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Joustra V, Li Yim AYF, van Gennep S, Hageman I, de Waard T, Levin E, Lauffer P, de Jonge W, Henneman P, Löwenberg M, D’Haens G. Peripheral Blood DNA Methylation Signatures and Response to Tofacitinib in Moderate-to-severe Ulcerative Colitis. J Crohns Colitis 2024; 18:1179-1189. [PMID: 37526299 PMCID: PMC11324342 DOI: 10.1093/ecco-jcc/jjad129] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/22/2023] [Accepted: 07/27/2023] [Indexed: 08/02/2023]
Abstract
INTRODUCTION Predictive biomarkers for treatment efficacy of ulcerative colitis [UC] treatments are lacking. Here, we performed a longitudinal study investigating the association and potential predictive power of genome-wide peripheral blood [PB] DNA methylation signatures and response to tofacitinib treatment in UC. METHODS We recruited moderate-to-severe UC patients starting tofacitinib treatment, and measured PB DNA methylation profiles at baseline [T1], after 8 weeks [T2], and in a subset [n = 8] after a median of 20 weeks [T3] using the Illumina Infinium HumanMethylation EPIC BeadChip. After 8 weeks, we distinguished responders [R] from non-responders [NR] based on a centrally read endoscopic response [decrease in endoscopic Mayo score ≥1 or Ulcerative Colitis Endoscopic Index of Severity ≥2] combined with corticosteroid-free clinical and/or biochemical response. T1 PB samples were used for biomarker identification, and T2 and publicly available intraclass correlation [ICC] data were used for stability analyses. RNA-sequencing was performed to understand the downstream effects of the predictor CpG loci. RESULTS In total, 16 R and 15 NR patients, with a median disease duration of 7 [4-12] years and overall comparable patient characteristics at baseline, were analysed. We identified a panel of 53 differentially methylated positions [DMPs] associated with response to tofacitinib [AUROC 0.74]. Most DMPs [77%] demonstrated both short- and long-term hyperstability [ICC ≥0.90], irrespective of inflammatory status. Gene expression analysis showed lower FGFR2 [pBH = 0.011] and LRPAP1 [pBH = 0.020], and higher OR2L13 [pBH = 0.016] expression at T1 in R compared with NR. CONCLUSION Our observations demonstrate the utility of genome-wide PB DNA methylation signatures to predict response to tofacitinib.
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Affiliation(s)
- Vincent Joustra
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Andrew Y F Li Yim
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Sara van Gennep
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Ishtu Hageman
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | - Peter Lauffer
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Pediatric Endocrinology, Emma Children’s Hospital, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Wouter de Jonge
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Tytgat Institute for Liver and Intestinal Research, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Surgery, University of Bonn, Bonn, Germany
| | - Peter Henneman
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Genome Diagnostics Laboratory, Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Mark Löwenberg
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Geert D’Haens
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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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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Cuccia G, Privitera G, Di Vincenzo F, Monastero L, Parisio L, Carbone L, Scaldaferri F, Pugliese D. Predictors of Efficacy of Janus Kinase Inhibitors in Patients Affected by Ulcerative Colitis. J Clin Med 2024; 13:766. [PMID: 38337460 PMCID: PMC10856140 DOI: 10.3390/jcm13030766] [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: 12/31/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Personalised medicine and the identification of predictors of the efficacy of specific drugs represent the ultimate goal for the treatment of ulcerative colitis (UC) in order to break the current therapeutic ceiling. JAK inhibitors are a new class of advanced therapies, orally administered, showing a good profile of efficacy and safety in both randomised controlled trials (RCTs) and real-world studies. Unfortunately, to date, it is not possible to draw the ideal profile of a patient maximally benefiting from this class of drugs to guide clinicians' therapeutic choices. Baseline clinical activities and inflammatory biomarkers, as well as their early variation after treatment initiation, emerged as the main predictors of efficacy from post hoc analyses of RCTs with tofacitinib. Similar findings were also observed in the real-life studies including mainly patients with a history of pluri-refractoriness to biological therapies. At last, a few new biomarkers have been explored, even though they have not been validated in large cohorts. This paper provides a review of the current knowledge on clinical variables and biomarkers predicting response to JAK inhibitors in UC.
