Regulatory Strategy Development for Smartphone-Based Malaria Detection

The project aims to develop a comprehensive regulatory strategy for Meiotic Inc.'s innovative smartphone-based malaria detection technology. This project will focus on navigating the FDA approval process and designing an early-stage clinical trial. The goal is to ensure that the product meets all regulatory requirements for market entry in the United States. Students will conduct in-depth regulatory research to understand the FDA's pathway for medical devices, particularly those utilizing smartphone technology. They will map out the submission route, identifying key milestones and potential challenges. Additionally, students will propose a design for an early clinical trial, considering factors such as participant selection, trial duration, and data collection methods. This project provides an opportunity for learners to apply their classroom knowledge of regulatory affairs and clinical trial design in a real-world context.

Data Analytics for Life Science Sales Platform

This project aims to analyze data from vablet’s pharmaceutical sales platform to identify key trends and insights regarding sales representatives' engagement with sales materials. Students will explore usage patterns, determine which materials are most frequently accessed, and provide data-driven recommendations to optimize content strategy. By leveraging data analytics techniques, students will help Meiotic understand the effectiveness of their platform in driving physician engagement while enhancing their skills in real-world business intelligence and data visualization.

AI Model for Medical Image Analysis

This project will involve developing an AI model to analyze a dataset of approximately 27,000 anonymized eye images collected during the COVID-19 pandemic. The model aims to detect and diagnose medical conditions such as hypertension and diabetes based on retinal images. By participating in this project, students will gain hands-on experience in machine learning, deep learning, and medical image analysis while contributing to a real-world healthcare application.

Strategic AI & Machine Learning Application

We want to leverage the latest technology to gain an advantage in our market. We would like to collaborate with students to strategically apply AI and machine learning in our organization. Through using internal (Azure AI) and open source data (Tensorflow) analytics models, methodologies, and machine learning tools, we hope to get an end-to-end machine learning solution. Students should be prepared to: Identify and present an opportunity within our organization, which AI could be applied. Develop a unique machine learning solution, based on the the latest technology. Create a report that provides an in-depth overview into the AI solution. A few areas of specific interest for us include: Search Recommendation Engine (Imagine you are looking for content and you do not know the name of it, part number you only have a few tags) Content Recommendation Engine (Our clients use vablet as presentation tool, what content should be shown next, or email as part of a follow/up) Email Recommendation Engine (which files when emailed are being engaged with. We track open, clicked on duration, much more). Image Recognition (very specifically, is the image just taken a close of up an Eye and is the Sciera in focus) Our data is currently captured and stored in SQL Server, and can be exported to CSV or can be pulled via an API. People talk about AI Powered Sales - Typically this means that the software is making content recommendations, this is our goal as well. This is a complex task because we only know part of the data. We know what content was shown, emailed, but to determine if the presented content actually resulted in the closing of a deal, you need CRM data. We have access to some CRM data but not 10K rows.