Automated severity scoring of atopic dermatitis patients by a deep neural network.
Ontology highlight
ABSTRACT:
SUBMITTER: Bang CH
PROVIDER: S-EPMC7961024 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin condition with a highly variable clinical phenotype.ObjectiveThis study aimed to identify historical and clinical features and biomarkers associated with AD severity.MethodsA US registry of extensively phenotyped AD participants (aged 0.73-80 years) were enrolled at 9 academic centers. Information on family and personal medical history, examination, skin swabs (culture), and serum biomarkers was collected to evaluate their association with AD severity.ResultsParticipants with AD (N = 2862) whose disease was categorized as mild (11.6%), moderate (58.0%), or severe (30.4%) based on Rajka-Langeland scoring were enrolled. The trend test, when adjusting for gender, race, and age, demonstrated that severity was strongly (P ≤ .04) associated with a personal/family history of allergic disorders, history of alopecia, exposure to passive smoke, ocular herpes infection, skin bacterial and viral infections, and history of arrhythmia. Features observed more frequently (P ≤ .002), as a function of severity, included skin infections (impetigo, human papillomavirus, and molluscum contagiosum virus), Staphylococcus aureus colonization, excoriations, hyperlinear palms, ichthyosis, blepharitis, conjunctivitis, ectropion, and wheezing. Serum IgE, allergen and food (≤6 years) Phadiatop, and eosinophilia were strongly linked to severity (P < .001).ConclusionsIn a diverse US AD population, severity was associated with a history of atopic disorders, skin and extracutaneous bacterial and viral infections (by history and physical examination), higher IgE, eosinophilia and allergen sensitization, atopic skin manifestations (ie, excoriation, hyperlinear palms, and ichthyosis), and atopic ocular features (ie, blepharitis, conjunctivitis, and ectropion) as well as asthma findings (ie, wheezing). Data from our prospective registry significantly advance our understanding of AD phenotypes and endotypes, which is critical to achieve optimal management.
| S-EPMC10524351 | biostudies-literature
Project description:PURPOSE:To develop an automated framework for sleep stage scoring from PSG via a deep neural network. METHODS:An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa. RESULTS:Two hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's Kappa of 0.7276. Top-2 accuracy (the proportion of test samples for which the true label is among the two most probable labels given by the model) was significantly improved compared to the overall classification accuracy, with the average being 0.9602. The number of arousals affected the model's performance. CONCLUSION:This research provides a robust and reliable model with the inter-rater agreement nearing that of human experts. Determining the most appropriate evaluation parameters for sleep staging is a direction for future research.
| S-EPMC7289784 | biostudies-literature
Project description:BackgroundThe Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.ObjectivesTo assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.MethodsImaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined image-based subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.ResultsIntraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).ConclusionsConvolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.
| S-EPMC9298301 | biostudies-literature
Project description:Atopic Dermatitis Research Network
| PRJNA714989 | ENA
Project description:PurposeTo develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists.Materials and methodsRadiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades.ResultsWith committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions.ConclusionAn end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020.
