An improved way of delivering PDT (Photodymanic Therapy) for surface level lesions. The idea is to create a modified array of LED lights (two types with spectral peak differences of 10 - 15nm), and vary the current through the lights slightly to produce an exitation light custom to each patient. Blue light photos will be taken of the treatment area after each treatment to track progress. The goal is to along with the doctor’s qualitative input, implement a machine learning algorithm to deliver the most ideal spectrum of light to the patient depending on their previous progress, required light penetration depth, and the similarity of physiology with other patients.

How we built it

Web app built using Angular, Node.js, MongoDB Machine learning algorithm implemented using Python Scipy Image processing implemented using Python PIL Hardware built using Arduino

Challenges we ran into

Had to change language from MATLAB to python with less than 24 hours left due to compatibility issues

Accomplishments that we're proud of

Built circuit from scratch Machine learning algorithm built from scratch Built custom imaging algorithm One guy did the whole front end and back end of the web app

What we learned

New languages: syntax, libraries associated with them

What's next for Photodynamic Therapy

Optimization to be done with more data and fine tuning algorithms for edge cases More realistic patient trials

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