Healthcare
❯
Treatment recommendation
❯Medical Assistants
❯AI-based mapping of optical to multi-electrode catheter recordings for atrial fibrillation treatment
Support system for optimization and personalization of drug therapy
For:
Public and private healthcare system, pharmaceutical companies
Scope:
This system is a full range of integrated solutions for the selection of the optimal
type of drug, its dose, and its combination with other drugs.
Goal:
Other
AI solution to predict post-operative visual acuity for LASIK surgeries
For:
Hospitals, patients undergoing LASIK surgeries.
Scope:
Predicting post-operative visual acuity for laser-assisted in SItu keratomileusis
(LASIK) surgeries from retrospective LASIK surgery data with patient follow-
ups.
Goal:
Automate a Business Process
Improving the knowledge base of prescriptions for drug and non-drug therapy and its use as a tool in support of medical professionals
For:
Doctors and patients
Scope:
Providing the medical professional with methods and means that would allow,
within the time allotted for the appointment of 0 patient with a known nosology,
to make a high-quality choice of drugs and to formulate a prescription
corresponding to good medical practices
Goal:
Improved Employee Efficiency
AI-based mapping of optical to multi-electrode catheter recordings for atrial fibrillation treatment
For:
Hospitals, cardiologists
Scope:
Predicting possible targets for atrial fibrillation ablation based on explanted human heart data of two modalities (multi-electrode mapping and near-infrared optical imaging)
Goal:
Improved Product Development / R&D
WebioMed clinical decision support system
For:
End-users (physician, nurse, laboratory technologist, pharmacist, patient)
Sales and marketing team
CDSS product development and maintenance team (system administrator,
system developer, system architect, project manager, and system
maintenance)
Scope:
Screening for cardiovascular disease risk prediction with machine and deep
learning methods
Goal:
Other
Integrated recommendation solution for prosthodontic treatments
For:
Dentist
Hospital
Scope:
In order to support complicated prosthetic treatments according to the patient's
condition, the artificial intelligence technology provides a comprehensive
analysis of the given information and situations to recommend various
prosthetic treatment methods and visualize them to support doctors and
patients.
Goal:
Improved Employee Efficiency
AI-based mapping of optical to multi-electrode catheter recordings for atrial fibrillation treatment
For:
Hospitals, cardiologistsGoal:
Improved Product Development / R&DProblem addressed
Given: Recordings from multi-electrode catheter grid, with ground-truth labels from near-infrared optical mapping, obtained from explanted hearts.
Output: Possibility of recordings to be from source (driver) region of atrial fibrillation.
Scope of use case
Predicting possible targets for atrial fibrillation ablation based on explanted human heart data of two modalities (multi-electrode mapping and near-infrared optical imaging)
Description
Atrial fibrillation (AF) is the most common cardiac
arrhythmia and the leading cause of stroke. The success rate
of current AF treatment is low, 50-70 %. Several
experimental and clinical studies suggest that AF may be
caused and maintained by micro-anatomic intramural re-
entry called drivers. Physical destruction of the driver, or
driver ablation, leads to the termination of AF. Unfortunately,
the current clinical method to look for drivers (MEM) suffers
from many limitations, including poor resolution and only-
surface tissue mapping. On the other hand, near-infrared
optical mapping (NIOM) has one thousand times higher
resolution and records electrical activity from the depth of
atrial tissue (up to 5 mm), but needs specific voltage-
sensitive dye to color the tissue and therefore can be used
only for explanted specimens. For our research, we used
unique data of the experiments with explanted human atria
from Ohio State University simultaneous recordings of AF
episodes by MEM and NIOM. In this work, we predicted the
possibility of AFib drivers to be visible in the MEM recording
as trained by the Optical ex-vivo data. We created the
machine learning classifier with ground-truth labels based
on NIOM maps. As features, we used characteristics from the
Fourier spectra of MEM recordings. Our experiments on a
dataset of more than 20 000 spectra provided an accuracy
and f1-score of 97,3 % and 0,89, respectively.
Machine Learning
AI: Understand