S I Cho1, S Sun2, J-H Mun1, C Kim3, S Y Kim3, S Cho4, S W Youn5, H C Kim2,6, J H Chung1. 1. Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea. 2. Interdisciplinary Program, Bioengineering Major, Graduate School, Seoul National University, Seoul, Korea. 3. Seoul National University College of Medicine, Seoul, Korea. 4. Department of Dermatology, SMG-SNU Boramae Medical Center, Seoul, Korea. 5. Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea. 6. Department of Biomedical Engineering, and Medical Research Center, Seoul National University College of Medicine, Seoul, Korea.
Abstract
BACKGROUND: Deep convolutional neural networks (DCNNs) can classify skin diseases at a level equivalent to a dermatologist, but their performance in specific areas requires further research. OBJECTIVE: To evaluate the performance of a trained DCNN-based algorithm in classifying benign and malignant lip diseases. METHODS: A training set of 1629 images (743 malignant, 886 benign) was used with Inception-Resnet-V2. Performance was evaluated using another set of 344 images and 281 images from other hospitals. Classifications by 44 participants (six board-certified dermatologists, 12 dermatology residents, nine medical doctors not specialized in dermatology and 17 medical students) were used for comparison. RESULTS: The outcomes based on the area under curve, sensitivity and specificity were 0·827 [95% confidence interval (CI) 0·782-0·873], 0·755 (95% CI 0·673-0·827) and 0·803 (95% CI 0·752-0·855), respectively, for the set of 344 images; and 0·774 (95% CI 0·699-0·849), 0·702 (95% CI 0·579-0·808) and 0·759 (95% CI 0·701-0·813), respectively, for the set of 281 images. The DCNN was equivalent to the dermatologists and superior to the nondermatologists in classifying malignancy. After referencing the DCNN result, the mean ± SD Youden index increased significantly for nondermatologists, from 0·201 ± 0·156 to 0·322 ± 0·141 (P < 0·001). CONCLUSIONS: DCNNs can classify lip diseases at a level similar to dermatologists. This will help unskilled physicians discriminate between benign and malignant lip diseases. What's already known about this topic? Deep convolutional neural networks (DCNNs) can classify malignant and benign skin diseases at a level equivalent to dermatologists. The lips are a unique feature in terms of histology and morphology. Previous studies of DCNNs have not investigated tumours on specific locations. What does this study add? This study shows that DCNNs can distinguish rare malignant and benign lip disorders at the same rate as dermatologists. DCNNs can help nondermatologists to distinguish malignant lip diseases. What are the clinical implications of this work? DCNNs can distinguish malignant and benign skin diseases even at specific locations such as the lips, as well as board-certified dermatologists. Malignant lip diseases are rare and difficult for less trained doctors to differentiate them from benign lesions. This study shows that in dermatology, DCNN can help improve decision-making processes for rare skin diseases in specific areas of the body.
BACKGROUND: Deep convolutional neural networks (DCNNs) can classify skin diseases at a level equivalent to a dermatologist, but their performance in specific areas requires further research. OBJECTIVE: To evaluate the performance of a trained DCNN-based algorithm in classifying benign and malignant lip diseases. METHODS: A training set of 1629 images (743 malignant, 886 benign) was used with Inception-Resnet-V2. Performance was evaluated using another set of 344 images and 281 images from other hospitals. Classifications by 44 participants (six board-certified dermatologists, 12 dermatology residents, nine medical doctors not specialized in dermatology and 17 medical students) were used for comparison. RESULTS: The outcomes based on the area under curve, sensitivity and specificity were 0·827 [95% confidence interval (CI) 0·782-0·873], 0·755 (95% CI 0·673-0·827) and 0·803 (95% CI 0·752-0·855), respectively, for the set of 344 images; and 0·774 (95% CI 0·699-0·849), 0·702 (95% CI 0·579-0·808) and 0·759 (95% CI 0·701-0·813), respectively, for the set of 281 images. The DCNN was equivalent to the dermatologists and superior to the nondermatologists in classifying malignancy. After referencing the DCNN result, the mean ± SD Youden index increased significantly for nondermatologists, from 0·201 ± 0·156 to 0·322 ± 0·141 (P < 0·001). CONCLUSIONS: DCNNs can classify lip diseases at a level similar to dermatologists. This will help unskilled physicians discriminate between benign and malignant lip diseases. What's already known about this topic? Deep convolutional neural networks (DCNNs) can classify malignant and benign skin diseases at a level equivalent to dermatologists. The lips are a unique feature in terms of histology and morphology. Previous studies of DCNNs have not investigated tumours on specific locations. What does this study add? This study shows that DCNNs can distinguish rare malignant and benign lip disorders at the same rate as dermatologists. DCNNs can help nondermatologists to distinguish malignant lip diseases. What are the clinical implications of this work? DCNNs can distinguish malignant and benign skin diseases even at specific locations such as the lips, as well as board-certified dermatologists. Malignant lip diseases are rare and difficult for less trained doctors to differentiate them from benign lesions. This study shows that in dermatology, DCNN can help improve decision-making processes for rare skin diseases in specific areas of the body.
Authors: Pir Masoom Shah; Faizan Ullah; Dilawar Shah; Abdullah Gani; Carsten Maple; Yulin Wang; Mohammad Abrar; Saif Ul Islam Journal: IEEE Access Date: 2021-05-05 Impact factor: 3.476