A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi

Autor: Alessandra Cartocci, Danica Tiodorovic, Marco Gori, Susana Puig, Elvira Moscarella, Jean-Luc Perrot, Francesca Farnetani, Monica Bianchini, Aimilios Lallas, Linda Tognetti, Cristina Carrera, Pietro Rubegni, Alberto Balistreri, Alessandro Mecocci, Giuseppe Argenziano, Caterina Longo, Simone Bonechi, Gennaro Cataldo, Elisa Cinotti, Gabriele Cevenini, Franco Scarselli, Giovanni Pellacani, Paolo Andreini
Přispěvatelé: Tognetti, L., Bonechi, S., Andreini, P., Bianchini, M., Scarselli, F., Cevenini, G., Moscarella, E., Farnetani, F., Longo, C., Lallas, A., Carrera, C., Puig, S., Tiodorovic, D., Perrot, J. L., Pellacani, G., Argenziano, G., Cinotti, E., Cataldo, G., Balistreri, A., Mecocci, A., Gori, M., Rubegni, P., Cartocci, A.
Rok vydání: 2021
Předmět:
Male
0301 basic medicine
Skin Neoplasms
Cutaneous melanoma
Dermoscopy
Deep convolutional neural network
Deep learning
Non-invasive imaging
Integrated diagnosis
Datasets as Topic
Biochemistry
030207 dermatology & venereal diseases
0302 clinical medicine
Non-invasive imaging
Medicine
Child
Melanoma
Skin
Aged
80 and over
Training set
Middle Aged
Child
Preschool
Female
Deep convolutional neural network
Skin lesion
Cutaneous melanoma
Adult
Noninvasive imaging
medicine.medical_specialty
Adolescent
Dermoscopy
Dermatology
Diagnosis
Differential
Young Adult
03 medical and health sciences
Integrated diagnosis
Image Interpretation
Computer-Assisted
Humans
Nevus
Molecular Biology
Aged
Retrospective Studies
business.industry
Deep learning
Infant
Newborn
Infant
Reproducibility of Results
Medical decision making
Atypical nevus
030104 developmental biology
Artificial intelligence
business
Integrated diagnosi
Zdroj: Journal of Dermatological Science. 101:115-122
ISSN: 0923-1811
Popis: Background Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists’ experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM). Objective We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL). Methods A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated “iDCNN_aMSL” model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models. Results In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %). Conclusions The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions.
Databáze: OpenAIRE
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