Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

Fagni, Filippo; Knitza, Johannes; Krusche, Martin; Kleyer, Arnd; Taşçılar, Koray; Simón, David

doi:10.3389/fmed.2021.718922

Cited by 8 publications

(11 citation statements)

References 105 publications

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“…Fagni et al. ( 34 ) reviewed the applications in the field of mHealth for early intercepting psoriatic arthritis patients. In particular, the authors identified the “Joint Pain Assessment Scoring Tool” (JPAST), a European Union-funded project on digital health, as a promising digital prognostic program that integrates PROMs (patient symptom checker inputs) with biological (serological and genetic) data.…”

Section: Resultsmentioning

confidence: 99%

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

et al. 2022

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BackgroundRheumatological and dermatological disorders contribute to a significant portion of the global burden of disease. Big Data are increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including dermatology and rheumatology. Rheumatology and dermatology can potentially benefit from Big Data.MethodsA systematic review of the literature was conducted according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines, mining “Uno per tutti”, a highly integrated and automated tool/meta-database developed at the University of Genoa, Genoa, Italy, and consisting of 20 major scholarly electronic databases, including PubMed/MEDLINE. Big Data- or artificial intelligence-based studies were judged based on the modified Qiao’s critical appraisal tool for critical methodological quality assessment of Big Data/machine learning-based studies. Other studies designed as cross-sectional, longitudinal, or randomized investigations, reviews/overviews or expert opinions/commentaries were evaluated by means of the relevant “Joanna Briggs Institute” (JBI)’s critical appraisal tool for the critical methodological quality assessment.ResultsFourteen papers were included in the present systematic review of the literature. Most of the studies included concerned molecular applications of Big Data, especially in the fields of genomics and post-genomics. Other studies concerned epidemiological applications, with a practical dearth of studies assessing smart and digital applications for psoriatic arthritis patients.ConclusionsBig Data can be a real paradigm shift that revolutionizes rheumatological and dermatological practice and clinical research, helping to early intercept psoriatic arthritis patients. However, there are some methodological issues that should be properly addressed (like recording and association biases) and some ethical issues that should be considered (such as privacy). Therefore, further research in the field is warranted.Systematic Review RegistrationRegistration code 10.17605/OSF.IO/4KCU2.

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Section: Resultsmentioning

confidence: 99%

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

et al. 2022

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“…Smartphone sensor-based measurements have demonstrated clinical validity for remote patient symptom measurement in Parkinson’s Disease 22 , multiple sclerosis 23 , rheumatoid arthritis, 24,25,26 , diabetes 27 and many others, 28 but no validated toolset exists for psoriasis and psoriatic arthritis. 29…”

Section: Introductionmentioning

confidence: 99%

Clinical validation of digital biomarkers and machine learning models for remote measurement of psoriasis and psoriatic arthritis

Webster

Haberman

Chada

et al. 2022

Preprint

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Background Psoriasis and psoriatic arthritis are common immune-mediated inflammatory conditions that primarily affect the skin, joints and entheses and can lead to significant disability and worsening quality of life. Although early recognition and treatment can prevent the development of permanent damage, psoriatic disease remains underdiagnosed and undertreated due in part to the disparity between disease prevalence and relative lack of access to clinical specialists in dermatology and rheumatology. Remote patient self-assessment aided by smartphone sensor technology may be able to address these gaps in care, however, these innovative disease measurements require robust clinical validation. Methods We developed smartphone-based assessments, collectively named the Psorcast suite, that can be self-administered to measure cutaneous and musculoskeletal signs and symptoms of psoriatic disease. The image and motion sensor data collected by these assessments was processed to generate digital biomarkers or machine learning models to detect psoriatic disease phenotypes. To evaluate these digital endpoints, a cross-sectional, in-clinic validation study was performed with 92 participants across two specialized academic sites consisting of healthy controls and participants diagnosed with psoriasis and/or psoriatic arthritis. Findings In the domain of skin disease, digital patient assessment of percent body surface area (BSA) affected with psoriasis demonstrated very strong concordance (CCC = 0.94, [95%CI = 0.91-0.96]) with physician-assessed BSA. Patient-captured psoriatic plaque photos were remotely assessed by physicians and compared to in-clinic Physician Global Assessment parameters for the same plaque with fair to moderate concordance (CCCerythema=0.72 [0.59-0.85]; CCCinduration=0.72 [0.62-0.82]; CCCscaling=0.60 [0.48-0.72]). Arm range of motion was measured by the Digital Jar Open assessment to classify physician-assessed upper extremity involvement with joint tenderness or enthesitis, demonstrating an AUROC = 0.68 (0.47-0.85). Patient-captured hand photos were processed with object detection and deep learning models to classify clinically-diagnosed nail psoriasis with an accuracy of 0.76, which is on par with remote physician rating of nail images (avg. accuracy = 0.63) with model performance maintaining accuracy when raters were too unsure or image quality was too poor for a remote assessment. Interpretation The Psorcast digital assessments, performed by patient self-measurement, achieve significant clinical validity when compared to in-person physical exams. These assessments should be considered appropriately validated for self-monitoring and exploratory research applications, particularly those that require frequent, remote disease measurements. However, further validation in larger cohorts will be necessary to demonstrate robustness and generalizability across populations for use in evidence-based medicine or clinical trial settings. The smartphone software and analysis pipelines from the Psorcast suite are open source and available to the scientific community. Funding This work is funded by the Psorcast Digital Biomarker Consortium consisting of Sage Bionetworks, Psoriasis and Psoriatic Arthritis Centers for Multicenter Advancement Network (PPACMAN), Novartis, UCB, Pfizer, and Janssen Pharmaceuticals. J.U.S work was supported by the Snyder Family Foundation and the Riley Family Foundation.

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“…Unfortunately, in daily routine of various health care systems around the world, the possibilities for the creation of a predictive models of PsA development are limited, and many obstacles have been reported ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, an extensive innovation in digitalization in rheumatology has occurred. The World Health Organization (WHO) defined “e-Health”, a collective term defined as “use of information and communications technology in support of health and health-related fields“ ( 14 ). This digital revolution included electronic health records, telemedicine and virtual visits, wearable technology, mobile health, artificial intelligence (AI), and machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…In the setting of patient at higher and imminent risk of developing PsA, AI-based tools (symptom checkers) are designed to collect patient symptoms, determine possible causes and direct the patient to the right specialist. In rheumatology, symptoms checkers could ensure a quick referral to the specialists and reduce diagnostic delay ( 14 ). Recently, a preliminary smartphone sensors-based measurement tool was tested in PsA ( 83 ).…”

Section: Introductionmentioning

confidence: 99%

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The Digital Way to Intercept Psoriatic Arthritis

et al. 2021

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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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Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

Cited by 8 publications

References 105 publications

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature

Clinical validation of digital biomarkers and machine learning models for remote measurement of psoriasis and psoriatic arthritis

The Digital Way to Intercept Psoriatic Arthritis

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