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Li H, Chen G, Zhang L, Xu C, Wen J. A review of psoriasis image analysis based on machine learning. Front Med (Lausanne) 2024; 11:1414582. [PMID: 39170035 PMCID: PMC11337201 DOI: 10.3389/fmed.2024.1414582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/02/2024] [Indexed: 08/23/2024] Open
Abstract
Machine Learning (ML), an Artificial Intelligence (AI) technique that includes both Traditional Machine Learning (TML) and Deep Learning (DL), aims to teach machines to automatically learn tasks by inferring patterns from data. It holds significant promise in aiding medical care and has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. This paper presents the findings of a systematic literature review focusing on the research and application of ML in psoriasis analysis over the past decade. We summarized 53 publications by searching the Web of Science, PubMed and IEEE Xplore databases and classified them into three categories: (i) lesion localization and segmentation; (ii) lesion recognition; (iii) lesion severity and area scoring. We have presented the most common models and datasets for psoriasis analysis, discussed the key challenges, and explored future trends in ML within this field. Our aim is to suggest directions for subsequent research.
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Affiliation(s)
- Huihui Li
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Guangjie Chen
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Li Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Dermatology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chunlin Xu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Ju Wen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Dermatology, Guangdong Second Provincial General Hospital, Guangzhou, China
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Schaffert D, Bibi I, Blauth M, Lull C, von Ahnen JA, Gross G, Schulze-Hagen T, Knitza J, Kuhn S, Benecke J, Schmieder A, Leipe J, Olsavszky V. Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study. JMIR Form Res 2024; 8:e55855. [PMID: 38738977 PMCID: PMC11240079 DOI: 10.2196/55855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/27/2024] [Accepted: 04/11/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. OBJECTIVE This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. METHODS Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. RESULTS Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. CONCLUSIONS The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
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Affiliation(s)
- Daniel Schaffert
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Igor Bibi
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Mara Blauth
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Christian Lull
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Jan Alwin von Ahnen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Georg Gross
- Department of Medicine V, Division of Rheumatology, University Medical Center and Medical Faculty Mannheim, Mannheim, Germany
| | - Theresa Schulze-Hagen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Johannes Knitza
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Sebastian Kuhn
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Astrid Schmieder
- Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Jan Leipe
- Department of Medicine V, Division of Rheumatology, University Medical Center and Medical Faculty Mannheim, Mannheim, Germany
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
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He S, Liu L, Long X, Ge M, Cai M, Zhang J. Single-cell analysis and machine learning identify psoriasis-associated CD8 + T cells serve as biomarker for psoriasis. Front Genet 2024; 15:1387875. [PMID: 38915827 PMCID: PMC11194350 DOI: 10.3389/fgene.2024.1387875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Psoriasis is a chronic inflammatory skin disease, the etiology of which has not been fully elucidated, in which CD8+ T cells play an important role in the pathogenesis of psoriasis. However, there is a lack of in-depth studies on the molecular characterization of different CD8+ T cell subtypes and their role in the pathogenesis of psoriasis. This study aims to further expound the pathogenesy of psoriasis at the single-cell level and to explore new ideas for clinical diagnosis and new therapeutic targets. Our study identified a unique subpopulation of CD8+ T cells highly infiltrated in psoriasis lesions. Subsequently, we analyzed the hub genes of the psoriasis-specific CD8+ T cell subpopulation using hdWGCNA and constructed a machine-learning prediction model, which demonstrated good efficacy. The model interpretation showed the influence of each independent variable in the model decision. Finally, we deployed the machine learning model to an online website to facilitate its clinical transformation.
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Affiliation(s)
- Sijia He
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Lyuye Liu
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiaoyan Long
- The Second Affiliated Hospital of Guizhou Medical University, Kaili, Guizhou, China
| | - Man Ge
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Menghan Cai
- Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Junling Zhang
- Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin, China
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Li Pomi F, Papa V, Borgia F, Vaccaro M, Pioggia G, Gangemi S. Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases. Life (Basel) 2024; 14:516. [PMID: 38672786 PMCID: PMC11051135 DOI: 10.3390/life14040516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.