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Affiliation(s)
- Giuseppe Cuccia
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, L. Go A. Gemelli 8, 00168 Rome, Italy; (G.C.); (F.D.V.); (L.M.); (F.S.)
| | - Giuseppe Privitera
- Dipartimento di Scienze della Salute, Università degli Studi di Milano, 20122 Milan, Italy;
| | - Federica Di Vincenzo
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, L. Go A. Gemelli 8, 00168 Rome, Italy; (G.C.); (F.D.V.); (L.M.); (F.S.)
| | - Lucia Monastero
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, L. Go A. Gemelli 8, 00168 Rome, Italy; (G.C.); (F.D.V.); (L.M.); (F.S.)
| | - Laura Parisio
- IBD UNIT-CEMAD (Centro Malattie Apparato Digerente), Medicina Interna e Gastroenterologia, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Luigi Carbone
- UOC Pronto Soccorso, Medicina d’Urgenza e Medicina Interna, Ospedale Isola Tiberina Gemelli Isola, 00186 Rome, Italy;
| | - Franco Scaldaferri
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, L. Go A. Gemelli 8, 00168 Rome, Italy; (G.C.); (F.D.V.); (L.M.); (F.S.)
- IBD UNIT-CEMAD (Centro Malattie Apparato Digerente), Medicina Interna e Gastroenterologia, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Daniela Pugliese
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, L. Go A. Gemelli 8, 00168 Rome, Italy; (G.C.); (F.D.V.); (L.M.); (F.S.)
- IBD UNIT-CEMAD (Centro Malattie Apparato Digerente), Medicina Interna e Gastroenterologia, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy;
- UOS Gastroenterologia, Ospedale Isola Tiberina Gemelli Isola, 00186 Rome, Italy
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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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Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [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: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
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Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
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Kundel V, Cohen O, Khan S, Patel M, Kim-Schulze S, Kovacic J, Suárez-Fariñas M, Shah NA. Advanced Proteomics and Cluster Analysis for Identifying Novel Obstructive Sleep Apnea Subtypes before and after Continuous Positive Airway Pressure Therapy. Ann Am Thorac Soc 2023; 20:1038-1047. [PMID: 36780659 DOI: 10.1513/annalsats.202210-897oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/13/2023] [Indexed: 02/15/2023] Open
Abstract
Rationale: Studies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. Objectives: We used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. Methods: Adults with newly diagnosed OSA had blood drawn at baseline and three to four months after CPAP. Samples were analyzed using the Olink proteomics platform, which measures 92 prespecified inflammatory proteins using proximity extension assay. Linear mixed-effects models were used to model changes in protein expression during the period of CPAP use, adjusting for batch, age, and sex. Unsupervised hierarchical clustering was performed to identify unique inflammatory OSA clusters on the basis of inflammatory biomarkers. Within-cluster impact of CPAP on inflammatory protein expression was assessed. Results: Among 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m2, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low (n = 21), intermediate (n = 19), and high (n = 6) inflammatory protein expression. After CPAP, there were significant within-cluster differences in protein expression. The low inflammatory cluster had a significant increase in protein expression (16%; P = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (-20%; P = 0.003), more significant among those compliant with CPAP in the low (25%; P = 0.04) and high (-22%; P = 0.01) clusters. Conclusions: We identified three unique inflammatory clusters in patients with OSA using plasma proteomics, with a differential response to CPAP by cluster. Our results are hypothesis generating and require further investigation in larger longitudinal studies for enhanced cardiovascular risk profiling in OSA.
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Affiliation(s)
| | - Oren Cohen
- Division of Pulmonary, Critical Care and Sleep Medicine
| | - Samira Khan
- Division of Pulmonary, Critical Care and Sleep Medicine
| | - Manishkumar Patel
- Human Immune Monitoring Center, Hess Center for Science and Medicine
| | | | - Jason Kovacic
- Cardiovascular Research Institute, and
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia; and
- St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Mayte Suárez-Fariñas
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Neomi A Shah
- Division of Pulmonary, Critical Care and Sleep Medicine
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Antonatos C, Asmenoudi P, Panoutsopoulou M, Vasilopoulos Y. Pharmaco-Omics in Psoriasis: Paving the Way towards Personalized Medicine. Int J Mol Sci 2023; 24:ijms24087090. [PMID: 37108251 PMCID: PMC10139144 DOI: 10.3390/ijms24087090] [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: 03/22/2023] [Revised: 04/04/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
The emergence of high-throughput approaches has had a profound impact on personalized medicine, evolving the identification of inheritable variation to trajectory analyses of transient states and paving the way for the unveiling of response biomarkers. The utilization of the multi-layered pharmaco-omics data, including genomics, transcriptomics, proteomics, and relevant biological information, has facilitated the identification of key molecular biomarkers that can predict the response to therapy, thereby optimizing treatment regiments and providing the framework for a tailored treatment plan. Despite the availability of multiple therapeutic options for chronic diseases, the highly heterogeneous clinical response hinders the alleviation of disease signals and exacerbates the annual burden and cost of hospitalization and drug regimens. This review aimed to examine the current state of the pharmaco-omic approaches performed in psoriasis, a common inflammatory disease of the skin. We sought to identify central studies that investigate the inter-individual variability and explore the underlying molecular mechanisms of drug response progression via biological profiling in psoriatic patients administered with the extended therapeutic armamentarium of psoriasis, incorporating conventional therapies, small molecules, as well as biological drugs that inhibit central pathogenic cytokines involved in the disease pathogenesis.
