Case studies

Can Planimetry Help With a Better Prognosis of a Unique Wound Healing Processes?

Deep learning can improve doctors work, we know how to do it.

Imagine you are a kid again. For sure while playing or running you hurt yourself at least once in a lifetime.

You noticed that the shape and size of wounds change as the healing process progressed. There are, wounds which treatment process is not as simple as the common. Happily, planimetry IT tools can support the monitoring of healing efficiently.

 

CHALLENGE

First of all, at each stage of the healing process, wound’s surface, shape or color have specific parameters and a different look. Especially, if the wounds are inflicted with a blunt tool, they are large or requiring hospitalization.

Specific parameters and a different look of wound’s surface, shape or color.

Observing whether the healing process is proceeding correctly or not, is a common problem for many patients and their doctors. The challenge was to code a tool which automatically monitors and predicts the whole process.

RESULTS

In order to facilitate the automatic monitoring of the healing process, Stermedia create a dedicated deep learning software application.This system base on neural networks, machine learning mechanisms, and image recognition in order to assure effective treatment.

First of all, you load a photo of the injured surface from your mobile device and makes a contour of the wound shape. Secondly, you can add elements like patient data, dates of consultations and medical comments at individual stages of the treatment process. The more extra information, the better. After some time you load the next photo. The system shows changes in the wound healing process throughout the entire process. Finally, the physician can see how the outline or color of the area has changed.

 

An intuitive app panel for physicians.

Based on the system, a physician makes a better prognosis.

At an early stage, as the patient, you can get the full range of information and better prepare for future treatment. You will get to know:
1) if the wound is proceeding in the right direction
2) if not, what are the anomalies of the normal healing processes in your case
3) what changes in the treatment are necessary to bring the expected results

To sum up, the tool developed by Stermedia.ai can be use by clinics and physicians dealing with the treatment of many types of wounds.

It increases the efficiency and efficacy of care. In the future, this versatile deep learning tool can be used, among others for fast analysis of emerging skin lesions in terms of early detection of skin cancers.

 

 

 

 

The machine learning methods are good solution for cancer diagnosis

https://medium.com/datadriveninvestor/wound-healing-progress-prediction-through-machine-learning-aa05c3b50b35


Marta Miszczak
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