| S-EPMC7104788 | biostudies-literature
Project description:BackgroundAccurate assessment of atopic dermatitis (AD) severity is critical when initiating and monitoring therapy. Use of existing research tools such as the Eczema Area and Severity Index (EASI) and Scoring Atopic Dermatitis (SCORAD) is complex and time-consuming in clinical practice. A previous analysis found the product of validated Investigator's Global Assessment (vIGA) and affected body surface area (BSA) to be an accurate and practical tool for routine assessment of paediatric AD.ObjectiveTo evaluate the IGAxBSA composite as an alternative to EASI or SCORAD for assessment of AD disease severity and disease responsiveness.MethodsThe relationship between IGAxBSA, EASI and SCORAD was assessed in a post hoc analysis of pooled data from the dupilumab clinical trial programme in adult and paediatric patients with moderate-to-severe AD who had received dupilumab or placebo, with or without topical corticosteroids (TCS). The trials are registered at ClinicalTrials.gov and EudraCT: LIBERTY AD SOLO 1 (NCT02277743, 2014-001198-15), LIBERTY AD SOLO 2 (NCT02277769, 2014-002619-40), LIBERTY AD SOLO-CONTINUE (NCT02395133, 2014-003384-38), LIBERTY AD CHRONOS (NCT02260986, 2013-003254-24), LIBERTY AD CAFÉ (NCT02755649, 2015-002653-35), LIBERTY AD ADOL (NCT03054428, 2015-004458-16), LIBERTY AD PEDS (NCT03345914, 2016-004997-16), LIBERTY AD OLE (NCT01949311, 2013-001449-15) and LIBERTY AD PEDS OLE (NCT02612454, 2015-001396-40).ResultsUsing datapoints from pooled dupilumab randomized controlled trials (n = 3473) and open-label extension trials (n = 3045), we found that IGAxBSA correlated well with EASI and SCORAD, irrespective of treatment group and race (white, Asian, black). IGAxBSA correlated better with objective measures (EASI, SCORAD) than with patient- or caregiver-reported subjective measures. IGAxBSA correlated strongly with EASI and SCORAD in assessing disease change over time (r = 0·90, r = 0·76, respectively; P < 0·0001), and concordance between IGAxBSA-50/75/90 and EASI-50/75/90 was excellent (88-94%).ConclusionsIGAxBSA is a valid alternative for assessment of AD disease severity and response over time, compared with EASI or SCORAD in patients with AD, irrespective of race.
| S-EPMC9303952 | biostudies-literature
Project description:Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.
| S-EPMC6910668 | biostudies-literature
Project description:Conjunctival hyperaemia is a common clinical ophthalmological finding and can be a symptom of various ocular disorders. Although several severity classification criteria have been proposed, none include objective severity criteria. Neural networks and deep learning have been utilised in ophthalmology, but not for the purpose of classifying the severity of conjunctival hyperaemia objectively. To develop a conjunctival hyperaemia grading software, we used 3700 images as the training data and 923 images as the validation test data. We trained the nine neural network models and validated the performance of these networks. We finally chose the best combination of these networks. The DenseNet201 model was the best individual model. The combination of the DenseNet201, DenseNet121, VGG19, and ResNet50 were the best model. The correlation between the multimodel responses, and the vessel-area occupied was 0.737 (p < 0.01). This system could be as accurate and comprehensive as specialists but would be significantly faster and consistent with objective values.
| S-EPMC6589312 | biostudies-literature
Project description:Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3-6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2-34.3% higher than comparator IAs), and 58.7% Kappa agreement (16-23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters-sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.
| S-EPMC7793092 | biostudies-literature
Project description:Preclinical studies of novel compounds rely on quantitative readouts from animal models. Frequently employed readouts from histopathological tissue scoring are time consuming, require highly specialized staff and are subject to inherent variability. Recent advances in deep convolutional neural networks (CNN) now allow automating such scoring tasks. Here, we demonstrate this for the case of the Ashcroft fibrosis score and a newly developed inflammation score to characterize fibrotic and inflammatory lung diseases. Sections of lung tissue from mice exhibiting a wide range of fibrotic and inflammatory states were stained with Masson trichrome. Whole slide scans using a 20x objective were acquired and cut into smaller tiles of 512x512 pixels. The tiles were subsequently classified by specialized CNNs, either an "Ashcroft fibrosis CNN" or an "inflammation CNN". For the Ashcroft fibrosis score the CNN was fine-tuned by using 14000 labelled tiles. For the inflammation score the CNN was trained with 3500 labelled tiles. After training, the Ashcroft fibrosis CNN achieved an accuracy of 79.5% and the inflammation CNN an accuracy of 80.0%. An error analysis revealed that misclassifications are almost exclusively with neighboring scores, which reflects the inherent ambiguity of parts of the data. The variability between two experts was found to be larger than the variability between the CNN classifications and the ground truth. The CNN generated Ashcroft score was in very good agreement with the score of a pathologist (r2 = 0.92). Our results demonstrate that costly and time consuming scoring tasks can be automated and standardized with deep learning. New scores such as the inflammation score can be easily developed with the approach presented here.
| S-EPMC6107205 | biostudies-other