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Affiliation(s)
- Charalabos Antonatos
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Paschalia Asmenoudi
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Mariza Panoutsopoulou
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Yiannis Vasilopoulos
- Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece
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Lunge SB, Shetty NS, Sardesai VR, Karagaiah P, Yamauchi PS, Weinberg JM, Kircik L, Giulini M, Goldust M. Therapeutic application of machine learning in psoriasis: A Prisma systematic review. J Cosmet Dermatol 2023; 22:378-382. [PMID: 35621249 DOI: 10.1111/jocd.15122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/15/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
Abstract
Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.
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Affiliation(s)
- Snehal Balvant Lunge
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Nandini Sundar Shetty
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Vidyadhar R Sardesai
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Priyanka Karagaiah
- Department of dermatology, Bangalore Medical College and Research Institute, Bangalore, India
| | - Paul S Yamauchi
- Dermatology Institute and Skin Care Center, Santa Monica, California, USA
- Division of Dermatology, David Geffen School of Medicine at University of California, Los Angeles, California, USA
| | | | - Leon Kircik
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mario Giulini
- Department of Dermatology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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Zhu W, Lévy-Leduc C, Ternès N. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics 2023; 24:25. [PMID: 36690931 PMCID: PMC9869528 DOI: 10.1186/s12859-023-05143-0] [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: 07/30/2022] [Accepted: 01/09/2023] [Indexed: 01/24/2023] Open
Abstract
In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.
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Affiliation(s)
- Wencan Zhu
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120 Palaiseau, France ,Biostatistics and Programming Department, Sanofi R&D, 91380 Chilly Mazarin, France
| | - Céline Lévy-Leduc
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120 Palaiseau, France
| | - Nils Ternès
- Biostatistics and Programming Department, Sanofi R&D, 91380 Chilly Mazarin, France
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Molecular and cellular regulation of psoriatic inflammation. Clin Sci (Lond) 2022; 136:935-952. [PMID: 35730381 DOI: 10.1042/cs20210916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 06/01/2022] [Accepted: 06/07/2022] [Indexed: 02/06/2023]
Abstract
This review highlights the molecular and cellular mechanisms underlying psoriatic inflammation with an emphasis on recent developments which may impact on treatment approaches for this chronic disease. We consider both the skin and the musculoskeletal compartment and how different manifestations of psoriatic inflammation are linked. This review brings a focus to the importance of inflammatory feedback loops that exist in the initiation and chronic stages of the condition, and how close interaction between the epidermis and both innate and adaptive immune compartments drives psoriatic inflammation. Furthermore, we highlight work done on biomarkers to predict the outcome of therapy as well as the transition from psoriasis to psoriatic arthritis.
