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.

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

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 Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Xiaoyu He ◽  
Zhentao Jin ◽  
Lisha Wu ◽  
Xinyu Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Chinese Population ◽  
Skin Diseases ◽  
Area Under The Curve ◽  
Seborrheic Keratosis ◽  
Clinical Images ◽  
Learning Techniques

Objectives. To evaluate CNN models’ performance of identifying the clinical images of basal cell carcinoma (BCC) and seborrheic keratosis (SK) and to compare their performance with that of dermatologists. Methods. We constructed a Chinese skin diseases dataset which includes 1456 BCC and 1843 SK clinical images and the corresponding medical history. We evaluated the performance using four mainstream CNN structures and transfer learning techniques. We explored the interpretability of the CNN model and compared its performance with that of 21 dermatologists. Results. The fine-tuned InceptionResNetV2 achieved the best performance, with an accuracy and area under the curve of 0.855 and 0.919, respectively. Further experimental results suggested that the CNN model was not only interpretable but also had a performance comparable to that of dermatologists. Conclusions. This study is the first on the assistant diagnosis of BCC and SK based on the proposed dataset. The promising results suggested that CNN model’s performance was comparable to that of expert dermatologists.

scholarly journals Applying Artificial Intelligence for the Diagnosis and Classification of Rosacea (Preprint)

2020 ◽  
Author(s):  
Zhixiang Zhao ◽  
CheMing Wu ◽  
Shuping Zhang ◽  
Fanping He ◽  
Fangfen Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Skin Diseases ◽  
Seborrheic Dermatitis ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Accurate Identification ◽  
Clinical Images ◽  
Convolution Neural Networks ◽  
Accuracy And Precision

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 of clinical manifestations, the lack of objective biochemical examinations and non-specificity of histopathology, 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 In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). METHODS In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). 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 (AUROC) of 0.972 for the detection of rosacea. The accuracy of classifying the three subtypes of rosacea, ETR, PPR, PhR 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 identificaiton between rosacea, seborrheic dermatitis and eczema, the overall accuracy was 0.757 and the precision was 0.667. Finally, by comparing the CNN with different levels of dermatologists, we showed that our CNN system is capable of identifying rosacea with a performance superior to resident doctors or attending physicians and comparable to experienced specialists. CONCLUSIONS In conclusion, by assessing clinical images, the CNN system in our study performed at dermatologist-level in the identification of rosacea. CLINICALTRIAL None

The Constructive Thinking Inventory: Factorial Structure in Healthy Individuals and Patients with Chronic Skin Diseases

1998 ◽  
Vol 14 (3) ◽  
pp. 226-233 ◽  
Author(s):  
Jürgen Hoyer ◽  
Mechthild Averbeck ◽  
Thomas Heidenreich ◽  
Ulrich Stangier ◽  
Karin Pöhlmann ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Principal Component ◽  
Initial Study ◽  
Statistical Independence ◽  
Constructive Thinking ◽  
Global Factor ◽  
Emotional Coping ◽  
Factor Congruence ◽  
Chronic Skin ◽  
Chronic Skin Diseases

Epstein's “Constructive Thinking Inventory” (CTI) was developed to measure the construct of experiential intelligence, which is based on his cognitive-experiential self-theory. Inventory items were generated by sampling naturally occurring automatic cognitions. Using principal component analysis, the findings showed a global factor of coping ability as well as six main factors: Emotional Coping, Behavioral Coping, Categorical Thinking, Personal Superstitious Thinking, Esoteric Thinking, and Naive Optimism. We tested the replicability of this factor structure and the amount of statistical independence (nonredundancy) between these factors in an initial study of German students (Study 1, N = 439) and in a second study of patients with chronic skin disorders (Study 2, N = 187). Factor congruence with the original (American) data was determined using a formula proposed by Schneewind and Cattell (1970) . Our findings show satisfactory factor congruence and statistical independence for Emotional Coping and Esoteric Thinking in both studies, while full replicability or independence could not be found in both for the other factors. Implications for the use and further development of the CTI are discussed.

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

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