دانلود مقاله انگلیسی رایگان:تشخیص اریتم مهاجر بر اساس هوش مصنوعی و ابهام زدایی در برابر سایر ضایعات پوستی - 2020
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  • AI-based detection of erythema migrans and disambiguation against other skin lesions AI-based detection of erythema migrans and disambiguation against other skin lesions
    AI-based detection of erythema migrans and disambiguation against other skin lesions

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    AI-based detection of erythema migrans and disambiguation against other skin lesions


    ترجمه فارسی عنوان مقاله:

    تشخیص اریتم مهاجر بر اساس هوش مصنوعی و ابهام زدایی در برابر سایر ضایعات پوستی


    منبع:

    Sciencedirect - Elsevier - Computers in Biology and Medicine, Journal Pre-proof, 103977. doi:10.1016/j.compbiomed.2020.103977


    نویسنده:

    Philippe M. Burlina, , Neil J. Joshi, Phil A. Mathew, William Paul, Alison W. Rebman, John N. Aucott


    چکیده انگلیسی:

    This study examines the use of AI methods and deep learning (DL) for prescreening skin lesions and detecting the characteristic erythema migrans rash of acute Lyme disease. Accurate identification of erythema migrans allows for early diagnosis and treatment, which avoids the potential for later neurologic, rheumatologic, and cardiac complications of Lyme disease. We develop and test several deep learning models for detecting erythema migrans versus several other clinically relevant skin conditions, including cellulitis, tinea corporis, herpes zoster, erythema multiforme, lesions due to tick bites and insect bites, as well as non-pathogenic normal skin. We consider a set of clinically-relevant binary and multiclass classification problems of increasing complexity. We train the DL models on a combination of publicly available images and test on public as well as images obtained in the clinical setting. We report performance metrics that measure agreement with a gold standard, as well as a receiver operating characteristic curve and associated area under the curve. On public images, we find that the DL system has an accuracy ranging from 71.58% (and 95% error margin equal to 3.77%) for an 8-class problem of EM versus 7 other classes including other skin pathologies, insect bites and normal skin, to 94.23% (3.66%) for a binary problem of EM vs. non-pathological skin. On clinical images of affected individuals, the DL system has a sensitivity of 88.55% (2.39%). These results suggest that a DL system can help in prescreening and referring individuals to physicians for earlier diagnosis and treatment, in the presence of clinically relevant confusers, thereby reducing further complications and morbidity.


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 38
    حجم فایل: 2525 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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