A tool using artificial intelligence for early detection of skin cancer is being developed with the participation of the Research Institute for Computer Science and Automation (SZTAKI) of the Eötvös Loránd (ELKH) Research Network.

The goal of the iToBoS project is to create a new diagnostic tool for early detection of skin cancer that uses all available patient information – read the SZTAKI communication. According to the institute, this comprehensive assessment tool should understand the specific characteristics of each patient, paving the way for early personalized detection of skin cancer.

Skin cancer is the most common and most common malignancy in humans. Within the general category of skin cancers, 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.

In this case, more than 90 percent of melanoma patients are still alive after 5 years. If the cancer cells have spread (metastatic melanoma), the proportion of patients alive after 5 years is 23 percent or less. Therefore, rapid diagnosis is essential to carry out treatment before local spread and metastases, as SZTAKI emphasizes in its statement.

The goal of the iToBoS project is to create an artificial intelligence (MI) system capable of integrating information from a variety of sources, from endoscopic images to complete medical records to genetic data.

As part of the program, a new diagnostic tool is being developed, and a cognitive assistant using MI is also being developed. 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.

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Algorithms will integrate additional patient information from various sources (such as the patient’s medical history, genomics, location of all moles, age, etc.) to provide a comprehensive and comprehensive assessment of each mole, taking into account the specifics of each patient.

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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