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Peng F, Chen F, Wen H, Bai J, Tian Y. Measurement of pre-treatment inflammatory cytokine levels is valuable for prediction of treatment efficacy to tumor necrosis factor inhibitor in axial spondyloarthritis patients. Int J Rheum Dis 2022; 25:844-850. [PMID: 35694730 PMCID: PMC9542033 DOI: 10.1111/1756-185x.14353] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/18/2022] [Accepted: 05/14/2022] [Indexed: 11/30/2022]
Abstract
Aim To evaluate the correlation of inflammatory cytokines with the treatment response to tumor necrosis factor inhibitor (TNFi) in axial spondyloarthritis (axSpA) patients. Methods This study enrolled 86 axSpA patients and 20 healthy controls (HCs). Inflammatory cytokines including tumor necrosis factor‐α (TNF‐α), interleukin (IL)‐1β, IL‐6, IL‐12, IL‐17A, IL‐21, IL‐23, and IL‐32 were determined in serum samples of axSpA patients before treatment and in HCs after enrollment. All patients received 40 mg adalimumab every 2 weeks for 12 weeks; meanwhile, ASAS40 (40 criteria of the Assessment by the SpondyloArthritis International Society) response rates were evaluated at weeks 2, 4, 8, and 12. Results Most inflammatory cytokines were elevated in axSpA patients compared with HCs (all P < 0.05) except for IL‐32 (P = 0.101). In axSpA patients, ASAS40 response rates were 0%, 19.5%, 34.5%, 47.1%, and 56.3% at weeks 0, 2, 4, 8, and 12, respectively. Baseline [interquartile range] IL‐6 (47.3 [32.5‐53.4] pg/mL vs 31.7 [23.0‐50.9] pg/mL, P = 0.005) and IL‐17A (127.9 [90.7‐149.5] pg/mL vs 96.6 [56.1‐112.6] pg/mL, P < 0.001) were higher in axSpA patients with ASAS40 response compared with those without ASAS40 response, while baseline TNF‐α, IL‐1β, IL‐12, IL‐21, IL‐23, and IL‐32 were not different between them (all P > 0.050). Multivariate logistic regression analysis disclosed that baseline IL‐17A (P = 0.037), C‐reactive protein (P = 0.012), and history of TNF inhibitor (P = 0.029) were independently associated with ASAS40 response. Furthermore, baseline IL‐17A, C‐reactive protein, history of TNFi, and their combination had an acceptable to good ability for predicting ASAS40 response. Conclusion Measurement of pre‐treatment inflammatory cytokine levels is valuable for predicting treatment efficacy of TNFi in axSpA patients.
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Affiliation(s)
- Fei Peng
- Department of Traditional Chinese Medicine, Section of Western Medicine Foundation Teaching and Research, Baoji Vocational & Technical College, Baoji, China
| | - Fengyun Chen
- Department of Hematology and Rheumatology, Baoji Central Hospital, Baoji, China
| | - Huijun Wen
- Department Neurology, Baoji Central Hospital, Baoji, China
| | - Jie Bai
- Department of Rheumatology and Immunology, No.215 Hospital of Shaanxi Nuclear Industry, Xianyang, China
| | - Yuping Tian
- Department of Hematology and Rheumatology, Baoji Central Hospital, Baoji, China
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12
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Krishnan VS, Kõks S. Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach. Int J Mol Sci 2022; 23:ijms23116130. [PMID: 35682804 PMCID: PMC9181806 DOI: 10.3390/ijms23116130] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/23/2022] [Accepted: 05/26/2022] [Indexed: 12/14/2022] Open
Abstract
Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient’s genetic profile will greatly improve patient outcome and cost savings for the healthcare system.
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Affiliation(s)
- Vidya S. Krishnan
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Discovery Way, Murdoch, WA 6150, Australia;
- Perron Institute for Neurological and Translational Science, 8 Verdun Street, Nedlands, WA 6009, Australia
| | - Sulev Kõks
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Discovery Way, Murdoch, WA 6150, Australia;
- Perron Institute for Neurological and Translational Science, 8 Verdun Street, Nedlands, WA 6009, Australia
- Correspondence:
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13
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Vincken NLA, Welsing PMJ, Silva-Cardoso SC, Bekker CPJ, Lopes AP, Nordkamp MO, Leijten EFA, Radstake TRDJ, Angiolilli C. Suppression of IL-12/IL-23 p40 subunit in the skin and blood of psoriasis patients by Tofacitinib is dependent on active interferon-γ signaling in dendritic cells: implications for the treatment of psoriasis and interferon-driven diseases. Exp Dermatol 2022; 31:962-969. [PMID: 35297512 PMCID: PMC9313893 DOI: 10.1111/exd.14566] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/28/2022] [Accepted: 03/13/2022] [Indexed: 11/27/2022]
Abstract
Interleukin (IL)‐12 and IL‐23 are pro‐inflammatory cytokines produced by dendritic cells (DCs) and associated with Psoriasis (Pso) and Psoriatic Arthritis (PsA) pathogenesis. Tofacitinib, a Janus kinase inhibitor, effectively suppresses inflammatory cascades downstream the IL‐12/IL‐23 axis in Pso and PsA patients. Here, we investigated whether Tofacitinib directly regulates IL‐12/IL‐23 production in DCs, and how this regulation reflects responses to Tofacitinib in Pso patients. We treated monocyte‐derived dendritic cells and myeloid dendritic cells with Tofacitinib and stimulated cells with either lipopolysaccharide (LPS) or a combination of LPS and IFN‐γ. We assessed gene expression by qPCR, obtained skin microarray and blood Olink data and clinical parameters of Pso patients treated with Tofacitinib from public data sets. Our results indicate that in DCs co‐stimulated with LPS and IFN‐γ, but not with LPS alone, Tofacitinib leads to the decreased expression of IL‐23/IL‐12 shared subunit IL12B (p40). In Tofacitinib‐treated Pso patients, IL‐12 expression and psoriasis area and severity index (PASI) are significantly reduced in patients with higher IFN‐γ at baseline. These findings demonstrate for the first time that Tofacitinib suppresses IL‐23/IL‐12 shared subunit IL12B in DCs upon active IFN‐γ signaling, and that Pso patients with higher IFN‐γ baseline levels display improved clinical response after Tofacitinib treatment.
