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 that automatically monitored and predicted the whole process.
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:
To sum up, the tool developed by Stermedia 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.
Here you can read more about healing process prediction through machine learning.
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