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Artificial intelligence for early detection of skin cancer

“This so-called comprehensive assessment tool needs to understand the specific characteristics of each patient, and thus pave the way for early personalized detection of skin cancer,” reads SZTAKI’s statement published by MTI.
Skin cancer is the most common type of malignancy in humans and is becoming more common. Within the general category of skin cancer, melanoma causes the most deaths. According to the latest statistics, skin cancer is currently the sixth most common cancer in Europe, with more than 144,000 new cases diagnosed in 2018.

Skin cancer is treatable if it is treated at an early stage. However, once the cancer cells have spread (metastatic melanoma), the chances of curing the disease are greatly reduced. Therefore, rapid diagnosis is essential for treatment to occur before local spread and metastases.
The iToBoS project is developing a new diagnostic tool, as well as a cognitive assistant using artificial intelligence.
With these tools, health care professionals may be able to make a comprehensive, patient-specific diagnosis of skin cancer, and skin cancer will be more recognizable.
“In the large-scale international project, our researchers can draw on their expertise in data management, IT clouds and artificial intelligence,” said Robert Lovas, Deputy Director of the ELKH Research Institute for Computer Science and Automation (SZTAKI).
The new diagnostic tool also uses the latest advances in artificial intelligence to facilitate the use of data already extracted using currently available technologies (dermatoscopy images) and data obtained using the new devices proposed in iToBoS.
The underlying algorithms will integrate additional patient information from various sources (such as patient history, genomics, location of all moles, age, and gender) to provide a comprehensive and comprehensive assessment of each mole, taking into account individual patient characteristics.
According to the announcement, through a regular and regular examination of the patient, the system will also be able to identify changes that occur in each mole. This is one of the most useful features in identifying skin cancer. With the proposed new approach, clinicians can diagnose dermatology earlier and with greater accuracy, thus increasing the efficiency and effectiveness of personalized clinical decision making.

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