Publications
121 results found
Fujihara, Murakami, Magi, et al., 2023, Omics-based mathematical modeling unveils pathogenesis of periodontitis in an experimental murine model, Journal of Dental Research, ISSN: 0022-0345
Attar R, Hurault G, Wang Z, et al., 2023, Reliable detection of eczema areas for fully automated assessment of eczema severity from digital camera images, JID Innovations, ISSN: 2667-0267
Assessing the severity of eczema in clinical research requires face-to-face skin examination by trained staff. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. Computer vision algorithms have been proposed to automate the assessment of eczema severity using digital camera images. However, they often require human intervention to detect eczema lesions and cannot automatically assess eczema severity from real-world images in an end-to-end pipeline.We developed a model to detect eczema lesions from images using data augmentation and pixel-level segmentation of eczema lesions on 1345 images provided by dermatologists. We evaluated the quality of the obtained segmentation compared to that of the clinicians, the robustness to varying imaging conditions encountered in real-life images, such as lighting, focus, and blur and the performance of downstream severity prediction when using the detected eczema lesions.The quality and robustness of eczema lesion detection increased by approximately 25% and 40%, respectively, compared to our previous eczema detection model. The performance of the downstream severity prediction remained unchanged. Use of skin segmentation as an alternative to eczema segmentation that requires specialist labelling showed the performance on par with when eczema segmentation is used.
Steele L, Li Tan X, Olabi B, et al., 2023, Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review, JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY, ISSN: 0926-9959
Hurault G, Attar R, Pan K, et al., 2022, Fully automated assessment of Atopic Dermatitis severity from real-world digital, 51st Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S202-S202, ISSN: 0022-202X
Steele L, Thomas B, O'Toole EA, et al., 2022, Deep learning prediction of filaggrin mutation status from palmar images, 51st Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S222-S222, ISSN: 0022-202X
Lee J, Miyano T, Tanaka RJ, 2022, Optimising Staphylococcus aureus-targeted therapies for atopic dermatitis using a mathematical modelling approach, 51st Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S231-S231, ISSN: 0022-202X
Duverdier A, Hurault G, Custovic A, et al., 2022, Recency bias in weekly POEM recording and its effects on POEM prediction, 51st Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S198-S198, ISSN: 0022-202X
Hurault G, Tanaka RJ, 2022, Personalised predictions of AD severity dynamics and treatment recommendations using a Bayesian machine learning approach, 51st Annual Meeting of the European-Society-for-Dermatological-Research (ESDR), Publisher: ELSEVIER SCIENCE INC, Pages: S195-S195, ISSN: 0022-202X
Hurault G, Pan K, Ricardo M, et al., 2022, Detecting eczema areas in digital images: an impossible task?, JID innovations, Vol: 2, Pages: 1-8, ISSN: 2667-0267
Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images. Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (SE=0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image. The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.
Fukuda K, Furuichi Y, Miyano T, et al., 2022, Three stepwise pH zones to form functional stratum corneum, Annual Meeting of the Society-for-Investigative-Dermatology (SID), Publisher: ELSEVIER SCIENCE INC, Pages: S70-S70, ISSN: 0022-202X
Sun Y, Hurault G, Kezic S, et al., 2022, From univariate analysis to machine learning: identifying atopic dermatitis-related biomarkers, Publisher: WILEY, Pages: E20-E20, ISSN: 0007-0963
Thomas BR, Steele L, Tanaka RJ, et al., 2022, Prediction of FLG genotype using human and computer-aided phenotype extraction with random forest machine learning, Publisher: WILEY, Pages: 195-196, ISSN: 0007-0963
Thomas BR, Steele L, Tanaka RJ, et al., 2022, Prediction of FLG genotype using human and computer-aided phenotype extraction with random forest machine learning, 102nd Annual Meeting of the British-Association-of-Dermatologists (BAD), Publisher: WILEY, Pages: 44-45, ISSN: 0007-0963
Miyano T, Irvine AD, Tanaka RJ, 2022, A mathematical model to investigate drug targets for dupilumab poor responders in atopic dermatitis, Publisher: WILEY, Pages: E39-E40, ISSN: 0007-0963
Lee J, Miyano T, Tanaka RJ, 2022, Staphylococcus aureus eradication can cause skin barrier damage due to S. epidermidis overgrowth in atopic dermatitis: a mathematical model study, Publisher: WILEY, Pages: E238-E239, ISSN: 0007-0963
Duverdier A, Custovic A, Tanaka RJ, 2022, Data-driven research on eczema: systematic characterization of the field and recommendations for the future, Publisher: WILEY
Duverdier A, Custovic A, Tanaka R, 2022, Data-driven research on eczema: systematic characterization of the field and recommendations for the future, Clinical and Translational Allergy, Vol: 12, Pages: 1-10, ISSN: 2045-7022
BackgroundThe past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods.MethodsWe retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.ResultsFive key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers.ConclusionsResearch areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and pro
Miyano T, Irvine A, Tanaka R, 2022, Model-based meta-analysis to optimise S. aureus-targeted therapies for atopic dermatitis, JID Innovations, Vol: 2, ISSN: 2667-0267
Several clinical trials of Staphylococcus aureus (S. aureus)-targeted therapies for atopic dermatitis (AD) have demonstrated conflicting results regarding whether they improve AD severity scores. This study performs a model-based meta-analysis to investigate possible causes of these conflicting results and suggests how to improve the efficacies of S. aureus-targeted therapies.We developed a mathematical model that describes systems-level AD pathogenesis involving dynamic interactions between S. aureus and Coagulase Negative Staphylococcus (CoNS). Our model simulation reproduced the clinically observed detrimental effects of application of S. hominis A9 (ShA9) and flucloxacillin on AD severity and showed that these effects disappeared if the bactericidal activity against CoNS was removed. A hypothetical (modelled) eradication of S. aureus by 3.0 log10 CFU/cm2, without killing CoNS, achieved comparable EASI-75 to dupilumab. This efficacy was potentiated if dupilumab was administered in conjunction with S. aureus eradication (EASI-75 at week 16; S. aureus eradication: 66.7%, dupilumab 61.6% and combination: 87.8%). The improved efficacy was also seen for virtual dupilumab poor responders.Our model simulation suggests that killing CoNS worsens AD severity and that S. aureus-specific eradication without killing CoNS could be effective for AD patients, including dupilumab poor responders. This study will contribute to design promising S. aureus-targeted therapy.
