| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 8
| Issue : 1 | Page : 29 |
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MSLO – Melanocytic skin lesion ontology
Karolina Szturo1, Jens Haueisen2, Lukasz Piatek3
1 Department of Systems Engineering, Faculty of Technical Sciences, University of Warmia and Mazury, Olsztyn, Poland
2 Institute of Biomedical Engineering and Informatics, Technical University of Ilmenau, Ilmenau, Germany
3 Institute of Biomedical Engineering and Informatics, Technical University of Ilmenau, Ilmenau, Germany; Department of Artificial Intelligence, University of Information Technology and Management in Rzeszów, Rzeszów, Poland
Correspondence Address:
Karolina Szturo
M. Oczapowskiego 11 Street, Olsztyn 10-719
Poland
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_18_22
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Background and Purpose: Malignant melanoma is a high-grade skin cancer with high feasibility to metastasize to both regional and distant sites when detected late. Therefore, it is crucial to diagnose this type of cancer at an early stage to ensure effective treatment. The identification of melanocytic lesions is a difficult issue, even for experienced experts. The current development of information technology, particularly related to image analysis and machine learning, is an opportunity to support the work of specialists and detect malignant melanoma more effectively. The aim of this work is to present a melanocytic skin lesion ontology (MLSO) structure, which serves as a basis for a melanoma diagnosis system and includes the formalization of the experts' and literature knowledge. Subjects and Methods: MLSO describes the most commonly used melanoma assessing strategies: Argenziano's (also known as the 7-point checklist), Menzies', and Stolz's (based on the ABCD rule) strategies as well as Chaos and Clues. Results: In this work, a case study was conducted on the description of a dermatoscopic digital image of a melanocytic skin nevus. The nevus was evaluated according to all of the strategies included in the MLSO, and inferences were made based on these strategies. The analyzed lesion was classified as a benign nevus since no malignancy was indicated by any of the applied strategies. Conclusion: Initial results indicate the usefulness of MLSO in diagnosing skin cancer. A significant advantage of MLSO is that it provides results obtained using four strategies. Therefore, the results are more objective and the possible errors may be avoided. The MLSO structure is still developing and will be implemented into an automated skin cancer diagnosis system.
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