Completed Projects

DTE’s Caudal Block project is a collaboration with CSSALT (Center for Safety, Simulation & Advanced Learning Technologies) to design, build, and code a mixed reality simulator for surgeons and residents to practice performing a caudal epidural block on a 6 month old child. A caudal block is a procedure used to administer surgical anesthesia to children by inserting a needle through the sacral hiatus. A very delicate procedure like this requires a large amount of practice, which DTE’s simulator hopes to provide.

The Disease Reporting and Infection Forecasting Tool (DRIFT) is similar to a hurricane forecasting map - but for diseases. DRIFT uses a bioinformatics model called the SEIRD model along with machine learning to visualize disease spread predictions on a map two weeks before they happen. This will allow public health officials to make informed decisions about where to allocate resources when epidemics strike.

The CT GAN project has the goal of using Generative Adversarial Networks (GANs) to produce artificial CT scans. We are currently focusing on producing COVID positive CT scans and healthy CT scans. We are comparing a few different GAN architectures to see which produces artificial CT scans of the highest quality. Once we identify the best GAN architecture for our purposes, we can start applying it to CT scans of more rare pathologies, allowing us to augment their datasets which will help train ML diagnoses models.

Care Connect is a web application made for the volunteers at the Equal Access Clinic of Gainesville to use when looking for community resources to give to patients. When the volunteers are interacting with patients in the clinic, they can use the web application to search for the patients criteria and category of the resource in order to select the best resource for each scenario. The website is developed using React and a SQL database to retrieve the information for all the resources available.

The Predictive Diagnostics Team is creating a machine learning model to predict the readmission rates of geriatric patients at Larkin Community Hospital in South Miami. By taking in data of patients aged 65 and older we are working to predict how likely they are to be readmitted back into the hospital within the next 30, 60, or 90 days. This will help doctors better prepare for their patients to ensure they are receiving the best treatment possible.

This project sought to measure and display the volume of urine bags on an LCD screen. This past semester, the Biofluid Analyzer team completed the wiring of the electrical components and calibrated the device. They prototyped 2 iterations for each of 4 holder designs and tested 8 different holder designs in the hospital and received user feedback. The team was able to deliver the final product to Dr. Kattan at Exhibition Day and are planning on a next iteration that will potentially be able to use the FHIR API interface with Epic for data communication and storage.

The team successfully completed their third iteration of the kidney model and is set to collect research from residents as they begin using the model next semester. The kidney model will allow residents to practice suturing a donated kidney to the artery, vein, and bladder of a simplified patient model. These residents will be evaluated on the efficacy of their sutures and how they feel the model compares to current cadaveric procedures.

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The Surgical team has successfully completed the fourth iteration of their  cholecystectomy model and is also set to begin use soon by the VA Hospital’s Da Vinci machine.

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​The Air Embolism project wrapped up this semester with a complete python executable project that detects air embolisms based on live sound frequency analysis by comparing the average amplitude to a baseline. This project will be sent to Cincinnati Children’s Hospital in the near future for clinical testing to see If the algorithm will be satisfactory in a clinical setting.

The Cardiology team developed a patient education tool for heart surgical process with LVAD implant. They utilized React for displaying 3D model and AWS for automatic heart segmentation for ease of use. They created an efficient pipeline for a doctor to process CT scans and experiment with different angles of inserting a LVAD implant into 3D model of patient’s heart.

The Diabetes VR team was focused on creating a 5-level virtual reality game for newly diagnosed diabetic children. This VR game educates them on: Distinguishing between Fats, Carbs, and Proteins Calorie Counting Reading Nutrition Labels Portion Sizing Soon, the VR game will get tested and receive any final touches before the team can hopefully have it deployed for use at the hospital.

The Cardiothoracic Research team completed two of the papers that they were working on this past semester. They completed the Berlin Heart Paper and SVT Paper. These papers were about creating a patient education protocol in efforts of reducing preoperative anxiety and bettering patient outcomes. They also submitted the Berlin Heart Paper to the Interactive Medical Science Journal - Research Protocols.

