Ros‐NET: A deep convolutional neural network for automatic identification of rosacea lesions

Binol, Hamidullah; Plotner, Alisha N.; Sopkovich, Jennifer; Kaffenberger, Benjamin H.; Niazi, M. Khalid Khan; Gürcan, Metin N.

doi:10.1111/srt.12817

Cited by 40 publications

(33 citation statements)

References 27 publications

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“…Previous efforts have been made to apply CNN to identify rosacea. However, previous work focused mainly on the development of networks or analysis of images instead of practically applying CNN for the identification of rosacea and differentiating it from other skin diseases or for the classification of subtypes of rosacea [32]. Besides, the number of images for model development was quite limited (less than 100) in the previous studies and the sensitivity or specificity were barely satisfactory [33].…”

Section: Discussionmentioning

confidence: 99%

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

Zhao¹,

Wu²,

Zhang³

et al. 2021

JMIR Med Inform

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Background Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. Objective The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. Methods In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. Results The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. Conclusions The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

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Section: Discussionmentioning

confidence: 99%

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

Zhao¹,

Wu²,

Zhang³

et al. 2021

JMIR Med Inform

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“…We used mini-batches of size 16 for U-Net. Early stopping was employed to avoid over-fitting [29, 30].…”

Section: The Proposed Methodologymentioning

confidence: 99%

SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

Binol

Moberly

Niazi

et al. 2020

Preprint

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Background and Objective: The aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: Frame selection improves the diagnostic quality of composite images from otoscope video clips.

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“…This is a notable limitation considering that the incidence of scalp psoriasis is 45-56% and nail psoriasis is 23-27% among psoriatic patients [57]. CNNs have been created for other diseases, such as atopic dermatitis [58], onychomycosis [56], and rosacea [59]. To classify onychomycosis, Han et al [56] used a R-CNN to generate a training datasets of 49,567 images of nails and found that a combination of their datasets performed better than dermatologists.…”

Section: Other Dermatological Diseasesmentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

165

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Ros‐NET: A deep convolutional neural network for automatic identification of rosacea lesions

Cited by 40 publications

References 27 publications

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

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