Precision Medical Imaging
Enabling doctors to see what they've never been able to see.
GeminiDT ™ uses advanced data science so doctors can detect cancer at its earliest stages. Many times years ahead of when it can be detected with current imaging technologies.
The current protocol for cancer identification is through using medical imaging such as MRI, CT, Xray and PET scan.
The presence of a tissue anomaly is identified by the radiologist through low resolution, grey scale, flat two-dimensional pixel based visual imagery.
This visual limitation can lead to missed interpretation or inconclusive diagnosis and result in a wide discrepancy in evaluations. Many anomalies are misdiagnosed or missed entirely until the cancer has become too large or has spread, making it sometimes too late to intervene.
GeminiDT ™ embedded technology is a paradigm shift from greyscale imaging and analysis to three-dimensional Spatial Data Structure with advanced data science.
GeminiDT ™ removes the subjective, human-eye interpretation and allows the medical community to “see” anomalies through 3D spatial analysis with its Medical Condition Discovery Engine™ which examines and provides volumetric data down to the micron in near real time.
GeminiDT’s savings value proposition means less risk (less misdiagnosis) and genuine empirical data (not opinion) for cancer diagnosis in its earliest stages.
GeminiDT ™ uses a proprietary new process to transform 2D sliced scan data, CT/MRI/Xray/PET into dimensional spatial data sets.
GeminiDT ™ captures all data points contained in the slices, compiling into a mathematical spatial digital twin data model that is 100% accurate of the patient...a true digital twin. Computer assisted research can be conducted to detect the minutest value and render it for the technician to identify.
Gemini Digital Twin breakthroughs enable the medical industry to harness the power of data science and volumertic data. From historical scan data to raw data sets from CT, MRI, PET and Sonography doctors, radiologists and researchers can now develop preventitive therapies, see cause and effect on the digital twin, do comparative studies with certainty and accuracy.
The Human Anatomy project will conduct studies leveraging GeminiDT's system to publish new findings with a level of accuracy needed to answer challenges in the human physiology and early detection.
Why is GGeminiDT ™ relevant?
By compiling to spatial Digital Twin data, GeminiDT ™
allows radiologists and medical professionals to isolate single values of interest and eliminate all other data points - a healthcare industry first.
They can also compare a single value, ranges of values, or combinations thereof to existing data sets (current or historical) to quantify data trends within a single patient or across populations.
Contrast agents can be significantly reduced or will no longer need to be administered. Another industry first.
This can eliminate unwanted short and long-term side effects of these materials, especially in patients already in a weakened state, especially in cases where progressive scans over short periods need to be administered.
How do we get there?
The first crucial phase is to compile existing 2d data sets into spatial data, where research may be performed.
We are inviting groups, whether you are a medical research institute, service provider, insurance group, pharmaceutical group or medical facilities around the world to take advantage of our GDT services to launch this era of data science.
What is a Spatial Digital Twin Database?
A spatial digital twin database is a database that is optimized for storing and querying data that represents objects defined in a geometric space. In this case, human physiology captured in the CT/MRI scan from the original device.
Quantifying in emprical data ensures accuracy and is validatable .
Immersability in data sets is assured harnessing Gemini Polyplexity contanerized architecture..
Defined values which correspond to known values in a subject area can now be isolated and identified. Spatial data blocks can now be quantified and once known values have been cataloged, predictive analysis can be applied.