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Affiliation(s)
- Federica Li Pomi
- Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, Italy;
| | - Vincenzo Papa
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
| | - Francesco Borgia
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Mario Vaccaro
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy;
| | - Sebastiano Gangemi
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Fernandes JRN, Teles AS, Fernandes TRS, Lima LDB, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. J Clin Med 2023; 13:180. [PMID: 38202187 PMCID: PMC10779723 DOI: 10.3390/jcm13010180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
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Affiliation(s)
- Jacks Renan Neves Fernandes
- PhD Program in Biotechnology—Northeast Biotechnology Network, Federal University of Piauí, Teresina 64049-550, Brazil;
| | - Ariel Soares Teles
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
- Federal Institute of Maranhão, Araioses 65570-000, Brazil
| | - Thayaná Ribeiro Silva Fernandes
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Lucas Daniel Batista Lima
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Surjeet Balhara
- Department of Electronics & Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Nishu Gupta
- Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway;
| | - Silmar Teixeira
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
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Benavent D, Muñoz-Fernández S, De la Morena I, Fernández-Nebro A, Marín-Corral J, Castillo Rosa E, Taberna M, Sanabra C, Sastre C. Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study. Ther Adv Musculoskelet Dis 2023; 15:1759720X231220818. [PMID: 38146537 PMCID: PMC10749530 DOI: 10.1177/1759720x231220818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/28/2023] [Indexed: 12/27/2023] Open
Abstract
Background Spondyloarthritis (SpA) is a group of related but phenotypically distinct inflammatory disorders that include axial SpA (axSpA) and psoriatic arthritis (PsA). Information on the characteristics and management of these patients in the real world remains scarce. Objectives To explore the characteristics and management [disease activity assessment and treatment with secukinumab (SEC) or other biologic disease-modifying antirheumatic drugs (bDMARDs)] of axSpA and PsA patients using natural language processing (NLP) in Electronic Health Records (EHRs). Design National, multicenter, observational, and retrospective study. Methods We analyzed free-text and structured clinical information from EHR at three hospitals. All adult patients with axSpA, PsA or non-classified SpA from 2018 to 2021 with minimum follow-up of three months were included when starting SEC or other bDMARDs. Clinical variables were extracted using EHRead® technology based on Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) terminology. Results Out of 887,735 patients, 758 were included, of which 328 had axSpA [58.5% male; mean (SD) age of 50.7 (12.7) years], 365 PsA [54.8% female, 53.9 (12.4) years], and 65 non-classified SpA. Mean (SD) time since diagnosis was 36.8 (61.0) and 24.1 (35.2) months for axSpA and PsA, respectively. Only 116 axSpA patients (35.3%) had available Ankylosing Spondylitis Disease Activity Score (ASDAS) or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) at bDMARD onset, of which 61 presented active disease. Disease Activity in PSoriatic Arthritis (DAPSA) or Disease Assessment Score - 28 joints (DAS-28) values at bDMARD onset were available for only 61 PsA (16.7%) patients, with 23 of them having active disease. The number of patients with available tender joint count or swollen joint count assessment was 68 (20.7%) and 59 (18%) for axSpA, and 115 (31.5%) and 119 (32.6%) for PsA, respectively. SEC was used in 63 (19.2%) axSpA patients and in 63 (17.3%) PsA patients. Conclusion Using NLP, the study showed that around one-third of axSpA and one-sixth of PsA patients have disease activity assessments with ASDAS/BASDAI or DAPSA/DAS-28, respectively, highlighting an area of improvement in these patients' management.