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The MRI team worked alongside the child life specialists of Shands to design a smaller scale MRI machine that will replicate the sounds and bed movements of an MRI machine. The idea behind having the smaller MRI machine is for the children of Shands to experience the sounds and movements before they go into a real MRI machine. MRI machines can be daunting to children with all the different sounds and movements, the goal of this project was to alleviate the stress and fear they may have.

The Milk Bank team created a combination web app to help optimize donor milk combinations for milk banks. The app is currently going through testing at the milk bank. In the future, it may be made compatible with mobile devices. A link to the web app can be found here.​​​

The Sinus Venosus Atrial Septal Defect Model consists of a 3D-printed model of a heart defect that is developed from a real patient’s CT scan. This model will be used by surgical residents to practice on before they perform the patient’s actual surgery.​​​​

The Complex Congential Heart Disease Model project consists of a 3D-printed model of a heart defect that is developed from a real patient’s CT scan. This model will be used by surgical residents to practice on before they perform the patient’s actual surgery.

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This application serves as the basis of a research project in distraction-based therapy within Shands Hospital. The team has published, playtested, and distributed the application through the Oculus Quest App Lab. This application is primarily designed to be a highly immersive game that uses the unique aspects of virtual reality while also taking into consideration design constraints inherently imposed by the use-case situation: a child will primarily be using this before and during routine medical procedures, limited in motion in their hospital bed. Limited FOV, simple and intuitive controls, fluid motion, calming music, and immersive environments are all crucial to keeping the child comfortable. A link to the SututingVR website can be found here.

 Inspired by the Shape-O-Ball toy, the Shape-O-Heart toy project incorporates two halves of a heart, the red half representing unhealthy foods and the blue half representing healthy foods. It will be used by the pediatric patients at Shands with the intention of teaching them about foods that can both promote and weaken heart health. This will educate them on developing healthy eating habits and the importance of a balanced diet while also assisting them in improving their fine motor skills. 

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Hypoplastic Syndrome Left Heart Syndrome (HLHS) is a congenital heart defect due to the left side of the heart being underdeveloped. The HLHS model is an educational tool that will help children learn about the three surgeries involved in this process, the Norwood procedure, Glenn operation, and Fontan procedure. It is also a playing tool in which children can see the blood traveling through the heart and how each of the three surgeries tie into one another. Breaking apart the educational tool into three categories makes the information easier to understand and less overwhelming for children.​​

This project was an adaptation of the common “Melissa & Doug'' children's toy. The toy models blood flow through the heart and lungs and it will be used to demonstrate to children how unoxygenated blood is converted to oxygenated blood.​​​​

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The Lego Learning Model is a learning kit for patients to assemble a mini Lego Berlin heart. It includes easy-to-follow instructions for the pediatric patients so that they are able to build it themselves. Through doing this, these patients are able to learn more about the Berlin heart and its various subfunctions.

The Berlin Heart Team created a mini functioning Berlin Heart model that has a pumping mechanism. It's goal was to show pediatric patients who are suffering from heart failure how a Berlin Heart works and to familiarize them with the insertion procedure before they go through it. ​​​​​

"B is for Brogan: A Story about the Berlin Heart" is a children's book that was created in partnership with Dream Team Engineering's Berlin Heart Team. A link to the book can be found by clicking here.​

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The Gainesville Fire Rescue Design Team is dedicated to revamping the city’s remote-activated fire hydrants to enhance their reliability and functionality during emergencies. This initiative focuses on replacing outdated valves and actuation systems for faster and more dependable operation, upgrading signal interception and processing to ensure seamless remote activation, and implementing efficient, long-lasting battery solutions to maintain continuous functionality. ​​​

The Suture Pads team is focused on developing a tool to measure performance and evaluate potential biases in suture training. Using external sensors and data collection methods, our project analyzes how medical professionals interact with suture pads of varying skin tones. By identifying discrepancies in performance, this tool aims to promote awareness, inclusivity, and equity in surgical education and training.