first class brains in America universities. A letter dated January 19, 2023, to Tony Okeke from the office of Paul W. Brandt, Scd, MD, DrPH, Dean & Distinguished University Professor, Drexel University, Philadelphia, Pennsylania, reads, ” Dear Tony, Congratulations. Your academic performance has earned you a place in Drexel University Dean’s list … Only a limited number of Biomedical Engineering students are able to meet the requirements for inclusion on the Dean’s list … making you a member of a select group of academically outstanding individuals.”

Earlier in 2021, Tony joined the exclusive club of US National Engineering Honor Society via a letter of invitation dated 22/10/21 from officers, members and advisors of Pennsylvania Chapter of the Society. The letter read, “Your distinguished academic record allows us to extend you an invitation for membership … You have distinguished yourself
as an exceptional Engineering student and one deserving of recognition”

As an undergraduate, he has researched and co-authored several publications with America professors published in several high profile American science journals. Some of these publications can be found at: https://scholar.google.com/citations?user=kPKT6SsAAAAJ&hl=en.

Tony is of mixed parentage, born to Emeka & Dr. Suhanyah Okeke, a Nigerian Lawyer and an Indian Surgeon.

Written by: Frank Oshanugor

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