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Affiliation(s)
- Diego Benavent
- SAVANA Research S.L., Calle de Larra 12, Madrid 28013, Spain
| | - Santiago Muñoz-Fernández
- Hospital Universitario Infanta Sofía, Universidad Europea de Madrid, San Sebastián de los Reyes, Madrid, Spain
| | - Isabel De la Morena
- Department of Rheumatology, Hospital Clínico Universitario de Valencia, Valencia, Valencia, Spain
| | - Antonio Fernández-Nebro
- Instituto de Investigación Biomédica de Málaga (IBIMA)-Plataforma Bionand, Málaga, Spain
- UGC de Reumatología, Hospital Regional Universitario de Málaga, Málaga, Spain
- Departamento de Medicina, Universidad de Málaga, Málaga, Spain
| | | | | | | | | | - Carlos Sastre
- Medical Department, Novartis Farmacéutica SA., Barcelona, Spain
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Polesie S, Alinaghi F, Egeberg A. A systematic review investigating at what proportion clinical images are shared in prospective randomized controlled trials involving patients with psoriasis and biological agents. J DERMATOL TREAT 2023; 34:2281261. [PMID: 37965743 DOI: 10.1080/09546634.2023.2281261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023]
Abstract
For many patients including those with psoriasis, scientific manuscripts comprising clinical outcomes including psoriasis area severity index (PASI) and/or physician global assessment (PGA) may be difficult to understand. However, most patients can relate to images at baseline and follow-up, particularly for dermatological diseases. This study aimed to assess the proportion of shared clinical images in psoriasis trials. A systematic review adhering to the PRISMA guidelines was performed. The review was limited to randomized controlled trials, and among these, only investigations involving biological agents for treatment of psoriasis were included. The Embase, MEDLINE and Scopus databases were searched for eligible studies published from inception to October 26, 2021. In total, 152 studies were included. When combining these, 62,871 patients were randomized. Overall, 203 images were shared depicting 60 patients in the manuscripts yielding an overall sharing rate of 0.1%. Patient images are seldom incorporated in clinical trial manuscripts which impairs interpretation for patients. Inclusion of image material would strengthen the patients' perspective and understanding on what treatment effects that can be expected. As such, this systematic review should be an invitation to the pharmaceutical industry, other sponsors, and editorial offices to improve easy transfer of information to patients using image data.
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Affiliation(s)
- Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Farzad Alinaghi
- National Allergy Research Centre, Department of Dermatology and Allergy, Herlev and Gentofte Hospital, University of Copenhagen, Hellerup, Denmark
| | - Alexander Egeberg
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
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Bibi I, Schaffert D, Blauth M, Lull C, von Ahnen JA, Gross G, Weigandt WA, Knitza J, Kuhn S, Benecke J, Leipe J, Schmieder A, Olsavszky V. Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study. J Med Internet Res 2023; 25:e50886. [PMID: 38015608 PMCID: PMC10716771 DOI: 10.2196/50886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. OBJECTIVE We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. METHODS After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. RESULTS Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. CONCLUSIONS This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.
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Affiliation(s)
- Igor Bibi
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Daniel Schaffert
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Mara Blauth
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Christian Lull
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Alwin von Ahnen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Georg Gross
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wanja Alexander Weigandt
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Johannes Knitza
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Sebastian Kuhn
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Leipe
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Astrid Schmieder
- Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
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Liu Z, Wang X, Ma Y, Lin Y, Wang G. Artificial intelligence in psoriasis: Where we are and where we are going. Exp Dermatol 2023; 32:1884-1899. [PMID: 37740587 DOI: 10.1111/exd.14938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/05/2023] [Accepted: 09/09/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.
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Affiliation(s)
- Zhenhua Liu
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Xinyu Wang
- Department of Economics, Finance and Healthcare Administration, Valdosta State University, Valdosta, Georgia, USA
| | - Yao Ma
- Student Brigade of Basic Medicine School, Fourth Military Medical University, Xi'an, China
| | - Yiting Lin
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Gang Wang
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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11
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Tiwari A, Gupta N, Singla D, Ranjan Swain J, Gupta R, Mehta D, Kumar S. Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review. Cureus 2023; 15:e45187. [PMID: 37842407 PMCID: PMC10576017 DOI: 10.7759/cureus.45187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023] Open
Abstract
Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists. The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.
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Affiliation(s)
- Anushree Tiwari
- Clinical Quality and Value, American Academy of Orthopaedic Surgeons, Rosemont, USA
| | - Neha Gupta
- Department of Oral Pathology, Microbiology & Forensic Odontology, Dental College, Rajendra Institute of Medical Sciences, Ranchi, IND
| | - Deepika Singla
- Department of Conservative Dentistry & Endodontics, Desh Bhagat Dental College & Hospital, Malout, IND
| | - Jnana Ranjan Swain
- Department of Pedodontics and Preventive Dentistry, Institute of Dental Sciences, Siksha 'O' Anusandhan, Bhubaneswar, IND
| | - Ruchi Gupta
- Department of Prosthodontics, Rungta College of Dental Sciences and Research, Bhilai, IND
| | - Dhaval Mehta
- Department of Oral Medicine and Radiology, Narsinbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, IND
| | - Santosh Kumar
- Department of Periodontology and Implantology, Karnavati School of Dentistry, Karnavati University, Gandhinagar, IND
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12
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Bieber T. Disease modification in inflammatory skin disorders: opportunities and challenges. Nat Rev Drug Discov 2023; 22:662-680. [PMID: 37443275 DOI: 10.1038/s41573-023-00735-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Progress in understanding of the mechanisms underlying chronic inflammatory skin disorders, such as atopic dermatitis and psoriasis vulgaris, has led to new treatment options with the primary goal of alleviating symptoms. In addition, this knowledge has the potential to inform on new strategies aimed at inducing deep and therapy-free remission, that is, disease modification, potentially impacting on associated comorbidities. However, to reach this goal, key areas require further exploration, including the definitions of disease modification and disease activity index, further understanding of disease mechanisms and systemic spillover effects, potential windows of opportunity, biomarkers for patient stratification and successful intervention, as well as appropriate study design. This Perspective article assesses the opportunities and challenges in the discovery and development of disease-modifying therapies for chronic inflammatory skin disorders.