Miyano T, Irvine AD, Tanaka RJ, 2022, Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis, JID Innovations, Vol: 2, Pages: 100110-100110, ISSN: 2667-0267
Hurault G, Stalder, Mery, et al., 2022, EczemaPred: a computational framework for personalised prediction of eczema severity dynamics, Clinical and Translational Allergy, Vol: 12, ISSN: 2045-7022
Background:Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.Objective:This study aims to develop a computational framework for personalised prediction of AD severity dynamics.Methods:We introduced EczemaPred, a computational framework to predict patient-dependent dynamic evolution of AD severity using Bayesian state-space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient-oriented scoring atopic dermatitis (PO-SCORAD) by combining predictions from the models for the nine severity items of PO-SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO-SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO-SCORAD was recorded daily by 16 AD patients for 12 weeks.Results:EczemaPred achieved good performance for personalised predictions of PO-SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time-series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO-SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty).Conclusions:EczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.
Hurault G, Pan K, Mokhtari R, et al., 2022, Detecting eczema areas in digital images: an impossible task?
<jats:title>ABSTRACT</jats:title><jats:p>Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images.</jats:p><jats:p>Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (<jats:italic>SE</jats:italic> = 0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image.</jats:p><jats:p>The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.</jats:p>
Miyano T, Irvine A, Tanaka R, 2022, A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis, Allergy, Vol: 77, Pages: 582-594, ISSN: 0105-4538
BackgroundSeveral biologics for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders.MethodsWe conducted model‐based meta‐analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferon‐gamma) by describing system‐level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.ResultsOur model reproduced reported time courses of %improved EASI and EASI‐75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL‐13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL‐13 and IL‐22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI‐75 at 24 weeks: 21.6% vs. max. 1.9%).ConclusionOur model identified IL‐13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL‐13 and IL‐22 as a promising drug therapy for dupilumab poor responders. This model will serve as a computational platform for model‐informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets and the mechanisms behind patient variability in drug response.
Hurault G, Roekevisch, Schram, et al., 2022, Can serum biomarkers predict the outcome of systemic immunosuppressive therapy in adult atopic dermatitis patients?, Skin Health and Disease, Vol: 2, ISSN: 2690-442X
Background: Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients.Objective: We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients.Methods: We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution of the latent disease severity and measurement errors to predict AD severity scores (EASI, (o)SCORAD and POEM) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores.Results: We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance.Conclusions: In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.
Lee J, Mannan AA, Miyano T, et al., 2022, OPTIMISING STAPHYLOCOCCUS AUREUS-TARGETED THERAPIES FOR ATOPIC DERMATITIS USING A MATHEMATICAL MODELLING APPROACH, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 34-34, ISSN: 0001-5555
Hurault G, Tanaka RJ, 2022, PERSONALIZED PREDICTIONS OF ATOPIC DERMATITIS SEVERITY DYNAMICS AND TREATMENT RECOMMENDATIONS USING A BAYESIAN MACHINE LEARNING APPROACH, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 15-15, ISSN: 0001-5555
Hurault G, Attar R, Pan K, et al., 2022, FULLY AUTOMATED ASSESSMENT OF ATOPIC DERMATITIS SEVERITY FROM REAL-WORLD DIGITAL IMAGES, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 15-15, ISSN: 0001-5555
Duverdier- A, Hurault G, Custovic A, et al., 2022, RECENCY BIAS IN WEEKLY POEM RECORDING AND ITS EFFECTS ON POEM PREDICTION, Publisher: ACTA DERMATO-VENEREOLOGICA, Pages: 14-15, ISSN: 0001-5555
Miyauchi, Ki, Ukai, et al., 2021, Essential role of STAT3 signaling in hair follicle homeostasis, Frontiers in Immunology, ISSN: 1664-3224
Steele L, Thomas B, O'Toole EA, et al., 2021, Machine learning prediction of filaggrin mutation status from palmar images: a proof-of- concept study, Publisher: ELSEVIER SCIENCE INC, Pages: S157-S157, ISSN: 0022-202X
Miyano T, Irvine AD, Tanaka RJ, 2021, A computational model suggested potential therapies for dupilumab poor responders in atopic dermatitis, Publisher: ELSEVIER SCIENCE INC, Pages: S156-S156, ISSN: 0022-202X
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