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Affiliation(s)
- Nanette L A Vincken
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Paco M J Welsing
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sandra C Silva-Cardoso
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Cornelis P J Bekker
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ana P Lopes
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Michel Olde Nordkamp
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Emmerik F A Leijten
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Timothy R D J Radstake
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Chiara Angiolilli
- Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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14
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. [Translated article] Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2021.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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15
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad? ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:30-46. [DOI: 10.1016/j.ad.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/18/2021] [Indexed: 11/25/2022] Open
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16
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AIM in Dermatology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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18
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Yan D, Gudjonsson JE, Le S, Maverakis E, Plazyo O, Ritchlin C, Scher JU, Singh R, Ward NL, Bell S, Liao W. New Frontiers in Psoriatic Disease Research, Part I: Genetics, Environmental Triggers, Immunology, Pathophysiology, and Precision Medicine. J Invest Dermatol 2021; 141:2112-2122.e3. [PMID: 34303522 PMCID: PMC8384663 DOI: 10.1016/j.jid.2021.02.764] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 02/07/2023]
Abstract
Psoriasis is a chronic inflammatory condition characterized by systemic immune dysregulation. Over the past several years, advances in genetics, microbiology, immunology, and mouse models have revealed the complex interplay between the heritable and microenvironmental factors that drive the development of psoriatic inflammation. In the first of this two-part review series, the authors will discuss the newest insights into the pathogenesis of psoriatic disease and highlight how the evolution of these scientific fields has paved the way for a more personalized approach to psoriatic disease treatment.
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Affiliation(s)
- Di Yan
- Ronald O. Perelman Department of Dermatology, NYU Langone Health, New York, New York, USA
| | | | - Stephanie Le
- Department of Dermatology, University of California, Davis, Sacramento, California, USA
| | - Emanual Maverakis
- Department of Dermatology, University of California, Davis, Sacramento, California, USA
| | - Olesya Plazyo
- Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA
| | - Christopher Ritchlin
- Center for Musculoskeletal Research, Division of Allergy, Immunology and Rheumatology, University of Rochester School of Medicine & Dentistry, Rochester, New York, USA
| | - Jose U Scher
- Department of Medicine, NYU Grossman School of Medicine, NYU Langone Health, New York, New York, USA
| | - Roopesh Singh
- Department of Nutrition, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Nicole L Ward
- Department of Nutrition, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA; Department of Dermatology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Stacie Bell
- National Psoriasis Foundation, Portland, Oregon, USA
| | - Wilson Liao
- UCSF Department of Dermatology, University of California San Francisco, San Francisco, California, USA.
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19
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Yu K, Syed MN, Bernardis E, Gelfand JM. Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review. ACTA ACUST UNITED AC 2021; 5:147-159. [PMID: 33733038 PMCID: PMC7963214 DOI: 10.1177/2475530320950267] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction. Objective To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances. Methods We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and "psoriasis" in the title and/or abstract. Results Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment. Conclusion Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.
