scholarly journals The Classification of Six Common Skin Diseases Based on Xiangya-Derm, A Chinese Database for Artificial Intelligence (Preprint)

10.2196/26025 ◽  
2021 ◽  
Author(s):  
Shuang Zhao ◽  
Xianggui Wang ◽  
Zixi Jiang ◽  
Yixin Li ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Skin Diseases ◽  
Common Skin

Xiangya-Derm, A Chinese Database For Artificial Intelligence and Research on Classification of Six Common Skin Diseases (Preprint)

2021 ◽  
Author(s):  
Shuang Zhao ◽  
Xianggui Wang ◽  
Zixi Jiang ◽  
Yixin Li ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Rural Areas ◽  
Chinese Population ◽  
Skin Diseases ◽  
Initial Study ◽  
Input Image ◽  
Clinical Images ◽  
Short Supply ◽  
Common Skin

BACKGROUND Skin and subcutaneous disease is the fourth leading cause of nonfatal disease burden globally and also one of the most common chief complaints in primary care. However, dermatologists are consistently in short supply, particularly in Chinese rural areas. Artificial intelligence tools can assist in diagnosing skin disorders from images, however the database for Chinese population is very limited, and it’s also non-trivial to directly apply the datasets built upon the US or EU population. OBJECTIVE To establish a dataset for artificial intelligence based on Chinese population, and present an initial study on six common skin diseases. METHODS Each image is captured with digital cameras or smartphones and verified by at least 3 experienced dermatologists and corresponding pathology information, and finally formed the Xiangya-Derm database. Based on this database, we conducted artificial intelligence-assisted classification research on 6 common skin diseases and then proposed a network called SkinNet. SkinNet applied a two-step strategy to identify skin diseases. Firstly, given an input image, we segment the regions of the skin lesion; Secondly, we introduce an information fusion block to combine the output of all segmented regions. We compare the performance with 31 dermatologists of varied experiences. RESULTS Xiangya-Derm, as a new database which consists of over 150,000 clinical images of 571 different skin diseases from Chinese population. It is known to be the largest and most abundant dermatological dataset of the Chinese. The artificial intelligence–based six-classification achieves the top-3 accuracy of 84.77%, which outperforms the average accuracy of dermatologists (78.15%). CONCLUSIONS Xiangya-Derm, a new and the largest database for the Chinese population was formed and the accuracy of classification of six common skin conditions based on Xiangya-Derm is comparable to that of dermatologists.

Efficacy and Safety of Oral Ivermectin and Topical Permethrin in the Treatment of Scabies

2020 ◽  
Vol 33 (1) ◽  
pp. 41-47
Author(s):  
Mohsena Akhter ◽  
Ishrat Bhuiyan ◽  
Zulfiqer Hossain Khan ◽  
Mahfuza Akhter ◽  
Gulam Kazem Ali Ahmad ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Close Contact ◽  
Randomized Study ◽  
Sarcoptes Scabiei ◽  
Efficacy And Safety ◽  
Common Skin ◽  
Group A ◽  
The Mean ◽  
The Difference ◽  
Group B

Background: Scabies is one of the most common skin diseases in our country. It is caused by the mite Sarcoptes scabiei var hominis, which is an ecto-parasite infesting the epidermis. Scabies is highly contagious. Prevalence is high in congested or densely populated areas. Individuals with close contact with an affected person should be treated with scabicidal which is available in both oral and topical formulations. The only oral but highly effective scabicidal known to date is Ivermectin. Amongst topical preparations, Permethrin 5 % cream is the treatment of choice. Objective: To evaluate the efficacy & safety of oral Ivermectin compared to topical Permethrin in the treatment of scabies. Methodology: This prospective, non-randomized study was conducted at the out-patient department of Dermatology and Venereology of Shaheed Suhrawardy Medical College & Hospital over a period of 6 months, from August 2016 to January 2017. The study population consisted of one hundred patients having scabies, enrolled according to inclusion criteria. They were divided into two groups. group A was subjected to oral Ivermectin and the group B to Permethrin 5% cream. Patients were followed up on day 7 and 14 for assessment of efficacy and safety. Result: The mean scoring with SD in group A (Ivermectin) and group B (Permethrin) were 8.26 ± 2.22 and 7.59 ± 2.01 respectively at the time of observation. The difference between the mean score of the two group is not significant (p=0.117) the mean scoring with SD in group A and group B were 4.54 ± 2.05 and 1.64 ± 1.84 respectively at 7thdays. The difference between the mean score of the two group is significant (p<0.001). The mean scoring with SD in group A and group B were 2.68± 2.35 and .36± 1.10 respectively at 14th day difference between the mean score of the group is significant (p<0.001). Conclusion: Topical application of permethrin 5% cream is more effective and safer than oral Ivermectin in the treatment of scabies. TAJ 2020; 33(1): 41-47

