Healthcare
❯
Predict patient outcome
❯Medical Assistants
❯Instant triaging of wounds
Predicting relapse of a dialysis patient during treatment
For:
Dialysis nurses, dialysis patients, partner hospitals
Scope:
Build an AI solution to augment dialysis nurses
Goal:
Improved Customer Experience
Instant triaging of wounds
For:
Wound nurses, diabetes patients, hospitals
Scope:
Build an AI solution to augment triaging decisions by wound nurses.
Goal:
Improved Employee Efficiency
Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.
Goal:
Improved Employee Efficiency
Applying machine learning to predict patient risk for in-hospital new infections
For:
Hospitals
Goal:
Improved Customer Experience, Anticipate Risks
Instant triaging of wounds
For:
Wound nurses, diabetes patients, hospitalsGoal:
Improved Employee EfficiencyProblem addressed
Use AI to identify and classify the intensity of wounds.
Scope of use case
Build an AI solution to augment triaging decisions by wound nurses.
Description
A wound nurse is the first line of medical attention when a
patient comes to the hospital suffering from serious external
wound injuries. The problem is more chronic in diabetic
patients. The wound nurse is expected to spend time to view
the wound and decide how to triage the seriousness of the
wound before sending the patient to the doctor. A CV model
was built that can use a 2 megapixel mobile camera and off-
the-shelf IR camera attachments to visualize wounds within
seconds, to help the wound nurse make faster and more
consistent triaging decisions.
Live Video
AI: Perceive