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Affiliation(s)
- Kimberley Yu
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Maha N Syed
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Elena Bernardis
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Gelfand
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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20
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AIM in Dermatology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_188-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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Mo X, Chen X, Ieong C, Zhang S, Li H, Li J, Lin G, Sun G, He F, He Y, Xie Y, Zeng P, Chen Y, Liang H, Zeng H. Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning. Front Pharmacol 2020; 11:1164. [PMID: 32848772 PMCID: PMC7411125 DOI: 10.3389/fphar.2020.01164] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Aims At present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR). Materials and Methods EMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model. Results Tender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively. Conclusion A pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.
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Affiliation(s)
- Xiaolan Mo
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,School of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Xiujuan Chen
- Guangzhou Women and Children's Medical Center, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Chifong Ieong
- School of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Song Zhang
- Guangzhou Women and Children's Medical Center, Pediatric Allergy Immunology & Rheumatology Department, Guangzhou Medical University, Guangzhou, China
| | - Huiyi Li
- School of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China.,Department of Pharmacy, Guangzhou Institute of Dermatology, Guangzhou, China
| | - Jiali Li
- School of Pharmaceutical Sciences, Institute of Clinical Pharmacology, Sun Yat-sen University, Guangzhou, China
| | - Guohao Lin
- Department of Pharmacy, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guangchao Sun
- Guangzhou Women and Children's Medical Center, Pediatric Allergy Immunology & Rheumatology Department, Guangzhou Medical University, Guangzhou, China
| | - Fan He
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yanling He
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Ying Xie
- Guangzhou Women and Children's Medical Center, Pediatric Allergy Immunology & Rheumatology Department, Guangzhou Medical University, Guangzhou, China
| | - Ping Zeng
- Guangzhou Women and Children's Medical Center, Pediatric Allergy Immunology & Rheumatology Department, Guangzhou Medical University, Guangzhou, China
| | - Yilu Chen
- Department of Pharmacy, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Huiying Liang
- Guangzhou Women and Children's Medical Center, Institute of Pediatrics, Guangzhou Medical University, Guangzhou, China
| | - Huasong Zeng
- Guangzhou Women and Children's Medical Center, Pediatric Allergy Immunology & Rheumatology Department, Guangzhou Medical University, Guangzhou, China
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22
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Maul LV, Meienberger N, Kaufmann L. [Role of artificial intelligence in assessing the extent and progression of dermatoses]. Hautarzt 2020; 71:677-685. [PMID: 32710130 DOI: 10.1007/s00105-020-04657-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND In recent years, many medical specialties with a visual focus have been revolutionized by image analysis algorithms using artificial intelligence (AI). As dermatology belongs to this field, it has the potential to play a pioneering role in the use of AI. OBJECTIVE The current use of AI for the diagnosis and follow-up of dermatoses is reviewed and the future potential of these technologies is discussed. MATERIALS AND METHODS This article is based on a selective review of the literature using Embase and MEDLINE and the keywords "psoriasis", "eczema", "dermatoses" and "acne" combined with "artificial intelligence", "machine learning", "deep learning", "neural network", "computer-guided", "supervised machine learning" or "unsupervised machine learning" were searched. RESULTS In comparison to examiner-dependent intra- and interindividually fluctuating scores for the assessment of inflammatory dermatoses (e.g. the Psoriasis Areas Severity Index [PASI] and body surface area [BSA]), AI-based algorithms can potentially offer reproducible, standardized evaluations of these scores. Whereas promising algorithms have already been developed for the diagnosis of psoriasis, there is currently only scarce work on the use of AI in the context of eczema. CONCLUSIONS The latest developments in this field show the enormous potential of AI-based diagnostics and follow-up of dermatological clinical pictures by means of an autonomous computer-based image analysis. These noninvasive, optical examination methods provide valuable additional information, but dermatological interaction remains indispensable in daily clinical practice.
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Affiliation(s)
- L V Maul
- Klinik für Dermatologie, Universitätsspital Basel, Basel, Schweiz.
| | - N Meienberger
- Klinik für Dermatologie, Universitätsspital Zürich, Zürich, Schweiz
| | - L Kaufmann
- Medizinische Klinik 3, Kardiologie, Universitätsklinikum Frankfurt am Main, Frankfurt am Main, Deutschland
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23
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Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W. Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations. Dermatol Ther (Heidelb) 2020; 10:365-386. [PMID: 32253623 PMCID: PMC7211783 DOI: 10.1007/s13555-020-00372-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.
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Affiliation(s)
- Stephanie Chan
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Vidhatha Reddy
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Bridget Myers
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Quinn Thibodeaux
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Nicholas Brownstone
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Wilson Liao
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA.
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