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Affiliation(s)
- Thomas Bieber
- Department of Dermatology and Allergy, University Hospital, Bonn, Germany.
- Christine Kühne - Center for Allergy Research and Education, Davos, Switzerland.
- Davos Biosciences, Davos, Switzerland.
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13
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Raj R, Londhe ND, Sonawane R. PsLSNetV2: End to end deep learning system for measurement of area score of psoriasis regions in color images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Jartarkar SR. Artificial intelligence: Its role in dermatopathology. Indian J Dermatol Venereol Leprol 2022:1-4. [PMID: 36688886 DOI: 10.25259/ijdvl_725_2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/01/2022] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI), a major frontier in the field of medical research, can potentially lead to a paradigm shift in clinical practice. A type of artificial intelligence system known as convolutional neural network points to the possible utility of deep learning in dermatopathology. Though pathology has been traditionally restricted to microscopes and glass slides, recent advancement in digital pathological imaging has led to a transition making it a potential branch for the implementation of artificial intelligence. The current application of artificial intelligence in dermatopathology is to complement the diagnosis and requires a well-trained dermatopathologist's guidance for better designing and development of deep learning algorithms. Here we review the recent advances of artificial intelligence in dermatopathology, its applications in disease diagnosis and in research, along with its limitations and future potential.
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Affiliation(s)
- Shishira R Jartarkar
- Department of Dermatology, Venereology and Leprosy, Vydehi Institute of Medical Sciences and Research Centre, Whitefield, Bengaluru, Karnataka, India
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15
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Hügle T, Caratsch L, Caorsi M, Maglione J, Dan D, Dumusc A, Blanchard M, Kalweit G, Kalweit M. Dorsal Finger Fold Recognition by Convolutional Neural Networks for the Detection and Monitoring of Joint Swelling in Patients with Rheumatoid Arthritis. Digit Biomark 2022; 6:31-35. [PMID: 35949225 PMCID: PMC9247561 DOI: 10.1159/000525061] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/25/2022] [Indexed: 08/09/2023] Open
Abstract
Digital biomarkers such as wearables are of increasing interest in monitoring rheumatic diseases, but they usually lack disease specificity. In this study, we apply convolutional neural networks (CNN) to real-world hand photographs in order to automatically detect, extract, and analyse dorsal finger fold lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA). Hand photographs of RA patients were taken by a smartphone camera in a standardized manner. Overall, 190 PIP joints were categorized as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed, and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares. In swollen joints, the number of automatically extracted deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49, p < 0.01). The joint diameter/deep skinfold length ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1, SD 0.6, p < 0.01). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84, a sensitivity of 88%, and a specificity of 75%. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to disease-modifying antirheumatic drug ± corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease in the mean diameter/finger fold length (finger fold index, FFI) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with disease flares. In conclusion, automated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real-world hand photos might serve as a digital biomarker in RA.
