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

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 Features of the Skin Microbiota in Common Inflammatory Skin Diseases

Life ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 962
Author(s):  
Iva Ferček ◽  
Liborija Lugović-Mihić ◽  
Arjana Tambić-Andrašević ◽  
Diana Ćesić ◽  
Ana Gverić Grginić ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Seborrheic Dermatitis ◽  
Bacterial Species ◽  
Clinical Manifestations ◽  
Skin Microbiome ◽  
Bacterial Strains ◽  
Skin Microbiota ◽  
Inflammatory Skin Diseases ◽  
Bacterial Microbiome ◽  
Inflammatory Skin

Many relatively common chronic inflammatory skin diseases manifest on the face (seborrheic dermatitis, rosacea, acne, perioral/periorificial dermatitis, periocular dermatitis, etc.), thereby significantly impairing patient appearance and quality of life. Given the yet unexplained pathogenesis and numerous factors involved, these diseases often present therapeutic challenges. The term “microbiome” comprises the totality of microorganisms (microbiota), their genomes, and environmental factors in a particular environment. Changes in human skin microbiota composition and/or functionality are believed to trigger immune dysregulation, and consequently an inflammatory response, thereby playing a potentially significant role in the clinical manifestations and treatment of these diseases. Although cultivation methods have traditionally been used in studies of bacterial microbiome species, a large number of bacterial strains cannot be grown in the laboratory. Since standard culture-dependent methods detect fewer than 1% of all bacterial species, a metagenomic approach could be used to detect bacteria that cannot be cultivated. The skin microbiome exhibits spatial distribution associated with the microenvironment (sebaceous, moist, and dry areas). However, although disturbance of the skin microbiome can lead to a number of pathological conditions and diseases, it is still not clear whether skin diseases result from change in the microbiome or cause such a change. Thus far, the skin microbiome has been studied in atopic dermatitis, seborrheic dermatitis, psoriasis, acne, and rosacea. Studies on the possible association between changes in the microbiome and their association with skin diseases have improved the understanding of disease development, diagnostics, and therapeutics. The identification of the bacterial markers associated with particular inflammatory skin diseases would significantly accelerate the diagnostics and reduce treatment costs. Microbiota research and determination could facilitate the identification of potential causes of skin diseases that cannot be detected by simpler methods, thereby contributing to the design and development of more effective therapies.

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

2019 ◽  
Vol 82 (6) ◽  
pp. 709-719 ◽  
Author(s):  
Chan‐Pang Kuok ◽  
Ming‐Huwi Horng ◽  
Yu‐Ming Liao ◽  
Nan‐Haw Chow ◽  
Yung‐Nien Sun
Keyword(s):  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Identification System ◽  
Accurate Identification ◽  
Convolution Neural Networks

scholarly journals An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

2021 ◽  
Vol 8 ◽  
Author(s):  
Tommaso Banzato ◽  
Marek Wodzinski ◽  
Federico Tauceri ◽  
Chiara Donà ◽  
Filippo Scavazza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pleural Effusion ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Computer Aided Detection ◽  
Radiographic Findings ◽  
Computer Aided ◽  
Operating Characteristic Curve

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

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 Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review

2020 ◽  
Vol 67 (2) ◽  
pp. 134-142 ◽  
Author(s):  
Dennis Jay Wong ◽  
Ziba Gandomkar ◽  
Wan‐Jing Wu ◽  
Guijing Zhang ◽  
Wushuang Gao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Literature Review ◽  
Convolution Neural Networks ◽  
Mammographic Images ◽  
Narrative Literature ◽  
Narrative Literature Review

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

2020 ◽  
pp. 27-28
Author(s):  
A.B. Movsisyan ◽  
◽  
A.V. Kuroyedov ◽  
G.A. Ostapenko ◽  
S.V. Podvigin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Modern Method ◽  
Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution
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