Case studies

Can Planimetry Help with a Better Prognosis of 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 your lifetime. You noticed that the shape and size of wounds changed as the healing process progressed. There are wounds, however, where the treatment process is not as simple as usual. Thankfully, planimetry IT tools can support the monitoring of healing efficiently.

CHALLENGE

 

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

RESULTS

 

In order to facilitate the automatic monitoring of the healing process, Stermedia created a dedicated deep learning software application. This system is based on neural networks, machine learning mechanisms, and image recognition, in order to assure effective treatment. To use the app, first you load a photo of the injured surface from your mobile device and make contours of the wound shape. 

Second, you can add elements like patient data, dates of consultations and medical comments from individual stages of the treatment process. The more additional 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.

Based on the system, a physician can make 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 find out:

  • if the wound is proceeding in the right direction
  • if not, what the anomalies are compared to the normal healing processes
  • what changes in treatment are necessary to bring desired results. 

To sum up, the tool developed by Stermedia.ai can be used 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 with others for fast analysis of emerging skin lesions, in terms of early detection of skin cancers.


https://stermedia.ai/category-case-studies-cancercentre-machine-learning-methods/

 

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


Marta Miszczak
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