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Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Leo Caratsch
- Department of Rheumatology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | | | - Jules Maglione
- Department of Informatics, EPFL, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Diana Dan
- Department of Rheumatology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Dumusc
- Department of Rheumatology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Marc Blanchard
- Department of Rheumatology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Gabriel Kalweit
- Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany
| | - Maria Kalweit
- Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany
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16
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Havelin A, Hampton P. Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes - A Narrative Review. PSORIASIS (AUCKLAND, N.Z.) 2022; 12:15-24. [PMID: 35320971 PMCID: PMC8935082 DOI: 10.2147/ptt.s323471] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/15/2022] [Indexed: 12/13/2022]
Abstract
The role of technology in dermatology is expanding. Telemedicine and eHealth are increasingly being used by doctors and patients in the management of psoriasis. This is a narrative review of the literature relating to the use of digital technology in the management of psoriasis. We divided psoriasis e-health into three areas: mobile phone applications, teledermatology and artificial Intelligence (AI). Literature searches were conducted using the following databases: Pubmed, Google Scholar, Scopus, both app stores using App Annie platform. The following words were used in searches; psoriasis, dermatology, mobile phone application, application, app, smartphone, teledermatology, telemedicine, artificial intelligence, AI, machine learning in various combinations. We defined three key questions, one relating to each of the 3 areas. We then reviewed the relevant papers found in the searches and selected the papers of highest research quality and greatest relevance in order to answer the questions. In addition, for apps, operating systems for IOS and android devices were searched for apps containing the key word "psoriasis" in the title using the app analytic website www.appannie.com on 08/11/21. Research publications linked to these apps were reviewed.
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Affiliation(s)
- Alison Havelin
- Department of Dermatology, Newcastle Hospitals NHS Trust, Newcastle, UK
| | - Philip Hampton
- Department of Dermatology, Newcastle Hospitals NHS Trust, Newcastle, UK
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Giovannini I, Bosch P, Dejaco C, De Marco G, McGonagle D, Quartuccio L, De Vita S, Errichetti E, Zabotti A. The Digital Way to Intercept Psoriatic Arthritis. Front Med (Lausanne) 2021; 8:792972. [PMID: 34888334 PMCID: PMC8650082 DOI: 10.3389/fmed.2021.792972] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 12/14/2022] Open
Abstract
Psoriasis (PsO) and Psoriatic Arthritis (PsA) are chronic, immune-mediated diseases that share common etiopathogenetic pathways. Up to 30% of PsO patient may later develop PsA. In nearly 75% of cases, skin psoriatic lesions precede arthritic symptoms, typically 10 years prior to the onset of joint symptoms, while PsO diagnosis occurring after the onset of arthritis is described only in 15% of cases. Therefore, skin involvement offers to the rheumatologist a unique opportunity to study PsA in a very early phase, having a cohort of psoriatic “risk patients” that may develop the disease and may benefit from preventive treatment. Progression from PsO to PsA is often characterized by non-specific musculoskeletal symptoms, subclinical synovio-entheseal inflammation, and occasionally asymptomatic digital swelling such as painless toe dactylitis, that frequently go unnoticed, leading to diagnostic delay. The early diagnosis of PsA is crucial for initiating a treatment prior the development of significant and permanent joint damage. With the ongoing development of pharmacological treatments, early interception of PsA has become a priority, but many obstacles have been reported in daily routine. The introduction of digital technology in rheumatology may fill the gap in the physician-patient relationship, allowing more targeted monitoring of PsO patients. Digital technology includes telemedicine, virtual visits, electronic health record, wearable technology, mobile health, artificial intelligence, and machine learning. Overall, this digital revolution could lead to earlier PsA diagnosis, improved follow-up and disease control as well as maximizing the referral capacity of rheumatic centers.
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Affiliation(s)
- Ivan Giovannini
- Rheumatology Clinic, Department of Medicine, University of Udine, c/o Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
| | - Philipp Bosch
- Department of Rheumatology and Immunology, Medical University of Graz, Graz, Austria
| | | | - Gabriele De Marco
- Leeds Institute of Rheumatic and Musculoskeletal Medicine (LIRMM), University of Leeds, Leeds, United Kingdom
| | - Dennis McGonagle
- Leeds Institute of Rheumatic and Musculoskeletal Medicine (LIRMM), University of Leeds, Leeds, United Kingdom
| | - Luca Quartuccio
- Rheumatology Clinic, Department of Medicine, University of Udine, c/o Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
| | - Salvatore De Vita
- Rheumatology Clinic, Department of Medicine, University of Udine, c/o Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
| | - Enzo Errichetti
- Department of Medical and Biological Sciences, Institute of Dermatology, University of Udine, Udine, Italy
| | - Alen Zabotti
- Rheumatology Clinic, Department of Medicine, University of Udine, c/o Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
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18
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Optimization of psoriasis assessment system based on patch images. Sci Rep 2021; 11:18130. [PMID: 34518578 PMCID: PMC8437948 DOI: 10.1038/s41598-021-97211-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/12/2021] [Indexed: 12/15/2022] Open
Abstract
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.
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