Research
Dream Team Engineering’s Research Team tests the usability, scope, and impact of DTE projects. Our teams use a combination of dry lab techniques, including data acquisition through patient surveys, professional writing, and statistical analysis, to determine the efficacy of DTE projects being used within the hospital.
Surgical Efficacy Research
The DTE Surgical Efficacy Research Team is responsible for designing and conducting survey-based research studies surrounding the training effectiveness of the kidney transplant and cholecystectomy models designed by the DTE Surgical Design Team. Our team's goal is to test the efficacy of those surgical training models. We have access to a population of first year medical students in Shands, and we will be testing how well our surgical models increase resident knowledge and confidence through a combined quantitative and qualitative survey-based study.
Neurospine
Neurospine is a DTE research team focused on expanding AI-powered tools that spine surgeons can use to better assess vertebral bone quality prior to surgery. Current preoperative bone assessment relies heavily on DEXA scans, where a T-score of –2.5 defines osteoporosis and –2.4 defines osteopenia. This cutoff creates a significant clinical gray area: patients who fall just above the osteoporosis threshold may not qualify for treatment, despite having bone that may still be too weak to support spinal instrumentation. This nuance is critical in spine surgery, where screw fixation strength and fusion success depend heavily on bone quality.
Cardiothoracic Research
We write the research design papers for the virtual reality, 3D modeling team, and the Berlin Heart models created by the DTE Cardiothoracic Design Team. The Berlin Heart Research Initiative studies the impact DTE’s 3D-printed Berlin Heart model has on the patients of Shands Hospital. Our team recently wrote a design paper on the Berlin Heart legacy project from the Cardiothoracic Design Team that is now awaiting approval into the Journal of Medical Internet Research (JIMR).
DBS Meta-Analysis Research
Our team is currently studying how machine learning tools can/are being utilized to optimize deep brain stimulation procedures that treat the neuromuscular symptoms of Parkinson’s. MER is used for accurately targeting brain regions during DBS.
Translational Osteoarthritis Diagnostic Team
SEKO (Segmentation of Experimental Knee Osteoarthritis) is a python based machine learning pipeline developed to automate the analysis of histological slides of osteoarthritic joints. We will expand SEKO’s scope by improving its anatomical coverage from medial tibia segmentation to whole-joint segmentation, incorporating human histopathological data for model retraining and validation, identifying new OA biomarkers such as osteophytes as a new predictive feature, and improving preprocessing, normalization, and tile handling to support large-format histological slides.