Automated severity scoring of atopic dermatitis patients by a deep neural network.
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ABSTRACT:
SUBMITTER: Bang CH
PROVIDER: S-EPMC7961024 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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: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:Background/objectivesCrisaborole ointment, 2%, is a nonsteroidal phosphodiesterase 4 inhibitor for the treatment of mild-to-moderate atopic dermatitis (AD). This pooled post hoc analysis of two phase 3 trials (NCT02118766, NCT02118792) assessed improvement and time to improvement in Investigator's Static Global Assessment (ISGA) and Severity of Pruritus Scale (SPS) outcomes in pediatric patients with mild-to-moderate AD.MethodsPatients aged ?2 years were randomly assigned 2:1 to receive twice-daily crisaborole or vehicle for 28 days. Patients aged 2-17 years were pooled for this analysis. Proportions of patients and time to achieving ISGA success (clear [0] or almost clear [1] with ?2-grade improvement from baseline), ISGA clear/almost clear, ?1-grade improvement in ISGA, SPS success (SPS score ?1 with ?1-grade improvement), or ?1-grade improvement in SPS score were analyzed and stratified by baseline ISGA.ResultsAt first postbaseline assessment (day 8), significantly higher proportions of crisaborole- than vehicle-treated patients achieved ISGA success, ISGA clear/almost clear, ?1-grade ISGA improvement, SPS success, or ?1-grade improvement in SPS regardless of baseline ISGA. Differences were significantly greater over time for all outcomes for patients with moderate baseline ISGA and numerically greater for those with mild baseline ISGA. Median times to ISGA and SPS outcomes were shorter for crisaborole versus vehicle.ConclusionImprovement in ISGA and SPS outcomes were observed with crisaborole in pediatric patients with mild-to-moderate baseline AD.
| S-EPMC7756882 | 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
Project description:BackgroundAtopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery.MethodsWe preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD.ResultsIn this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD.ConclusionsOverall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.
| S-EPMC10792836 | biostudies-literature