Prevalence and factors associated with anxiety among patients with common skin diseases

2020 ◽  
pp. 153-159 ◽  
Author(s):  
Homaid Al-Otaibi ◽  
Nawaf Alotibi ◽  
Fahad Althiyabi ◽  
Sami Alosaimi ◽  
Yazid Alharbi ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Factors Associated ◽  
Common Skin

AI-Provided Instant Differential Diagnosis of Pemphigus Vulgaris and Bullous Pemphigoid: Qualitative Study (Preprint)

2020 ◽  
Author(s):  
Xiaoyu He ◽  
Juan Su ◽  
Guangyu Wang ◽  
Kang Zhang ◽  
Navarini Alexander ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Diagnosis ◽  
Image Classification ◽  
Bullous Pemphigoid ◽  
Skin Diseases ◽  
Pemphigus Vulgaris ◽  
Classification Model ◽  
Clinical Image ◽  
Clinical Images ◽  
Multimodal Classification

BACKGROUND Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are two rare but severe inflammatory dermatoses. Due to the regional lack of trained dermatologists, many patients with these two diseases are misdiagnosed and therefore incorrectly treated. An artificial intelligence diagnosis framework would be highly adaptable for the early diagnosis of these two diseases. OBJECTIVE Design and evaluate an artificial intelligence diagnosis framework for PV and BP. METHODS The work was conducted on a dermatological dataset consisting of 17,735 clinical images and 346 patient metadata of bullous dermatoses. A two-stage diagnosis framework was designed, where the first stage trained a clinical image classification model to classify bullous dermatoses from five common skin diseases and normal skin and the second stage developed a multimodal classification model of clinical images and patient metadata to further differentiate PV and BP. RESULTS The clinical image classification model and the multimodal classification model achieved an area under the receiver operating characteristic curve (AUROC) of 0.998 and 0.942, respectively. On the independent test set of 20 PV and 20 BP cases, our multimodal classification model (sensitivity: 0.85, specificity: 0.95) performed better than the average of 27 junior dermatologists (sensitivity: 0.68, specificity: 0.78) and comparable to the average of 69 senior dermatologists (sensitivity: 0.80, specificity: 0.87). CONCLUSIONS Our diagnosis framework based on clinical images and patient metadata achieved expert-level identification of PV and BP, and is potential to be an effective tool for dermatologists in remote areas in the early diagnosis of these two diseases.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Classification of artificial intelligence technologies to determine the civil liability

2021 ◽  
Vol 1794 (1) ◽  
pp. 012001
Author(s):  
A Alekseev ◽  
O Erakhtina ◽  
K Kondratyeva ◽  
T Nikitin
Keyword(s):  
Artificial Intelligence ◽  
Civil Liability

scholarly journals Treatment of acne and acne-related scarring with fixed combination clindamycin phosphate and benzoyl peroxide gel (1.2%/3.75%) and tretinoin gel microsphere 0.06% in an Asian American transgender female

2020 ◽  
Vol 8 ◽  
pp. 2050313X2098403
Author(s):  
Edidiong CN Kaminska
Keyword(s):  
Benzoyl Peroxide ◽  
Asian American ◽  
Skin Diseases ◽  
Fixed Combination ◽  
Acne Vulgaris ◽  
Topical Therapy ◽  
The United States ◽  
Clindamycin Phosphate ◽  
Common Skin ◽  
Group Post

Acne vulgaris is one of the most common skin diseases in the United States and can affect any gender or ethnic group. Post-inflammatory hyperpigmentation (PIH) and scarring from acne can have a negative psychosocial impact on patients. Skin of color patients are particularly prone to PIH, as the dark marks left from acne may take several months to resolve, far after the acne has cleared. Here, we report a case of moderate acne with associated scarring in a transgender, Asian American female who was successfully treated with fixed combination topical therapy with clindamycin phosphate and benzoyl peroxide gel 1.2%/3.75% and tretinoin gel microsphere 0.06%.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

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